This document discusses challenges with current data analytics practices and how adopting a DataOps approach can help address them. It notes that current practices often involve many people using complex, fragmented toolchains which results in high error rates, slow deployment speeds, and an inability to deliver insights at the speed of business. DataOps is presented as a way to transform data analytics by applying practices from DevOps and Lean manufacturing like continuous integration, monitoring, version control systems, and reusable components. The document provides a seven step framework for implementing DataOps along with additional considerations for architecture, metrics, and collaboration.
DataOps: An Agile Method for Data-Driven OrganizationsEllen Friedman
DataOps expands DevOps philosophy to include data-heavy roles (data engineering & data science). DataOps uses better cross-functional collaboration for flexibility, fast time to value and an agile workflow for data-intensive applications including machine learning pipelines. (Strata Data San Jose March 2018)
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
“TODAY, COMPANIES ACROSS ALL INDUSTRIES ARE BECOMING SOFTWARE COMPANIES.”
The familiar refrain is certainly true of the new-school, born-in-the-cloud set. But it can also apply to traditional enterprises that are reinventing themselves by coupling DevOps excellence with intelligent DataOps.
DataOps: An Agile Method for Data-Driven OrganizationsEllen Friedman
DataOps expands DevOps philosophy to include data-heavy roles (data engineering & data science). DataOps uses better cross-functional collaboration for flexibility, fast time to value and an agile workflow for data-intensive applications including machine learning pipelines. (Strata Data San Jose March 2018)
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
“TODAY, COMPANIES ACROSS ALL INDUSTRIES ARE BECOMING SOFTWARE COMPANIES.”
The familiar refrain is certainly true of the new-school, born-in-the-cloud set. But it can also apply to traditional enterprises that are reinventing themselves by coupling DevOps excellence with intelligent DataOps.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
The Path to Data and Analytics ModernizationAnalytics8
Learn about the business demands driving modernization, the benefits of doing so, and how to get started.
Can your data and analytics solutions handle today’s challenges?
To stay competitive in today’s market, companies must be able to use their data to make better decisions. However, we are living in a world flooded by data, new technologies, and demands from the business for better and more advanced analytics. Most companies do not have the modern technologies and processes in place to keep up with these growing demands. They need to modernize how they collect, analyze, use, and share their data.
In this webinar, we discuss how you can build modern data and analytics solutions that are future ready, scalable, real-time, high speed, and agile and that can enable better use of data throughout your company.
We cover:
-The business demands and industry shifts that are impacting the need to modernize
-The benefits of data and analytics modernization
-How to approach data and analytics modernization- steps you need to take and how to get it right
-The pillars of modern data management
-Tips for migrating from legacy analytics tools to modern, next-gen platforms
-Lessons learned from companies that have gone through the modernization process
Data Marketplace and the Role of Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3IS9sQS
A data marketplace is like an online shopping interface specializing in data. Ideally, it should work just like an online store, with minimal latency and maximum responsiveness. However, this does not mean that all of the data in the data marketplace needs to be stored in the same central repository.
In this session, Shadab Hussain, Americas Sales Head, Data Analytics at Wipro, a partner company with Denodo and a co-sponsor of DataFest 2021, talks about the role of data virtualization in enabling full-featured data marketplaces. Such data marketplaces provide real-time, curated access to data, even when the data is stored across many different sources throughout the organization.
You will learn:
- The main features of a data marketplace
- Why organizations need data marketplaces
- Why data marketplaces sometimes fail
- How data virtualization enables the most effective data marketplaces
- How one of Europe’s premiere public healthcare system organizations leveraged a data marketplace to improve data consumption and ease of access
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From Data Stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an Operating Model of Roles and Responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
- Executive, Strategic, Tactical, Operational, and Support Level Roles
- How to customize an Operating Model to fit your organization
- Detailed Responsibilities for each level
- Defining who participates at each level
- Using working teams to implement tactical solutions
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
To be able to use analytics effectively and thus leverage the data treasures in the company, you need a modern and scalable data platform that can react flexibly to events and was designed with a DataOps mindset from the very beginning.
Data Lakehouse, Data Mesh, and Data Fabric (r1)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 data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
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 Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
The Path to Data and Analytics ModernizationAnalytics8
Learn about the business demands driving modernization, the benefits of doing so, and how to get started.
Can your data and analytics solutions handle today’s challenges?
To stay competitive in today’s market, companies must be able to use their data to make better decisions. However, we are living in a world flooded by data, new technologies, and demands from the business for better and more advanced analytics. Most companies do not have the modern technologies and processes in place to keep up with these growing demands. They need to modernize how they collect, analyze, use, and share their data.
In this webinar, we discuss how you can build modern data and analytics solutions that are future ready, scalable, real-time, high speed, and agile and that can enable better use of data throughout your company.
We cover:
-The business demands and industry shifts that are impacting the need to modernize
-The benefits of data and analytics modernization
-How to approach data and analytics modernization- steps you need to take and how to get it right
-The pillars of modern data management
-Tips for migrating from legacy analytics tools to modern, next-gen platforms
-Lessons learned from companies that have gone through the modernization process
Data Marketplace and the Role of Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3IS9sQS
A data marketplace is like an online shopping interface specializing in data. Ideally, it should work just like an online store, with minimal latency and maximum responsiveness. However, this does not mean that all of the data in the data marketplace needs to be stored in the same central repository.
In this session, Shadab Hussain, Americas Sales Head, Data Analytics at Wipro, a partner company with Denodo and a co-sponsor of DataFest 2021, talks about the role of data virtualization in enabling full-featured data marketplaces. Such data marketplaces provide real-time, curated access to data, even when the data is stored across many different sources throughout the organization.
You will learn:
- The main features of a data marketplace
- Why organizations need data marketplaces
- Why data marketplaces sometimes fail
- How data virtualization enables the most effective data marketplaces
- How one of Europe’s premiere public healthcare system organizations leveraged a data marketplace to improve data consumption and ease of access
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From Data Stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an Operating Model of Roles and Responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
- Executive, Strategic, Tactical, Operational, and Support Level Roles
- How to customize an Operating Model to fit your organization
- Detailed Responsibilities for each level
- Defining who participates at each level
- Using working teams to implement tactical solutions
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
To be able to use analytics effectively and thus leverage the data treasures in the company, you need a modern and scalable data platform that can react flexibly to events and was designed with a DataOps mindset from the very beginning.
Data Lakehouse, Data Mesh, and Data Fabric (r1)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 data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
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 Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
How the world of data analytics, science and insights is failing and how the principles from Agile, DevOps, and Lean are the way forward. #DataOps Given at DevOps Enterprise Summit 2019
Building a Data Platform Strata SF 2019mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
[This is a new, changed version of the presentations of the same title from last year's Strata]
Watch this webinar in full here: https://buff.ly/2MVTKqL
Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy.
Attend this session to learn why data virtualization:
• Is a must for implementing the right self-service BI
• Makes self-service BI useful for every business user
• Accelerates any self-service BI initiative
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
2015 02 12 talend hortonworks webinar challenges to hadoop adoptionHortonworks
Hadoop is no longer optional. Companies of all sizes are in various phases of their own Big Data journey. Whether you are just starting to explore the platform or have multiple clusters up and running, everyone is presented with a similar challenge - developing their internal skillset. Hadoop specialists are hard to find. Hand coding is too prone to error when it comes to storing, integrating or analyzing your data. However, it doesn’t need to be this difficult.
In this recorded webinar, Talend and Hortonworks help you learn how to unify all your data in Hadoop, with no specialized Big Data skills.
Find the recording here. www.talend.com/resources/webinars/challenges-to-hadoop-adoption-if-you-can-dream-it-you-can-build-it
This webinar covers: How Hadoop opens a new world of analytic applications, How to bridge the skills gap with our Big Data solutions, Experience a real-world, simple technical demo
Big Data Developer Career Path: Job & Interview PreparationIntellipaat
Youtube link : https://www.youtube.com/watch?v=iggl879a0s8
Intellipaat Big Data Hadoop Training: https://intellipaat.com/big-data-hadoop-training/
Read complete Big Data Hadoop tutorial here: https://intellipaat.com/blog/tutorial/hadoop-tutorial/
Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?SnapLogic
Companies collect more data but struggle with how to glean the best insights. Use of Machine Learning also needs power data integration.
In this presentation, Janet Jaiswal, SnapLogic's VP of product marketing, reviews key strategies and technologies to deliver intelligent data via self-service ML models.
To learn more, visit https://www.snaplogic.com
Better Total Value of Ownership (TVO) for Complex Analytic Workflows with the...ModusOptimum
Customers are looking for ways to streamline analytic decisioning, looking for quicker deployments, faster time to value, lower risks of failure and higher revenues/profits. The IBM & Hortonworks solution delivers on these customer needs.
https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&eventid=1789452&sessionid=1&eventid=1789452&sessionid=1&mode=preview&key=E0F94DE1191C59223B6522A075023215
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA
Curtis ODell, Global Director Data Integrity at Tricentis
Join me to learn about a new end-to-end data testing approach designed for modern data pipelines that fills dangerous gaps left by traditional data management tools—one designed to handle structured and unstructured data from any source. You'll hear how you can use unique automation technology to reach up to 90 percent test coverage rates and deliver trustworthy analytical and operational data at scale. Several real world use cases from major banks/finance, insurance, health analytics, and Snowflake examples will be presented.
Key Learning Objective
1. Data journeys are complex and you have to ensure integrity of the data end to end across this journey from source to end reporting for compliance
2. Data Management tools do not test data, they profile and monitor at best, and leave serious gaps in your data testing coverage
3. Automation with integration to DevOps and DataOps' CI/CD processes are key to solving this.
4. How this approach has impact in your vertical
Role of Data in Digital TransformationVMware Tanzu
Data plays a big role in building the kinds of experiences demanded by the market today. In this session, we’ll unpack what goes into building a data-driven app, case studies of how organizations have successfully overcome siloed data and analytics to bring new predictive features into their applications, and what your next steps for data should be on your digital transformation journey.
Speaker: Les Klein, EMEA CTO Data, Pivotal
The Data Lake: Empowering Your Data Science TeamSenturus
Data science overview: defined, purpose, relation to BI, differences from BI and benefits from using both data science and BI. View the webinar video recording and download this deck: http://www.senturus.com/resources/data-lake-empowering-data-science-team/.
Learn how the data lake can empower data science teams and free up valuable data warehouse resources.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Data summit connect fall 2020 - rise of data opsRyan Gross
Data governance teams attempt to apply manual control at various points for consistency and quality of the data. By thinking of our machine learning data pipelines as compilers that convert data into executable functions and leveraging data version control, data governance and engineering teams can engineer the data together, filing bugs against data versions, applying quality control checks to the data compilers, and other activities. This talk illustrates how innovations are poised to drive process and cultural changes to data governance, leading to order-of-magnitude improvements.
Intro of Key Features of Soft CAAT Ent Softwarerafeq
This presentation provides a brief overview of SoftCAAT Ent with use cases. SoftCAAT Ent is a data analytics/BI software used by CAs and CXOs for Assurance, Compliance and Fraud Investigations.
Similar to Washington DC DataOps Meetup -- Nov 2019 (20)
Open Data Science Conference Agile DataDataKitchen
To rephrase an old saying: ‘It takes a village to raise an Analyst.’ Data Analysts and Scientists are working in teams delivering insight and analysis on an ongoing basis. So how do you get the team to support experimentation and insight delivery without ending up in an IT Engineer vs Analyst vs Data Governance war? We present 5 shocking steps to get these teams of people working together with practical, doable steps that can help you achieve data agility.
Open Data Science Conference Big Data Infrastructure – Introduction to Hadoop...DataKitchen
The main objective of this workshop is to give the audience hands on experience with several Hadoop technologies and jump start their hadoop journey. In this workshop, you will load data and submit queries using Hadoop! Before jumping in to the technology, the Founders of DataKitchen review Hadoop and some of its technologies (MapReduce, Hive, Pig, Impala and Spark), look at performance, and present a rubric for choosing which technology to use when.
NOTE: To complete hands on poriton in the time allotted, attendees should come with a newly created AWS (Amazon Web Services) Account and complete the other prerequisites found in the DataKitchen blog <http: />.
Do Agile Data in Just 5 Shocking Steps!DataKitchen
For over 10 years, we have been doing agile for software development yet people struggle to do agile for data, BI, and analytics. After a quick review of the agile manifesto and principles, this talk looks at which agile practices have worked for data and which are still hard. Then, with analyst requirements in mind, this talk reveals the 5 shocking steps to actually do agile with data.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
52. Chris – DataOps
Engineer
Eric – Production
Engineer
Betty – Data
Engineer
This Is A Multi Step, Multi Person, Multi
Environment Process To Make this Request a
Reality
Challenges:
• How to leverage best practices and re-use?
• How to collaborate and coordinate work?
• How to ease movement between team
members with many tools and environments?
• How to maintain security?
• How to automate work and reduce manual
errors?
DataOps and Intra-Team Coordination
Pat – Data
Scientist
53. Chris – DataOps
Engineer
Production Environment:
• Separate
Hardware/Software
Environment
• Secure
• No Access By
Developers
• Managed by Eric
• Separate Credentials
PRODUCTION
DEVELOPMENT
Development
Environment:
• Separate
Hardware/Software
Environment
• Secure
• Access By Data
Engineers, Data
Scientists, Analysts and
DataOps Engineers
• Setup by Chris (DataOps
Engineer)
• Separate Credentials
Eric – Production
Engineer
Betty – Data
Engineer
Intra-Team Coordination
Pat – Data
Scientist