AnalytiX DS Unified Software Platform for enterprise data mapping, governance and code automation manage data, metadata, data mappings and integration processes through the system development life-cycle (SDLC) process, enabling governance, automation transparency and lineage of the data in a single unified platform for data integration professionals
The document discusses data observability at Intuit and how it helps prevent data incidents. It introduces Intuit's data ecosystem and challenges around data quality. It then describes Intuit's data observability model of curing, detecting, preventing and eradicating issues through techniques like data quality checks, anomaly detection, infrastructure monitoring and data triage at multiple layers. These techniques help reduce incorrect reporting, incidents, troubleshooting time and ensure SLAs are met by improving data reliability across Intuit's data platforms and products.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data lineage and observability with Marquez - subsurface 2020Julien Le Dem
This document discusses Marquez, an open source metadata management system. It provides an overview of Marquez and how it can be used to track metadata in data pipelines. Specifically:
- Marquez collects and stores metadata about data sources, datasets, jobs, and runs to provide data lineage and observability.
- It has a modular framework to support data governance, data lineage, and data discovery. Metadata can be collected via REST APIs or language SDKs.
- Marquez integrates with Apache Airflow to collect task-level metadata, dependencies between DAGs, and link tasks to code versions. This enables understanding of operational dependencies and troubleshooting.
- The Marquez community aims to build an open
Qlik and Confluent Success Stories with Kafka - How Generali and Skechers Kee...HostedbyConfluent
Converting production databases into live data streams for Apache Kafka can be labor intensive and costly. As Kafka architectures grow, complexity also rises as data teams begin to configure clusters for redundancy, partitions for performance, as well as for consumer groups for correlated analytics processing. In this breakout session, you’ll hear data streaming success stories from Generali and Skechers that leverage Qlik Data Integration and Confluent. You’ll discover how Qlik’s data integration platform lets organizations automatically produce real-time transaction streams into Kafka, Confluent Platform, or Confluent Cloud, deliver faster business insights from data, enable streaming analytics, as well as streaming ingestion for modern analytics. Learn how these customer use Qlik and Confluent to: - Turn databases into live data feeds - Simplify and automate the real-time data streaming process - Accelerate data delivery to enable real-time analytics Learn how Skechers and Generali breathe new life into data in the cloud, stay ahead of changing demands, while lowering over-reliance on resources, production time and costs.
Azure Purview provides a unified platform for data governance across hybrid and multi-cloud environments. It enables discovery of data assets, visualization of lineage and workflows, and management of a business glossary. Key features include automated scanning and classification of data, a centralized catalog for browsing and searching data, and insights into sensitive data and metadata usage. Purview integrates with services like Azure Synapse, Power BI, and Microsoft 365 to provide enhanced governance capabilities and propagate classifications and labels.
Core Archive for SAP Solutions is a fully-featured archiving and document viewing solution that allows customers to archive content from the main SAP database yet still view and interact with the content directly from the Archive. Core Archive supports the archiving of all content and data from SAP and can leverage SAP ILM disciplines. Content is stored in a compliant manner ensuring that GDPR, CCPA and other standards can be met. Core Archive is entirely cloud-based, reducing the IT footprint and offering rapid time to value.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
The document discusses data observability at Intuit and how it helps prevent data incidents. It introduces Intuit's data ecosystem and challenges around data quality. It then describes Intuit's data observability model of curing, detecting, preventing and eradicating issues through techniques like data quality checks, anomaly detection, infrastructure monitoring and data triage at multiple layers. These techniques help reduce incorrect reporting, incidents, troubleshooting time and ensure SLAs are met by improving data reliability across Intuit's data platforms and products.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data lineage and observability with Marquez - subsurface 2020Julien Le Dem
This document discusses Marquez, an open source metadata management system. It provides an overview of Marquez and how it can be used to track metadata in data pipelines. Specifically:
- Marquez collects and stores metadata about data sources, datasets, jobs, and runs to provide data lineage and observability.
- It has a modular framework to support data governance, data lineage, and data discovery. Metadata can be collected via REST APIs or language SDKs.
- Marquez integrates with Apache Airflow to collect task-level metadata, dependencies between DAGs, and link tasks to code versions. This enables understanding of operational dependencies and troubleshooting.
- The Marquez community aims to build an open
Qlik and Confluent Success Stories with Kafka - How Generali and Skechers Kee...HostedbyConfluent
Converting production databases into live data streams for Apache Kafka can be labor intensive and costly. As Kafka architectures grow, complexity also rises as data teams begin to configure clusters for redundancy, partitions for performance, as well as for consumer groups for correlated analytics processing. In this breakout session, you’ll hear data streaming success stories from Generali and Skechers that leverage Qlik Data Integration and Confluent. You’ll discover how Qlik’s data integration platform lets organizations automatically produce real-time transaction streams into Kafka, Confluent Platform, or Confluent Cloud, deliver faster business insights from data, enable streaming analytics, as well as streaming ingestion for modern analytics. Learn how these customer use Qlik and Confluent to: - Turn databases into live data feeds - Simplify and automate the real-time data streaming process - Accelerate data delivery to enable real-time analytics Learn how Skechers and Generali breathe new life into data in the cloud, stay ahead of changing demands, while lowering over-reliance on resources, production time and costs.
Azure Purview provides a unified platform for data governance across hybrid and multi-cloud environments. It enables discovery of data assets, visualization of lineage and workflows, and management of a business glossary. Key features include automated scanning and classification of data, a centralized catalog for browsing and searching data, and insights into sensitive data and metadata usage. Purview integrates with services like Azure Synapse, Power BI, and Microsoft 365 to provide enhanced governance capabilities and propagate classifications and labels.
Core Archive for SAP Solutions is a fully-featured archiving and document viewing solution that allows customers to archive content from the main SAP database yet still view and interact with the content directly from the Archive. Core Archive supports the archiving of all content and data from SAP and can leverage SAP ILM disciplines. Content is stored in a compliant manner ensuring that GDPR, CCPA and other standards can be met. Core Archive is entirely cloud-based, reducing the IT footprint and offering rapid time to value.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
This session describes the roles and skill sets required when building a Data Science team, and starting a data science initiative, including how to develop Data Science capabilities, select suitable organizational models for Data Science teams, and understand the role of executive engagement for enhancing analytical maturity at an organization.
Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
Objective 3: Understand the importance of Executive support for Data Science initiatives and role it plays in their successful deployment.
Data Mesh in Azure using Cloud Scale Analytics (WAF)Nathan Bijnens
This document discusses moving from a centralized data architecture to a distributed data mesh architecture. It describes how a data mesh shifts data management responsibilities to individual business domains, with each domain acting as both a provider and consumer of data products. Key aspects of the data mesh approach discussed include domain-driven design, domain zones to organize domains, treating data as products, and using this approach to enable analytics at enterprise scale on platforms like Azure.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Doug Bateman, a principal data engineering instructor at Databricks, presented on how to build a Lakehouse architecture. He began by introducing himself and his background. He then discussed the goals of describing key Lakehouse features, explaining how Delta Lake enables it, and developing a sample Lakehouse using Databricks. The key aspects of a Lakehouse are that it supports diverse data types and workloads while enabling using BI tools directly on source data. Delta Lake provides reliability, consistency, and performance through its ACID transactions, automatic file consolidation, and integration with Spark. Bateman concluded with a demo of creating a Lakehouse.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
Forget Big Data. It's All About Smart DataAlan McSweeney
This proposes an initial smart data framework and structure to allow the nuggets of value contained in the deluge of largely irrelevant and useless data to be isolated and extracted. It enables your organisation to ask the questions to understand where it should be in terms of its data state and profile and what it should do to achieve the desired skills level across the competency areas of the framework.
Every organisation operates within a data landscape with multiple sources of data relating to its activities that is acquired, transported, stored, processed, retained, analysed and managed. Interactions across the data landscape generate primary data. When you extend the range of possible interactions business processes outside the organisation you generate a lot more data.
Smart data means being:
• Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including organisation structures, roles, devolution and delegation of decision-making, processes and automation
• Smart in being realistic, pragmatic and even skeptical about what can be achieved and knowing what value can be derived and how to maximise value obtained
• Smart in defining an achievable, benefits-lead strategy integrated with the needs business and in its implementation
• Smart in selecting the channels and interactions to include – smart data use cases
Smart data competency areas comprise a complete set of required skills and abilities to design, implement and operate an appropriate smart data programme.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Scaling and Modernizing Data Platform with DatabricksDatabricks
This document summarizes Atlassian's adoption of Databricks to manage their growing data pipelines and platforms. It discusses the challenges they faced with their previous architecture around development time, collaboration, and costs. With Databricks, Atlassian was able to build scalable data pipelines using notebooks and connectors, orchestrate workflows with Airflow, and provide self-service analytics and machine learning to teams while reducing infrastructure costs and data engineering dependencies. The key benefits included reduced development time by 30%, decreased infrastructure costs by 60%, and increased adoption of Databricks and self-service across teams.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Automated Data Governance 101 - A Guide to Proactively Addressing Your Privac...DATAVERSITY
“Data privacy,” “data security,” “data protection” –
whatever we call the way we control our data, it isn’t working. Data is as
vulnerable as ever. And this is true for both consumers hoping to keep their
data safe, and for enterprises seeking to govern their corporate and customer
data.
We’re at a crossroads: Governing data and putting data to
use are two dueling objectives, and businesses are stuck in the middle.
Can this problem be solved? In a word: yes.
The answer is through what we call automated Data Governance, which introduces speed, agility, and precision into the process of applying rules on data. Join Immuta for a webinar as we explore these Data Governance challenges and discuss how you can proactively address them with automated Data Governance.
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
Oracle Planning and Budgeting Cloud Service (PBCS)US-Analytics
70% of executives say they are moving their finance systems into the cloud within the next year. Ready to bring world-class planning and forecasting to your organization?
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
Good old u serv product derby in the brave new world of decision managementJacob Feldman
This document provides a summary of a presentation on Decision Model and Notation (DMN) and lessons learned from submissions to the UServ Product Derby decision modeling challenge. Key points include:
- Submissions used different decision table formats and approaches to decision structure, showing a lack of interoperability between tools. DMN aims to standardize these.
- Decision requirements diagrams were rarely used, with all logic encoded in business knowledge models. DMN separates requirements from logic.
- Having a standardized business glossary and interchange format would improve tool interoperability. The presenters suggest further refinements to DMN to improve usability.
The document discusses operational data warehousing and the Data Vault model. It begins with an agenda for the presentation and introduction of the speaker. It then provides a short review of the Data Vault model. The remainder of the document discusses operational data warehousing, how the Data Vault model is well-suited for this purpose, and the benefits it provides including flexibility, scalability, and productivity. It also discusses how tools and technologies are advancing to support automation and self-service business intelligence using an operational data warehouse architecture based on the Data Vault model.
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
This session describes the roles and skill sets required when building a Data Science team, and starting a data science initiative, including how to develop Data Science capabilities, select suitable organizational models for Data Science teams, and understand the role of executive engagement for enhancing analytical maturity at an organization.
Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
Objective 3: Understand the importance of Executive support for Data Science initiatives and role it plays in their successful deployment.
Data Mesh in Azure using Cloud Scale Analytics (WAF)Nathan Bijnens
This document discusses moving from a centralized data architecture to a distributed data mesh architecture. It describes how a data mesh shifts data management responsibilities to individual business domains, with each domain acting as both a provider and consumer of data products. Key aspects of the data mesh approach discussed include domain-driven design, domain zones to organize domains, treating data as products, and using this approach to enable analytics at enterprise scale on platforms like Azure.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Doug Bateman, a principal data engineering instructor at Databricks, presented on how to build a Lakehouse architecture. He began by introducing himself and his background. He then discussed the goals of describing key Lakehouse features, explaining how Delta Lake enables it, and developing a sample Lakehouse using Databricks. The key aspects of a Lakehouse are that it supports diverse data types and workloads while enabling using BI tools directly on source data. Delta Lake provides reliability, consistency, and performance through its ACID transactions, automatic file consolidation, and integration with Spark. Bateman concluded with a demo of creating a Lakehouse.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
Forget Big Data. It's All About Smart DataAlan McSweeney
This proposes an initial smart data framework and structure to allow the nuggets of value contained in the deluge of largely irrelevant and useless data to be isolated and extracted. It enables your organisation to ask the questions to understand where it should be in terms of its data state and profile and what it should do to achieve the desired skills level across the competency areas of the framework.
Every organisation operates within a data landscape with multiple sources of data relating to its activities that is acquired, transported, stored, processed, retained, analysed and managed. Interactions across the data landscape generate primary data. When you extend the range of possible interactions business processes outside the organisation you generate a lot more data.
Smart data means being:
• Smart in what data to collect, validate and transform
• Smart in how data is stored, managed, operated and used
• Smart in taking actions based on results of data analysis including organisation structures, roles, devolution and delegation of decision-making, processes and automation
• Smart in being realistic, pragmatic and even skeptical about what can be achieved and knowing what value can be derived and how to maximise value obtained
• Smart in defining an achievable, benefits-lead strategy integrated with the needs business and in its implementation
• Smart in selecting the channels and interactions to include – smart data use cases
Smart data competency areas comprise a complete set of required skills and abilities to design, implement and operate an appropriate smart data programme.
Emerging Trends in Data Architecture – What’s the Next Big ThingDATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
An introduction to self-service data with Dremio. Dremio reimagines analytics for modern data. Created by veterans of open source and big data technologies, Dremio is a fundamentally new approach that dramatically simplifies and accelerates time to insight. Dremio empowers business users to curate precisely the data they need, from any data source, then accelerate analytical processing for BI tools, machine learning, data science, and SQL clients. Dremio starts to deliver value in minutes, and learns from your data and queries, making your data engineers, analysts, and data scientists more productive.
Scaling and Modernizing Data Platform with DatabricksDatabricks
This document summarizes Atlassian's adoption of Databricks to manage their growing data pipelines and platforms. It discusses the challenges they faced with their previous architecture around development time, collaboration, and costs. With Databricks, Atlassian was able to build scalable data pipelines using notebooks and connectors, orchestrate workflows with Airflow, and provide self-service analytics and machine learning to teams while reducing infrastructure costs and data engineering dependencies. The key benefits included reduced development time by 30%, decreased infrastructure costs by 60%, and increased adoption of Databricks and self-service across teams.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Automated Data Governance 101 - A Guide to Proactively Addressing Your Privac...DATAVERSITY
“Data privacy,” “data security,” “data protection” –
whatever we call the way we control our data, it isn’t working. Data is as
vulnerable as ever. And this is true for both consumers hoping to keep their
data safe, and for enterprises seeking to govern their corporate and customer
data.
We’re at a crossroads: Governing data and putting data to
use are two dueling objectives, and businesses are stuck in the middle.
Can this problem be solved? In a word: yes.
The answer is through what we call automated Data Governance, which introduces speed, agility, and precision into the process of applying rules on data. Join Immuta for a webinar as we explore these Data Governance challenges and discuss how you can proactively address them with automated Data Governance.
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
Oracle Planning and Budgeting Cloud Service (PBCS)US-Analytics
70% of executives say they are moving their finance systems into the cloud within the next year. Ready to bring world-class planning and forecasting to your organization?
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
Good old u serv product derby in the brave new world of decision managementJacob Feldman
This document provides a summary of a presentation on Decision Model and Notation (DMN) and lessons learned from submissions to the UServ Product Derby decision modeling challenge. Key points include:
- Submissions used different decision table formats and approaches to decision structure, showing a lack of interoperability between tools. DMN aims to standardize these.
- Decision requirements diagrams were rarely used, with all logic encoded in business knowledge models. DMN separates requirements from logic.
- Having a standardized business glossary and interchange format would improve tool interoperability. The presenters suggest further refinements to DMN to improve usability.
The document discusses operational data warehousing and the Data Vault model. It begins with an agenda for the presentation and introduction of the speaker. It then provides a short review of the Data Vault model. The remainder of the document discusses operational data warehousing, how the Data Vault model is well-suited for this purpose, and the benefits it provides including flexibility, scalability, and productivity. It also discusses how tools and technologies are advancing to support automation and self-service business intelligence using an operational data warehouse architecture based on the Data Vault model.
A strong relationship with the founder
of Data Vault for over 3 years now.
Supporting your business with 40+
certified consultants.
Incorporated as the preferred
Enterprise Data Warehouse modelling
paradigm in the Logica BI Framework.
Satisfied customers in many countries
and industry sectors
DAMA, Oregon Chapter, 2012 presentation - an introduction to Data Vault modeling. I will be covering parts of the methodology, comparison and contrast of issues in general for the EDW space. Followed by a brief technical introduction of the Data Vault modeling method.
After the presentation i I will be providing a demonstration of the ETL loading layers, LIVE!
You can find more on-line training at: http://LearnDataVault.com/training
Given at Oracle Open World 2011: Not to be confused with Oracle Database Vault (a commercial db security product), Data Vault Modeling is a specific data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It has been in use globally for over 10 years now but is not widely known. The purpose of this presentation is to provide an overview of the features of a Data Vault modeled EDW that distinguish it from the more traditional third normal form (3NF) or dimensional (i.e., star schema) modeling approaches used in most shops today. Topics will include dealing with evolving data requirements in an EDW (i.e., model agility), partitioning of data elements based on rate of change (and how that affects load speed and storage requirements), and where it fits in a typical Oracle EDW architecture. See more content like this by following my blog http://kentgraziano.com or follow me on twitter @kentgraziano.
Agile Data Warehouse Design for Big Data PresentationVishal Kumar
Synopsis:
[Video link: http://www.youtube.com/watch?v=ZNrTxSU5IQ0 ]
Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design - a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.
The method utilizes BEAM✲ (Business Event Analysis and Modeling) - an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature "best practice" dimensional DW design techniques, and collects "just enough" non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.
BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin "white board" modeling interactively with BI stakeholders. With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.
The BEAM✲ method is fully described in
Agile Data Warehouse Design - a text co-written by Lawrence Corr and Jim Stagnitto.
About the speaker:
Jim Stagnitto Director of a2c Data Services Practice
Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.
Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.
Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of "Agile Data Warehouse Design", guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta's latest book: “The DW ETL Toolkit”.
John DiPietro Chief Technology Officer at A2C IT Consulting
John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.
Sponsor Note:
Thanks to:
Microsoft NERD for providing awesome venue for the event.
http://A2C.com IT Consulting for providing the food/drinks.
http://Cognizeus.com for providing book to give away as raffle.
AnalytiX Data Services is a data integration company founded in 2006 that provides the AnalytiX Mapping Manager solution. The Mapping Manager is a metadata and data mapping repository that automates the data mapping process and generates ETL jobs. It has over 700 customers, many of which are Fortune 1000 companies. The solution aims to accelerate project delivery by making the data mapping process faster, more manageable, and collaborative.
Unified Enterprise Data Mapping, Governance & Automation PlatformAnalytiX DS
The AnalytiX DS Unified Platform is an all-in-one platform to integrate Enterprise Metadata Management,data governance, and code automation into a single, comprehensive product suite to automate manual processes and truly accelerate the delivery of integration projects.
The document describes AnalytiX Mapping Manager, a software tool that manages metadata and automates the data mapping process. It has three core modules - Resource Manager, System Manager, and Mapping Manager. Key features include creating and versioning data mappings using a drag-and-drop interface, generating ETL jobs, managing projects and resources, and providing data lineage analysis and impact reports. Optional add-on modules provide additional integration and reporting capabilities.
The Mapping Manager is the market leader in enterprise software which automates and manages the "source to target" mappings through the life-cycle process. Mapping Manager is robust, scalable and customizable platform for creating and governing enterprise data mappings and a code-generator for auto-generating ETL jobs for leading ETL tools. Mapping Manager accelerates delivery of integration projects while enabling standards, control, auditability, manageability and governance of the data mapping process.
Pysyvästi laadukasta masterdataa SmartMDM:n avullaBilot
1.9.2016 aamiaistilaisuuden esitys.
Mitäpä jos valjastaisit koko organisaatio masterdatan ylläpitoon? Hallitsisit hajauttamalla? Uudistunut SmartMDM tuo käyttöösi hallinnan, Microsoft SQL Server Master Data Services (MDS) keskityksen.
Lisää tapahtumiamme sivustollamme: http://www.bilot.fi/en/events/
Aniket Sarkar has over 6 years of experience as a senior developer at Tata Consultancy Services, specializing in Microsoft Business Intelligence tools and data warehousing. He has worked on several projects for clients such as Alcoa and Humana, developing ETL processes, data models, reports, and dashboards. Some of his accomplishments include developing a real-time analytical cube for financial reporting and extracting data from Oracle and Hyperion applications into SQL Server. He holds technical certifications in SQL Server and .NET Framework and has received recognition from both customers and his employer.
Manager in the filed of BPMA, providing services in below areas:
- Data Warehousing
- Business Intelligence
- SDLC (Waterfall & Agile)
- Business Analysis
- Project Management
- MIS & Reporting
- CRM development
- Artificial Intelligence
- Production Support
- Data Quality & Governance framework
- System Integration
Skill Set:
Sql, SAS, Qlik sense, SAP BO
IBM InfoSphere Information Server 8.1 is a unified platform for understanding, cleansing, transforming and delivering trustworthy information. It combines the technologies of components like the Information Server Console, Metadata Workbench, Business Glossary, DataStage & QualityStage, Information Analyzer and Information Services Director. The platform provides shared services for administration and reporting. Metadata services allow accessing and integrating data. Key components include the Metadata Server, Metadata Workbench and Business Glossary for managing metadata. DataStage & QualityStage is used for designing jobs to transform and cleanse data, while Information Analyzer helps understand data quality.
Analytix Data Services provides data integration software and services. Its flagship product, AnalytiX Mapping Manager, is an enterprise solution for data mapping and metadata management. It helps customers accelerate project delivery through automating source-to-target mappings, enabling collaboration, and increasing productivity. The solution comprises modules for resource management, system management, and mapping specifications. It has over 50 customers worldwide.
Matthew Tartaglia is an Information Technology Senior Manager with over 20 years of experience leading enterprise application development implementations, overseeing support groups, and managing technology platforms. He has expertise in areas such as organizational leadership, client orientation, technology solutions, budget planning, quality management, and strategic planning. His technical expertise includes languages, databases, data warehousing tools, operating systems, and quality assurance tools. He has held senior consulting and architecture roles at Ally Financial, Jefferies, and Merrill Lynch where he led technology assessments, implemented applications, and provided strategic guidance.
Bi Architecture And Conceptual FrameworkSlava Kokaev
This document discusses business intelligence architecture and concepts. It covers topics like analysis services, SQL Server, data mining, integration services, and enterprise BI strategy and vision. It provides overviews of Microsoft's BI platform, conceptual frameworks, dimensional modeling, ETL processes, and data visualization systems. The goal is to improve organizational processes by providing critical business information to employees.
Alok Singh is seeking challenging assignments in Business Intelligence/Data warehousing. He has nearly 7 years of experience in BI/DW, ETL, data integration, and data warehousing solution design. He is proficient in SQL, ETL tools like Informatica and SSIS, and visualization tools like QlikView and Tableau. He has experience designing and developing ETL solutions, requirements gathering, and data analysis. His past roles include positions at Technologia, Subex, and Reliance Communications where he worked on projects involving Teradata, Oracle, billing systems, and fraud detection. He has a bachelor's degree in electronics and telecommunications.
I built this presentation for Informatica World in 2006. It is all about Data Administration, Data Quality and Data Management. It is NOT about the Informatica product. This presentation was a hit, with standing room only full of about 150 people. The content is still useful and applicable today. If you want to use my material, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
The document discusses Carestream Health's transformation to a global product development model through implementing a holistic PLM (Product Lifecycle Management) solution. Key steps included aligning IT and R&D, selecting technology partners through a rigorous evaluation, and establishing a standard global architecture and processes. This improved collaboration, sped development cycles, and better supported compliance and quality across a distributed workforce.
This candidate has over 4 years of experience as an Informatica Developer, Master Data Management specialist, and Information Development Director developer. She has worked on various projects involving ETL development using Informatica PowerCenter, master data management using Informatica MDM, and application development using Informatica IDD. She has strong skills in Oracle PL/SQL, Informatica PowerCenter, MDM, and IDD and has experience designing and developing mappings, packages, and applications to meet client needs and requirements.
This candidate has over 4 years of experience as an Informatica Developer, Master Data Management specialist, and Information Development Director developer. She has worked on various projects involving ETL development using Informatica PowerCenter, master data management using Informatica MDM, and application development using Informatica IDD. She is proficient in Oracle PL/SQL and has experience analyzing requirements, designing solutions, developing mappings and workflows, testing, and supporting clients. She is a motivated professional who works well independently and in a team.
The document discusses business intelligence and analytics programs and careers. It provides information on topics like data mining, dashboards, enterprise resource planning systems, online analytical processing, and multidimensional data models. It also lists relevant course descriptions and curriculum from technical schools and colleges to prepare for careers in fields like business intelligence specialist, business intelligence developer, and business intelligence report developer.
This document provides a summary of Mohammed Kaleem's professional experience and qualifications. He has over 25 years of experience in business intelligence, with expertise in MicroStrategy, Informatica, and data warehousing. Some of his roles include senior consultant, solution architect, and lead developer. He has extensive experience designing, developing, and implementing BI solutions for many large companies.
- The document contains the resume of Abdul Mohammed, an ETL developer with 8 years of experience using Informatica for data warehousing projects.
- He has expertise in requirements gathering, data extraction from various sources, transforming the data using Informatica tools, and loading the data into target databases.
- His most recent role was as an ETL/SR Informatica Lead from 2015-present where he worked on building a data warehouse for a pharmaceutical company using Informatica to extract data from Oracle and flat files.
How to Automate your Enterprise Application / ERP TestingRTTS
This document discusses automating enterprise application and data warehouse testing using QuerySurge. It begins with an introduction to QuerySurge and its modules for automating data interface testing. These modules allow testing across different data sources with no coding required. The document then covers data maturity models and how QuerySurge can help improve testing processes. It demonstrates how QuerySurge can automate testing to gain full coverage while decreasing testing time. In conclusion, it discusses how QuerySurge provides value through increased testing efficiency and data quality.
The Data Quality Assessment Manager is a Data Quality product specifically designed to manage data quality assessments, manage data quality scores, review and correct quality issues and manage the workflow across all stakeholders involved in a data quality assessment. DQAM is the industry’s first platform designed to put data quality in the hands of data stewards and business owners who know and understand the data the best.
The FIRST Automation Framework Built for Data Integration.CATfX uses Code Automation Templates (CATs) to abstract away the low-level complexity of common or routine integration tasks.
Governance and Architecture in Data IntegrationAnalytiX DS
This document discusses starting a data governance program in an agile way using AnalytiXTM Mapping ManagerTM. It describes AnalytiXTM Mapping ManagerTM as an enterprise mapping tool that can manage all metadata related to data integration projects, including documenting mappings, business rules, and providing traceability and auditability of data. Implementing AnalytiXTM Mapping ManagerTM can help satisfy regulatory compliance needs like those in the Sarbanes-Oxley Act by providing a centralized metadata repository and standardizing processes. Starting a data governance program with AnalytiXTM Mapping ManagerTM can help address metadata management gaps and jumpstart governance in a flexible manner.
Code Data Mapping Application Migration | CDMA MigrationAnalytiX DS
The document discusses AnalytiX Mapping Manager and Code Set Manager software. It describes how the software allows healthcare organizations to centrally manage code sets and reference data from various sources to reduce risks and eliminate data quality issues. It also details how the software provides functionality to build standard mapping specifications that can generate integration processes and be used across integration projects. Additionally, it outlines how the software allows for automated migration of code sets from HP's CDMA application while preserving associated metadata.
Basel III Compliance: This whitepaper gives insights about how to overcome risk of data aggregation governance and reporting capabilities in Banking and Financial Institutions.
AnalytiXTM Release Manager is a software tool that provides a web-based interface for defining, planning, tracking, approving, and validating software releases. It aims to streamline the release management process, provide visibility into releases, and facilitate standardization. Key features include integrating with mapping tools, creating and managing planned releases, consolidating related documents, and tracking approvals. It can be deployed on-premises or as a cloud-based software as a service. Support and implementation services are also available from AnalytiX Data Services.
The AnalytiX DS – LiteSpeed Conversion® (ALC) solution runs as Software as a Service (SaaS) and provides an automated framework that automates the conversion of ETL tool platforms. Enable FAST and AUTOMATED conversion between one ETL Platform and another.
AnalytiX DS specializes in the development of ‘agile tools’ for the data integration industry which automate manual data mapping and ETL conversion processes.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
What is Master Data Management by PiLog Groupaymanquadri279
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Oracle Database 19c New Features for DBAs and Developers.pptx
AnalytiX DS - Master Deck
1. AnalytiX Data Services
Enterprise Data Mapping Governance Automation
For any questions regarding the contents of this presentation please contact us at info@AnalytiXds.com and we will assist you as soon as possible. The contents of this presentation are
protected under NDA. All rights reserved by AnalytiX DS 2016
Unified Platform Introduction and Overview
October 2016
2. Standard Agenda
• Introductions
• Company & Unified Platform Introduction (15)Mins
1. AnalytiX DS Unified platform – governance & lifecycle management of the data mapping process
• Capabilities Demonstration of Mapping Manager and Unified Platform (30mins)
1. Governance of metadata data dictionaries & business glossaries
2. Governance and versioning of source to target mappings through the lifecycle change process
3. Governance of Data Lineage, Impact Analysis and Business Rules Repository.
4. Code Generation - Generate Big Data & ETL Jobs and other types of CODE automatically from the tool.
5. Test Automation and Data Quality – Integration with HP ALM (Quality Center)
• Deeper Dive into LifeCycle Management Add-On Modules
• Code Set Manager, Reference Data Manager, Test Manager, Release Manager, Data Quality Assessment Manager and Metadata Connectors
• Deep Dive Into Automation Platform CATfX Automation Modules
• CATfX Automation Platform & Code-Generator ETL Platforms & Other Technologies
• Automation JumpStart Program
• ETL Platform Conversion (LiteSpeed Conversion & Autonomy)
Modified Agenda
3. Mapping Manager Big Data Edition
AnalytiX Mapping Manager:
Create Mapping designs using scanned metadata and group
Categorize and Auto-Generate your ETL jobs
Maintain Version Control, Auditability, Impact Analysis &
Lineage
The integration industry’s
premier enterprise data mapping &
automation platform
4. Nearly 50% of Fortune 1000
Global Customer Base
Global Government Install Base
Company Overview
More than 1000 Customers
Snap Shot
Founded
2006
Offices
HQ: Chantilly, VA USA
USA: Development Center: Dallas, TX
Global Delivery Center: Hyderabad, India
Leadership
50+ years experience in Integration
Market Leader
Enterprise Data Mapping & Automation
ETL Conversion Technology
Employees
More than 200
Relations with Top 5 System Integrators and Off-shore Integrators
20+ Resellers Globally (HP Global Reseller)
Partners/ Resellers
20 Most Promising Productivity Tools
Bloor Research,
2012
“A Must Have Tool” Honorable Mention
Informatica, 2010, 2012 Winner
Informatica, 2012
Innovation Award
Most Valuable Partner
CIO REVIEW –2015
5. Michael Boggs
CTO and Founder
• Invented the integration industry’s first enterprise
data mapping tool, The Mapping Manager and the
universal Code Automation Platform – CATfX
• 20+ years experience in Data Integration, Data
Warehousing and BI Technologies
• Responsible for all Aspects of Technology
Innovation across AnalytiXDS software products
Sam Benedict
VP of Strategic Accounts
• 20+ years experience as a Data Integration
Manager
• Responsible for all sales and marketing for
strategic customer accounts.
John Carter
Director of Professional Services
• 15+ years experience in Data Integration and
Automation Services
• Responsible for all aspects of Service Delivery
and Conversion Technologies
Madan Kalakuntla
Managing Partner, Global Delivery Center
• 20+ years experience in services delivery and
off-shore operations
• Responsible for global delivery center
operations and services.
AnalytiX DS Product Leadership Team
Over 100 years of combined experience
6. Corporate Vision
Manage data, metadata, data mappings and integration processes through the
system development life-cycle (SDLC) process, enabling governance, automation
transparency and lineage of the data in a single unified platform for data
integration professionals
Process Driven Solutions for Integration Professionals
Innovate, Automate, Accelerate
7. AnalytiX DS Unified Software Platform
Enterprise Data Mapping, Governance and Automation.
AnalytiX Mapping Manager
Metadata Management
Enterprise Data Mapping
Add-on Modules
SDLC Governance
Open Automation Framework
Mapping Manager
(flagship product)
Modules
1. CATfX Automation
2. CATfX Global Marketplace
3. Packaged Automation Bundles
4. LiteSpeed Conversion
Add-on Modules
Automated ETL platform conversion
Big Data
ETL, ELT
Data Vault
1. Business Glossary
2. Requirements Manager
3. Reference Data Manager
4. Test Manager
5. Data Quality Assessment
Manager (DQAM)
6. Release Manager
7. Report Manager
8. Plug-Ins for metadata tools
9. Where does AnalytiX DS fit in the ecosphere of software
tools?
AUTOMATION TOOLS
GOVERNANCE TOOLS
10. ETL
ALM
IMM
IDQ
• Compliment & Integrates w/ MDM & DQ Tools
• Identify Valid & Invalid Data
• Design Cleansing Rules & Gen Code to Remediate
Mapping Management & Governance of the SDLC process
Integration with leading tool platforms
Center of the Universe
Governance of the mapping process
• ETL Tool & Big Data Code Generation
• Compliments Testing Tools
• Generate Test SQL
• Sync Test Cases and Results
• Metadata Plug-in for Metadata Mgmt Tools
11. By Project
By Project, By Team Role
Use Cases for the unified platform
1. Data Warehouse
2. Big Data
3. Application Consolidation and Migration
1. App Retirement
2. Mergers and Acquisitions
3. DBMS Conversion
4. Data Migration
5. ETL Platform conversion
6. Application Modernization & Tools Replatform
7. Cloud migration
4. Regulatory Compliance & Mapping Standards
5. Data Governance
6. Data Quality Assessment
7. Testing & Quality Assurance
8. Metadata Management
9. Master Data Management
10. Code Parsers to quickly document code
By Resource Role
1. Data Modelers (mapping data elements)
2. Data Analyst, Mapping Analyst, Business Analyst
3. Data Stewards & Data Governance
4. Data Quality Analyst
5. ETL and Big Data Developers
6. QA Testers
7. Release Manager & Project Managers
8. Information Management Executives
12. (SDLC) Requirements
Data Mapping
Rules
Design Build Test Assess Data Quality Deploy Maintenance
Business Analyst Mapping Analyst /
Business Analyst
Architect / Lead
Developer
Developers Testers Data Stewards /
Quality Analyst
Release Managers /
Project Manager
Mapping Analyst
Developers
Testers
Data Stewards
Release Managers
optional
Reference Data Manager
optional
test automation via CATfX
Information Governance – Component Reference Model
Data Dictionaries /
Business Glossary
(mm)
Reference Data &
Code-set Management
(rdm)
Governance of Mapping
Specifications
(mm)
Data Lineage & Impact
Analysis
(mm)
Data Steward &
Workflow Management
(mm)
Governance of Releases
(rm)
Governance of Testing &
Data Quality
(tm) / (dqam)
Regulatory Audit
(mm)
IT Security Audit
(mm)
Data Quality Audit
(dqam)
Compliance
Mapping Manager
(mm)
CATfX Automation
(catfx)
CATfX Automation
(catfx)
Test Manager
(tm)
Data Quality
(dqam)
Release Manager
(rm)
Mapping Manager
CATfX Automation
Test Manager
Release Manager
DQAM
Governance across the Systems Development Life-Cycle (SDLC) Process
Reqts Manager
(rqm)
Business
Application
Information Governance
Component Reference Model
13. (SDLC) Requirements
Data Mapping
Rules
Design Build Test Assess Data Quality Deploy Maintenance
Business Analyst Mapping Analyst /
Business Analyst
Architect / Lead
Developer
Developers Testers Data Stewards /
Quality Analyst
Release Managers /
Project Manager
Mapping Analyst
Developers
Testers
Data Stewards
Release Managers
optional
Reference Data Manager
optional
test automation via CATfX
Identify data source
requirements
Identify target
definitions
Scan and manage
metadata definitions
and dictionaries
Manage or integrate
with business
glossaries
Drag, drop build,
version data
mapping rules and
transformations
Govern re-usable
transformation rules
Manage reference
data and code-set
Mappings
View data lineage
and impact Analysis
Data steward and
workflow
management
Use, modify or create
CAT templates
Existing Big Data
CATs
Types of CATs
ETL/ELT Jobs
Generate DDL
Parse files
Read/Write to ESB
Custom API connectors
Metadata plug-ins
Cobol Programs
Java Programs
Data Vault Bundles
More…
Data Vault Template
STAGE/HUB/LINK/SAT/
ESB
Data Mart Template
SCD1,2,3
FACT Load
Click CATs & generate
code from metadata
Generate DDL
Generate and unit
test generated
ETL/ELT Jobs
Generate and Test
Big Data processes
Any code based on
CAT template…
Associate re-usable
test cases to
mappings and target
tables
Generate test SQL
and comparative
source and target
SQL statements (19
test automation
CATs)
Create new test
cases and link to
mappings
Execute test cases
and capture test
results
Create new test
automation (CATfX)
Sync with industry
standard test tools
such as HP ALM and
IBM Doors
Manage data quality
for high value and
governed data
values.
Automatically scan
and detect sensitive
data.
Assign data stewards
& track DQ goals
Create data quality
schedules to profile
data and conduct
“value level”
assessments
Manage and
maintain data quality
scores
Identify valid values
for master data
management
Identify values which
require remediation
Create, govern and
audit the release
management
process
Manage data
mappings and
release objects for
every release
Customize release
objects forms to your
internal standards
Audit release
approvals and
validation checkouts
Yearly, monthly,
weekly and daily
release calendar
views
Mapping
Maintenance. As
new columns, tables,
data source are
added the mappings
can be versioned,.
maintained,
compared to
previous versions
Code Changes.
Regenerate Code for
New Mappings
Regression Unit
Test. Regenerate
Test Cases/Test
SQL
Release
Management.
Manage releases
and release Objects
Continuous Data
Quality Monitoring of
scores
Information Governance – Component Reference Model
Data Dictionaries /
Business Glossary
(mm)
Reference Data &
Code-set Management
(rdm)
Governance of Mapping
Specifications
(mm)
Data Lineage & Impact
Analysis
(mm)
Data Steward &
Workflow Management
(mm)
Governance of Releases
(rm)
Governance of Testing &
Data Quality
(tm) / (dqam)
Regulatory Audit
(mm)
IT Security Audit
(mm)
Data Quality Audit
(dqam)
Compliance
Mapping Manager
(mm)
CATfX Automation
(catfx)
CATfX Automation
(catfx)
Test Manager
(tm)
Data Quality
(dqam)
Release Manager
(rm)
Mapping Manager
CATfX Automation
Test Manager
Release Manager
DQAM
Governance across the Systems Development Life-Cycle (SDLC) Process
Mapping Manager
(mm)
Business
Application
Capability
14. Accelerate Timelines
Unified platform offers accelerated timelines and governance of the across the SDLC
Manualwith Mapping Manager 50% Faster
Manual
Manualwith Test Manager 50% Faster
Manualwith Mapping Manager + CATs 60% Faster
with Mapping Manager + CATfX 60% Faster
Requirements - Requirements and Data
Mapping
Design - Creating & Enforcing ETL/ELT and
Big Data Job Design Standards
Build - DDL, ETL/ELT and Big Data
Integration Jobs
Test – Construct Test Cases, Comparative
Test SQL, Execute & Manage test runs
Data Quality - Assess and Manage Data
Quality
Data Quality – manage enterprise reference
data, codesets and validation rules
Deploy – Manage releases and release
objects
Database Platform Migration
ETL Platform Migration
Data Vault Platform Migrations
Data Lake Automation
Timeline
Manualwith Release Manager 30% Faster
Manualwith unified platform 50% Faster
Manual
Manualwith DQAM 50% Faster
Manualwith Reference Data Manager 50% Faster
Manual
Data warehouse modernization – platform migrations
SDLC
with unified platform 50% Faster
with unified platform 50% Faster
16. • Deliver faster & better with higher quality deliverables
• Reduce project risks and big data management costs
• Deliver instant business value and improve Governance Across the SDLC process
• Standardize and accelerate ETL processes with reusable templates
• Standardize and accelerate Testing processes with reusable templates
• Global visibility into enterprise projects
• Improve performance by adopting and generating Hadoop Scripts
• Generate Code and deliver faster with built-in design standards
• Focus on standards and process improvements
• Meet regulatory, compliance and process audit
• Build repeatable processes and solutions
• Assess & Govern Data Quality Scores
• Transform Big Data into useful data
• Gain Better Control and Management of data glossaries and the data mapping
process
Benefits
18. Mapping Manager has provided us
with a platform to use our intellectual
property more effectively and provide
greater visibility to the business across
the Organization and the response of
the support has been the best we have
seen in the industry.
With AMM and CATfX Automation, we
were able to reduce overall project
duration by close to 25% and cost by close
to 35%.Our deliverables have also
improved considerably due to fact project
resources now have a common interface
to a centralized metadata repository which
provides us with complete process, flow
and version control capability. AnalytiX DS
have been extremely responsive to our
enhancement requests and have one of
the best turnaround times in the industry.
This was best demonstrated in the manner
in which AnalytiX DS built a custom plugin
that integrated with one of our critical
MDM solutions.
We have successfully completed and
delivered various project initiatives
using the AnalytiX Mapping Manager.
The Data Lineage and Impact Analysis
have been our biggest challenges over
the years and Mapping Manager
helped us close this gap instantly. We
now have wider and accurate visibility
into the upstream and downstream
dependencies thus helping us make
quicker business decisions.
What are our referenceable customers are saying about AnalytiX DS?
19. Mapping Manager has been the most
widely accepted tool and the fastest
tool that we have rolled out to our
internal IT and business users.
We use the product to Map hundreds
of data sources and generate
informatica jobs to accelerate our
delivery process.
The AnalytiX toolset is an accelerator
for data integration projects involving
metadata and data mapping. The tool
makes a valuable contribution in
managing enterprise metadata and
data mappings and facilitates the
efficient turn-over of project
deliverables to our clients. The Lineage
Analyzer and Impact Analysis features
have been key features that have
helped us realize instant ROI on the
product.
We have found this to be the only
product in the market that caters
widely to the Mortgage and Financial
sectors and MISMO in particular. The
tool provides an array of features that
create and govern the data flow and
help maintain the mappings and
lineage between different MISMO
releases. The mappings standards help
newer model versions to be easily
integrated into the tool and maintain
the new changes with greater visibility
across the organization.
What are our referenceable customers are saying about AnalytiX DS?
(Contd.)
20. Mapping Manger has helped us govern
our mapping for our Enterprise Data
Warehouse and alert us to impacts
downstream before they occur
We use the product to Map hundreds
of data sources map and version data
sources to our Enterprise Data
Warehouse.
We use the AnalytiX DS Unified platform
in our Enterprise Data Warehouse to
govern Source to Target Mappings,
CodeSets, Reference Data and
integration test management. We are
able to view complete end to end
linage of all data elements across the
EDW. The AnalytiX DS team has been
incredible responsive and receptive to
our needs. We have watched our
feedback evolve into product
enhancements over the past 3 years.
We now have a product that governs
every phase of the SDLC.
We have used AnalytiX DS ETL platform
conversion to convert legacy ETL jobs
to new a new modern ETL platform. The
platform as services offered support for
all major ETL tools and a delivery
guarantee with a fixed price
conversion model making AnalytiX DS
a one-stop-shop for our ETL platform
conversion needs.
What are our referenceable customers are saying about AnalytiX DS?
(Contd.) Featured Government Customers
22. Metadata
Manager
Mapping
Manager
Reference
Data
Manager
Data
Quality
Manager
Customer Infrastructure Layer
(DBMS, Files, System, Code, ETL Tools, ESB, Hadoop etc)
AnalytiX DS – Unified Platform for Governance
CATs are configured using a 100% metadata Driven Approach. Configured CAT, reads
metadata from repository, infrastructure, file data, applies code-gen logic and generates code
based on the logic configured in the CAT.
Integrated Development Environment (IDE)
Scripting language under support
Java | Java Script | Jython | Groovy | XSLT
Universal Code GeneratorCode Automation Template Framework (CATfX)
File Export
From CAT generates
code in the SDK
format of target
System
Execution and Run-Time Management
execution and runtime is managed within the target system as it is
normally done today. Business as usual for system administrators, job
monitoring, backups, code versioning systems etc.
Any Code
Any Platform
Governance and Automation Platform
Code Automation Template Framework (CATfX) – How it works
The universal code-generator for data integration professionals
Business
Glossary
Manager
Big Data:
24. Mapping Management & Automation using CATfX
Universal Code-Generation using CATs
Import generated code into the respective technology platforms
Integration Bundles
Automation Bundles Big Data – Data Lake ETL, ELT Integration Data Vault
Hub
Link
Satellite
Gen Stage DDL
Source 2 Stage
Stage 2 Data Mart
SPARK
Scoop
PIG
27. Design Accelerators (4)
----
DDL & Mapping Generators
DDL generation and Mapping generation CATs are
available to automate the design phase
1. Generate Stage DDL
2. Automap Source to
Data Vault
------------------------------
3. *RapidVaultModel
4. *RapidSQLViews
Build Accelerators (6)
----
ETL/ELT Generators
ETL Code Generation is avail for all ETL platforms and
standard SQL to automate the Data Vault
1. Source 2 Stage
2. Hub Generator
3. Link Generator
4. Satellite Generator
5. Point in Time (PIT)
Generator
6. Bridge Generator
Test Accelerators (15+)
----
Test SQL Generators
Over 15 test automation CATs available in Test Manager to
automate and accelerate testing phase
1. Metadata Structure Testing
2. Data Completeness Testing
Manage Quality Scores
----
Data Quality Assessment
Manager (DQAM)
Manage Requirements
----
Requirements Manager
(RQM)
Manage Codesets
----
CodeSet Manager
(CSM)
Manage Reference
Data
----
Reference Data Manager
(RDM)
Manage Test Results
----
Test Manager
(TM)
Manage Release
----
Release Manager
(RM)
Mapping & Metadata Management (MM) Enterprise Business Glossary (EBG)
Data Vault Automation Bundle (10 CATs)
UNIFIED
PLATFORM
DATA VAULT
AUTOMATION
Governance and Compliance
100% Metadata Driven Approach to Automation
METADATA
LAYER
Data Vault Automation Platform
A complete platform to universally generate any code and accelerate each phase of the SDLC process
28. LANDING STAGE
Raw
Data Vault
Business
Data Vault
Hub / Link / Satellites Pit / Bridge
Data Sources
Process Groups: Source 2 Landing | Landing 2 Stage | Stage to Raw DV | Raw DV 2 Business DV
DM1
DM2
DM3
Bus DV 2 DM2
Data Vault Load Process Architecture
Code Generation: 5 Step Process for loading Raw Vault, Business Vault and Data Marts
DBMS
any
Data Vault Load Process Architecture
1 2 3 4
5
33. System Metadata
• Stored in ERwin files.
Mappings from Source
Data Set files back to the
legacy source systems
are stored in User
Defined Properties of
ERwin files.
Mapping & Transform Logic
• Mapping logic between Source
Data Set and Legacy Schema
are stored in .xls mapping files
Export and Generate
PRODUCTION READY ETL Jobs
can Sources & Targets Create or Upload Mappings Generate ETL Jobs From Ma
Code Generation
Extract Map & Transform Generate Code
MAP & AUTOMATE the Data Migration PROCESS
Create Mappings & use the universal code-generator to Generate Code and accelerate the data integration process for complex data migrations
More…
34. Proof of Concept Process
www.analytixds.com34
CAT Configuration Recommendations
Unit Test ETL Jobs and Run Test Automation
IN
SCOPE
Auto-Generate ETL
SME sends
examples of mappingsAnalytiX DS Automation
Assessment Engine
POC Team
AnalytiX DS +1
Customer SME
OUT OF
SCOPE
Customizing CATs
“Requires Paid POC”
UAT
Getting Started is FREE - Evaluation License and a supported Proof-of-Concept!
View, Edit and Manage Mappings
35. Data Vault
Recommended Proof of Concept Process
www.analytixds.com35
Modify DV Automation CATs
1. Dan Linstedt Certification of DV CATs
2. Unit Test ETL Jobs and Run Test Automation
YES
Auto-Generate Code
POC Kickoff Planning
Agree on Scope
(recommended 25 Source Tables)
Architecture Review
(Assess modification to CATs)
POC Team
Customer Sponsor +1
AnalytiX DS (4)
Data Vault Sme + 1
Mapping Analyst +1
ETL Developer +1
CAT Developer +1
NO
UAT
Getting Started requires a dedicated support team to ensure success of all deliverables and answering of all questions
Generate and Create DV Mappings
Setup Metadata Scans, Generate DV Model
Implement Data Vault
(Generate Code Deliverables and schedule loads)
Data Vault DDL
Data Vault Mappings
ETL Processes for: Source to Stage, Hub, Links, Satellites
Generate Test Cases in Test Manager
37. Enterprise Business Glossary
Collaborate and manage business terms, acronyms definitions and enrich
with rich media definitions such as images, videos, sound-files and more
Map semantic business definitions to physical data dictionaries and
view relationships and lineage
Manage Data Stewardship Roles, Responsibilities, Assignments, goals
Associate Business Terms to Technical Metadata, requirements, mappings,
test cases, KPI’s and more via integrated relationship manager
Publish Business Terms to the business community
Integrated Project Management & Workflow capabilities
Generate end to end semantic and data lineage views
Promotes data quality and governance participation
and collaboration in a web based business friendly
platform
Enables greater control and visibility for all major
stakeholders and the business though an integrated
workflow process
Promotes collaboration and establishes relationships
between semantic business terms and physical data
dictionaries
Manage your Data Stewardship Program Roles,
Responsibilities, goals and ownership
Assign Roles and Responsibilities to data stewards and
business users
KEY FEATURES BENEFITS
Integrated collaboration center enables enterprise collaboration
and promotes data governance programs
Helps promote a robust Data Governance and
Stewardship program through organizational
collaboration between data stewards and the business.
Business friendly web user interface
Integrated Reporting and Import/Export Features
Enables business users IT to create, manage and collaborate on common business vocabulary across the organization Supports regulatory compliance,
data governance, and data stewardship initiatives by governing business vocabulary terms and enabling lineage maps showing how semantic definitions
are related to physical data dictionaries, data mappings, lineage of federated data in a business friendly governance platform.
38. Requirements Manager
Standardize the requirements documentation process using
customizable “requirements template” to capturing requirements
Capture various Artifacts/Specifications beyond Requirements
Link Requirements to Mappings. Test Cases and Test Results
Integrated Workflow & Approval Management Process
Dynamic association of KPIs around Requirements
Create and Publish an reusable enterprise Requirement
Specification template for the business Users to fill the requirements
Build Repeatable Requirements Documentation
processes based on your best practices
Helps capture and standardize rapidly changing
requirements
Promotes greater visibility and collaboration surrounding
the requirements management process and
downstream SDLC processes
Enterprise Level Traceability between Requirements,
Mappings, Test Cases and Test Results
Integrates with the AnalytiX DS Unified Platform bringing
together an efficient SDLC process
KEY FEATURES BENEFITS
Integrates with third party SDLC tools
Import and Export feature for MS Word and MS Excel Files
Integrated reporting and Requirement Traceability Matrix
An agile and collaborative platform to standardize the functional requirements documentation process using a customizable template driven approach. Easily, create and
customize requirements templates to standardize the capture, trace, and requirements definition process for integration projects. Rich features allow for management of
functional requirements and how they “link” to data mappings, test cases, test results to promote greater transparency and visibility of requirements process and downstream
impacts.
39. Mapping Manager
Metadata driven approach to Source to Target Mapping
Management and Code Generation
Data Lineage and Impact Analysis capability
Version Management & Change Control
Collaboration and Workflow Management
Accelerate the Pre-ETL mapping process by using the drag/drop
feature to create mapping specifications. Using the metadata
driven Drag/Drop approach eliminates scope for any human errors
Code Generation for Big Data & leading ETL/ELT Tools
Create reusable mapping templates to standardize the mapping process
Easily import Legacy Mappings
Gain better control and management of enterprise
data glossaries and the data mapping process
Seamless integration with your Organization’s
methodology toolsets
Web based User interface. no hassles of deploying any
software on the end user’s machine
Reduce project risks and lower cost
Enterprise wide visibility into Integration Projects
Robust automation framework to build repeatable
processes and standards
Deliver faster and better with higher quality deliverables
Meet regulatory and compliance standards
KEY FEATURES BENEFITS
Web user interface and roles based security to govern metadata, data
dictionaries and govern Mappings, Codesets, Code Crosswalks and
Reference Tables
Code Generation for Data Vault Methodology
Support for Agile and Iterative development
Eliminate duplicate efforts from silo project teams and
loss of knowledge capital due to attrition
Integration Industry’s leading “Pre-ETL” enterprise data mapping solution. Enables organizations to create, version and manage source to target
mappings (STM) through the change process and view end to end data lineage across the data mapping process and information management
architecture.
40. Reference Data Manager
Centralized Repository to create, version and maintain Codesets
and Reference Data in a business friendly interface
Manually add new codes and/or Import Codes and Reference
Data directly out of databases or files
Tightly govern the addition of new codes using the workflow
management process
Integrated Versioning and Publishing to handle the change
management and parallel deployment processes
Change Comparison Reports to identify changes between different
versions of Codesets/Reference Tables
Auto Mapping Capabilities to accelerate the development of
Code Crosswalks
Validation Rules Engine - Define and Execute SQL Queries on Reference
Tables to identify duplicate rows, integrity violations, nulls etc.
Deploy Reference Tables and Codesets to various environments
using the publishing capabilities
Standardize and collaborate Codeset & Reference
Data Management across the Enterprise
Streamline Conversion between legacy and enterprise
Codesets
Gain better control and management over Codesets
and Code Crosswalks
Gain better governance, control and manageability of
enterprise reference data
Meet regulatory, compliance and process audit
Build repeatable processes and solutions
Compliance to healthcare standards like HIPAA, SOX
etc.
Improve Data Quality confidence
KEY FEATURES BENEFITS
Restricted User Access to tightly manager and govern Codesets,
Code Crosswalks and Reference Tables
Enables business and IT users to scan, manage, validate and version govern code-sets and reference data across the organization. Perform validation,
link to mappings and integrate with ETL Processes to ensure consistency across governed reference data.
41. Release Manager
Centralized repository to manage and govern releases and all
release artifacts
Integrated Release Plans and enterprise Calendar views
Integrated Workflow Management & Approval process
Detailed change and audit tracking
Easy to use UI to collaborate and standardize the release processes
Automatic promotion of mappings and tight alignment with
releases when used in conjunction with Mapping Manager
Quickly view and generate release plans and other release reports
Integration with Third party configuration management tools
through CATfX
Empowers release managers to define and promote
best practices and standards
Streamline, collaborate and every aspect of the release
management process
Reduce risk and meet corporate compliance standards
Standardize and govern all documentation surrounding
the release management process
Global visibility into the release process and release
objects
Release management audit-trail tracking and support
Increased productivity and improved organizational
autonomy via shared and collaborative release
management environment
Streamline communication and collaboration
KEY FEATURES BENEFITS
Enables Program and Project Managers to create, validate and manage releases, release calendars and release artefacts such as ETL Jobs, Data
Mappings, Database Objects, Server objects, release notes and more to standardize all release processes and documentations standards around the
release process.
42. Test Manager
Comprehensive Test Case Management Capabilities
Integrated Test Case Repository to manage reusable test cases, test
SQL and governance of the the entire testing process
Customizable and Scalable Automation Testing Framework
Test SQL Generation and documentation of Testing Results
Integration with leading Testing Tools like HP-ALM
Testing Automation for Data Vault Methodology
Centralized Repository to manage Test Cases and other
test artifacts
Improves testing efficiency by enhancing collaboration
between testers and developers
Promotes the reusability and accessibility of test cases
Increases visibility so all stakeholders can monitor test
processes and gauge application readiness
Testing Automation and Customization with the CATfX
Framework
Supports connectivity to any Database and big data
systems
Manage data migration projects and reconcile old and
new system for data correctness
Streamline communication and collaboration
KEY FEATURES BENEFITS
Auto Generation of reusable Test Cases based on source and
target metadata
Testing Report Summaries
Test Manager enables Test Cases and Test SQL generation to be managed in a purpose built module for testing data mappings and ETL processes. There are over 15+ built-in
automation CATS which enable immediate creation ofTest Cases andTEST SQL to accelerate the testing phases of integration projects.
43. Data Quality Assessment Manager (DQAM)
Remediate invalid values and create data repair scripts
Easily Manage Data Quality Scores (A,B,C,D, and F) and enable
governance and stewardship programs
Collaborative review and integrated workflow process
Define monitoring and remediation rules to correct and improve
data
Publish Data Quality Assessment & Six Sigma Category Scores
Compliments existing DQ and MDM tools available on the market
Schedule assessments, monitoring rules and trend DQ scores
Enterprise data quality management. Take advantage
of the data quality console for an enterprise view of your
data quality scores and programs
Assign and track data quality tasks to completion using
the integrated project management capability
Enables ability to measure effectiveness of data quality
and stewardship programs by trending quality scores
Business-friendly interfaces and workflow. The workflow
separates the technical and analysis functions
supporting business-friendly interfaces for end-users
Meet compliance and Data Quality standards
KEY FEATURES BENEFITS
Engage Business Users and standard and execute a formal Data
Quality Assessment Methodology
Govern valid and Invalid values and categories data quality issues
Enables organizations to standardize and execute a formal data quality assessment methodology by selecting the objects which should be assessed,
scheduling the assessments, manage the data quality scores and workflow review process. Governance features manage data steward and
stakeholder accountability for the quality reviews and sign-off. Govern valid values, invalid values, remediation rules and generate scripts to “fix/repair”
invalid data values.
44. Reporting Manager
Flexible Reporting
Build Charts and Dashboards
Customized Reporting and publishing Capabilities
Connectors to leading databases and third party apps
Schedulers to automate report generation to key stakeholders
Helps improve decision making and promotes cross-
team collaboration across the SDLC process
Bring in greater control and visibility for all major
stakeholders through the integrated reporting portal
Business friendly user interface, enables team
dashboards and reports to be published
Ability to report on internal and external repositories
KEY FEATURES BENEFITS
Helps build a robust Reporting Portal specifically around
the data management and SDLC process to promote
more efficient status and workflow management
reporting.
Role based security to govern access and publishing controls
Enables IT teams to quickly build and configure reports and dashboards from AnalytiX DS repository to manage their teams workflow status and
cross team communication by creating and publishing reports and dashboards to streamline communications across the data management
process. The easy to use interface enables reporting 10 times faster without all the training hassles required by larger BI reporting platforms.
45. Metadata Management Plug-ins
Sync AnalytiX DS metadata repositories to third party metadata
management tool platforms
Compliments external metadata repositories by leverage a rich set
of pre-built “connectors” others tools to do not support (e.g. Cobol
Programs, Reverse Engineer ETL jobs, code Parsers, Data Mappings
and more)
Ability to customize existing connectors via CATfX
Schedulers to automate synch processes
Maximizes the investment of existing metadata
management platforms
Extends capabilities of existing metadata management
platforms using AnalytiX DS plug-ins for unsupported
data sources
Enables End to End Lineage and greater support of
data sources
Ability to sync business oriented data mappings and
view data mappings alongside of ETL jobs
KEY FEATURES BENEFITS
Ability to consolidate business reference data and
codeset standards within the enterprise metadata
repository.
Role based security to govern access and publishing controls
Metadata plug-in are available for Informatica Metadata Manager, IBM Metadata Workbench/GDC, DAG metacentre and Rochade. This enables all the metadata in Mapping
Manager Unified Platform to be ported into third party metadata environments. Metadata connectors for all ETL Tools, Java Scripts, Cobol copybooks, Cobol Programs, Sales
Force.com and more.
Global community of automation developers and connectors
available via AnalytiX DS automation marketplace
46. Code AutomationTemplate Framework (CATfX)
Multitude of scripting language support for the developer
community (JavaScript, JRuby, Groovy, J-Python, R & XSLT)
End to End Automation Framework
Developer Friendly Integrated Development Environment
Automation for ETL, SQL, Big Data and much more…
ETL/ELT Standardization and Automation
Testing and Custom Code Generation
Code Gen for Data Vault Methodology
Workflow Management for Sequential Execution of Automated
Tasks
Enterprise Code management
Drastically increases developer productivity with
reusable code automation templates(CATs)
Fits seamlessly into Agile Development methodologies
ensuring rapid development to quickly changing
requirements
Reduce repetitive and time consuming manual tasks
Increase quality, standards & time to value
Drastically reduce costs
Generate re-usable templates based on Job design
best practices
Wider adaptability due to amount of supported scripting
languages
KEY FEATURES BENEFITS
Create and version Re-usable code automation templates (CAT)
The integration Industry’s first Automation Platform for Data Integration Professionals. CATfX Defines Work Smarter. Save Big. Create, version and publish
re-usable code automation templates (CATS) to generate code for Big Data, ETL/ELT Tools, Code Parsers, SQL generation or Custom Adaptors and
Connectors to anything by Scanning metadata and Generate code for anything using a 100% metadata driven approach to automation. The
automation marketplace is a global marketplace for automation developers to post automation CATs for the global community to browse buy, sell and
download to automate manual process.
47. Mapping Manager Jumpstart
Installation, training, and services to
get stared quickly.
ETL Center of Excellence
AnalytiX DS can assist enabling your best
practices and generate code-automation
templates to drive standards and perform
production monitoring of ETL Processes.
LiteSpeed Conversion Services
Quickly convert between one legacy
ETL Platform to another using software
driven automation & our conversion
methodology. LiteSpeed Conversion &
Autonomy.
Team Enablement Services
AnalytiX DS certified resources can
augment your team and accelerate
Mapping, ETL, Testing and Big Data.
Automation Jumpstart (CATfX)
Jumpstart program is intended to
analyze manual integration processes
and build ‘reusable’ CATs to
automate, standardize and accelerate
the process.
Available Services
• Services Categories
Mission Statement:
We are driven by our relentless focus on the “pursuit of Excellence”, we will constantly strive to implement the critical initiatives required to achieve our vision. In doing this, we will deliver operational & innovation excellence in every
corner of the Company and meet or exceed our commitments to our customers, our employees and our partners. All of our long-term strategies and short-term actions will be molded by a set of core values that are shared by each and
every associate aimed at ensuring customer success and innovating the Data Integration industry and remaining a leader in enterprise data mapping, governance and automation. We strive for Excellence in everything we do!
QA
Data Quality & Testing Automation
Jumpstart program around Data Quality and
Testing Are available with Test Manager & Data
Quality Manager Modules.
48. Service Program Offerings
• Available Services Programs
Ask about our team enablement services and discount schedules for
Automation support services
Governance & Data Quality support services
Data Mapping support services
Big Data support Services
ETL/ ELT Center of Excellence
Production Support & Monitoring support services
49. Mapping Manager Module
(Screen Orientation)
Enterprise Data Mapping, Governance &
Automation
Build
Mappings
using
Drag n Drop
ect & Mapping
Browser
S2T Mapping
Specifications
Auto-Generate
ETL Jobs
Metadata Browser
(Manage Data Dictionaries)
Business Glossaries
RDBMS Metadata
Data Models
File Data
Internet of Things (IoT)
JSON
Hadoop HDFS
HIVE
Ecosphere DBMS
More
iously Versioned
Mappings
Mapping Details
(Customizable)
54. Before you think about writing code for
weeks and months on end – think about
how the code can be automatically
generated. Then contact the experts at
AnalytiX DS to for review with an
automation expert.
We have an extensive library of automation
templates in our library which will work for your or
can be customized to fit your exact automation
requirement.
The larger the coding task – the better! We can
create a reusable automation template that can
save hundreds of man hours and hundreds of
thousands of dollars by addressing the manual
coding effort with automation techniques made
possible with our CATfX Automation Framework.
Idea Concept Library Metadata Any Volume
AUTOMATION MADE EASY.
We create re-usable automation solutions to your most complex and expensive processes
56. Contact Us
14175 Sullyfield Circle
Suite 500
Chantilly,VA 20151
Corporate HQ
info@analytixds.com
support@analytixds.com
(800) 656-9860
Http://www.analytixds.com
Phone & Email
www.analytixds.com/community
www.youtube.com/user/AnalytiXDS
Facebook.com/analytixds
LinkedIn.com/analytixds
Community & Social Media
SALES
For Sales - Please call (800)-603-4790 (9am-5pm EST) or Email: sales@analytixds.com
SUPPORT
For Product Support - Please call (800)-617-9620 or Email: support@analytixds.com