PowerDesigner is a modeling tool that can be used to model various aspects of an enterprise data management system. It allows users to create conceptual data models, logical data models, physical data models, data movement models, and more. The presentation discussed how PowerDesigner can be used to model the migration of an OLTP database to a data warehouse, including reverse engineering the source database, generating models for the data warehouse, defining transformations, and generating scripts to move data. It concluded that PowerDesigner and a model-driven approach can accelerate development and increase productivity for data warehouse projects.
This document provides an overview of best practices in metadata management. It discusses what metadata is, why it is important, and how it adds context and definition to data. Metadata management is part of an overall data strategy. The document outlines different types of metadata and how it is used by various roles like developers, business people, auditors, and data architects. It discusses challenges like inconsistent metadata that can lead to issues. It also provides examples of metadata sources, architectural options, and how metadata enables capabilities like data lineage, impact analysis, and semantic relationships.
SAP Business Objects Architecture
SAP Business Objects Universe Design
SAP Business Objects 12 Reporting Tools
SAP Business Objects Web intelligence Reporting Strength and limitations
SAP Business Objects Crystal Reports Architecture, Strength and limitations
SAP Business Objects Issues, performance Tuning.
SAP Business Objects Xcelcius Architecture, strength and weakness.
SAP Business Objects Universe Design Issues, performance tunning
SAP Business Objects SAP source Vs Non SAP source
SAP Business Objects Security
SAP Business Objects Application security lifecycle
SAP Business Objects Data Integrator, MDX, Cube
How to Implement Data Governance Best PracticeDATAVERSITY
This document provides an overview of a webinar on implementing data governance best practices. It discusses defining data governance best practices and assessing an organization's current practices against those best practices. Examples of best practices from different industries are provided. The document emphasizes communicating best practices in a non-threatening way and building best practices into daily operations. Key aspects covered include criteria for determining best practices, messages to convey to management, and best practices related to creating a best practices document.
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
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.
Chapter 4: Data Architecture ManagementAhmed Alorage
This document provides an overview of data architecture management. It defines data architecture as an integrated set of specifications that define data requirements, guide integration, and align data investments with business strategy. The key concepts discussed include enterprise architecture, architectural frameworks like Zachman, and the roles and activities of data architects. Data architecture management is presented as the process of defining a blueprint for managing data assets through specifications like enterprise data models and information value chain analysis.
This document provides an overview of best practices in metadata management. It discusses what metadata is, why it is important, and how it adds context and definition to data. Metadata management is part of an overall data strategy. The document outlines different types of metadata and how it is used by various roles like developers, business people, auditors, and data architects. It discusses challenges like inconsistent metadata that can lead to issues. It also provides examples of metadata sources, architectural options, and how metadata enables capabilities like data lineage, impact analysis, and semantic relationships.
SAP Business Objects Architecture
SAP Business Objects Universe Design
SAP Business Objects 12 Reporting Tools
SAP Business Objects Web intelligence Reporting Strength and limitations
SAP Business Objects Crystal Reports Architecture, Strength and limitations
SAP Business Objects Issues, performance Tuning.
SAP Business Objects Xcelcius Architecture, strength and weakness.
SAP Business Objects Universe Design Issues, performance tunning
SAP Business Objects SAP source Vs Non SAP source
SAP Business Objects Security
SAP Business Objects Application security lifecycle
SAP Business Objects Data Integrator, MDX, Cube
How to Implement Data Governance Best PracticeDATAVERSITY
This document provides an overview of a webinar on implementing data governance best practices. It discusses defining data governance best practices and assessing an organization's current practices against those best practices. Examples of best practices from different industries are provided. The document emphasizes communicating best practices in a non-threatening way and building best practices into daily operations. Key aspects covered include criteria for determining best practices, messages to convey to management, and best practices related to creating a best practices document.
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Designed to address more mature programs, this tutorial covers the issues and approaches to sustaining Data Governance and value creation over time, amongst a changing business and personnel environment.
Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program.
Learn about:
• Typical obstacles to sustainable Data Governance
• Re-energizing your program after a key player (or two) leave and other personnel challenges
• Staying relevant to the company as the business evolves over time
• Understanding the role of metrics and why they are critical
• Leveraging Communication and Stakeholder Management practices to maintain commitment
• Embedding Data Governance into the operations of the company
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.
Chapter 4: Data Architecture ManagementAhmed Alorage
This document provides an overview of data architecture management. It defines data architecture as an integrated set of specifications that define data requirements, guide integration, and align data investments with business strategy. The key concepts discussed include enterprise architecture, architectural frameworks like Zachman, and the roles and activities of data architects. Data architecture management is presented as the process of defining a blueprint for managing data assets through specifications like enterprise data models and information value chain analysis.
The Data Driven Enterprise - Roadmap to Big Data & Analytics SuccessBigInsights
The document discusses how data-driven companies are performing better financially and outlines the benefits of big data and analytics. It provides examples of companies using big data and analytics to improve customer experience through personalization, predict maintenance needs, and identify at-risk veterans to prevent suicide. The challenges of big data are also reviewed. Finally, it proposes a seven-step methodology for leveraging big data and analytics to address critical business challenges.
The document outlines a presentation about data analytics and business intelligence given by Chris Ortega. The presentation covers:
1. Definitions of data analytics and business intelligence.
2. Why data analytics and business intelligence are important for faster decision making, establishing a learning culture, and exploring opportunities.
3. The decision cycle and how business intelligence tools can automate parts of extracting data, analyzing it, and making decisions.
4. Limitations of current data analytics approaches like manual spreadsheet updates and the difficulty combining multiple data sources.
The document discusses managing content at LexisNexis, which involves:
1. Classifying millions of documents into a taxonomy to group similar content and enable better governance. This includes identifying content types and assigning owners.
2. Developing consistent enterprise data models and standards to represent content structures and relationships across systems. This includes defining quality principles and evaluating current practices.
3. Defining roles for content architects who create taxonomy, policies, and data models to consistently represent content for downstream uses like customer products.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Real-World Data Governance: What is a Data Steward and What Do They Do?DATAVERSITY
This document is a transcript from a webinar on the topic of "What is a Data Steward?". It discusses different definitions and approaches to defining the role of a Data Steward. Key points include:
- A Data Steward is someone who is responsible for data used in their job, including defining, producing, and ensuring quality of data.
- The role of a Data Steward depends on the organization's data governance approach. It should leverage existing responsibilities rather than assigning new roles.
- Different types of Data Stewards are discussed, including Operational Stewards, Domain Stewards, and Steward Coordinators.
- The responsibilities of Data Stewards include data definition, production
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
RWDG Slides: Three Approaches to Data StewardshipDATAVERSITY
There are different ways to connect people with data stewardship responsibilities. You can assign people to be data stewards, identify people as data stewards or recognize people as data stewards. These approaches vary in several ways.
Join Bob Seiner for this month’s installment of the RWDG webinar series where he will compare and contrast three distinct approaches to data stewardship. The approach you select and follow will heavily influence how data governance results will be achieved.
In this webinar Bob will discuss:
- Three approaches to data stewardship
- The influence of each approach on program results
- Factors to assist in the selection of the approach to follow
- Obstacles to being successful with each approach
- Benefits of following each approach
The document discusses best practices for capturing data requirements for projects that are data-rich. It emphasizes the importance of taking a top-down requirements approach and maintaining traceability between requirements. While use cases are useful, they often do not fully capture data needs. The document advocates looking beyond immediate needs to plan for business intelligence by capturing additional relevant data elements upfront.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
1. It is important to define data quality metrics that are purpose-fit and meaningful to customers. Dashboards should focus more on driving outcomes than just design.
2. Commonly used data quality dimensions include completeness, conformity, consistency, duplication, integrity, and accuracy. Specific metrics are then defined within each dimension tied to business objectives and rules.
3. Targets and trends provide valuable insights, with traffic light targets highlighting priority areas in red and trends showing progress over time.
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
RWDG Webinar: Data Steward Definition and Other Data Governance RolesDATAVERSITY
1. The document discusses defining data steward roles and responsibilities in a data governance program. It describes different approaches to defining data stewards and levels of data stewards, from operational to tactical.
2. The webinar will cover selecting the right approach to data stewardship for an organization and discussing an operating model of data governance roles at different levels, from executive to operational.
3. The role of the data steward is critical to data governance success and there are various ways to identify and recognize data stewards based on their existing responsibilities and relationships to the data they define, produce and use.
The business dimensional life cycle. Summarized from the second chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
The document provides guidance on designing a data and analytics strategy. It discusses why data and analytics are important for business success in the digital age. It outlines 13 approaches to a data and analytics strategy organized by core business strategy and value proposition. It emphasizes the importance of data literacy, governance, and quality. It provides examples of how organizations have used data and analytics to improve outcomes. The overall message is that a clear strategy is needed to communicate the business value of data and maximize its impact.
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, customer centricity, 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.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Your favorite data modeling tool, your partner in crime for Data Warehouse Au...FrederikN
The document discusses using a data modeling tool to automate the process of converting a logical data model to a physical data vault model. It provides examples of using the tool's built-in functionality to define properties, generate models, and make modifications without custom code. The goal is to leverage the tool's standard features to benefit from automation while supporting an incremental, multi-user approach to data vault modeling.
The Data Driven Enterprise - Roadmap to Big Data & Analytics SuccessBigInsights
The document discusses how data-driven companies are performing better financially and outlines the benefits of big data and analytics. It provides examples of companies using big data and analytics to improve customer experience through personalization, predict maintenance needs, and identify at-risk veterans to prevent suicide. The challenges of big data are also reviewed. Finally, it proposes a seven-step methodology for leveraging big data and analytics to address critical business challenges.
The document outlines a presentation about data analytics and business intelligence given by Chris Ortega. The presentation covers:
1. Definitions of data analytics and business intelligence.
2. Why data analytics and business intelligence are important for faster decision making, establishing a learning culture, and exploring opportunities.
3. The decision cycle and how business intelligence tools can automate parts of extracting data, analyzing it, and making decisions.
4. Limitations of current data analytics approaches like manual spreadsheet updates and the difficulty combining multiple data sources.
The document discusses managing content at LexisNexis, which involves:
1. Classifying millions of documents into a taxonomy to group similar content and enable better governance. This includes identifying content types and assigning owners.
2. Developing consistent enterprise data models and standards to represent content structures and relationships across systems. This includes defining quality principles and evaluating current practices.
3. Defining roles for content architects who create taxonomy, policies, and data models to consistently represent content for downstream uses like customer products.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Real-World Data Governance: What is a Data Steward and What Do They Do?DATAVERSITY
This document is a transcript from a webinar on the topic of "What is a Data Steward?". It discusses different definitions and approaches to defining the role of a Data Steward. Key points include:
- A Data Steward is someone who is responsible for data used in their job, including defining, producing, and ensuring quality of data.
- The role of a Data Steward depends on the organization's data governance approach. It should leverage existing responsibilities rather than assigning new roles.
- Different types of Data Stewards are discussed, including Operational Stewards, Domain Stewards, and Steward Coordinators.
- The responsibilities of Data Stewards include data definition, production
How to Make a Data Governance Program that LastsDATAVERSITY
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. Join Cameron, VP, Product Management, Precisely, as he shares a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term. He will give examples of organizations worldwide who have successfully implemented a data governance program by engaging with key stakeholders using innovative techniques such as gamification and data catalog scavenger hunts.
RWDG Slides: Three Approaches to Data StewardshipDATAVERSITY
There are different ways to connect people with data stewardship responsibilities. You can assign people to be data stewards, identify people as data stewards or recognize people as data stewards. These approaches vary in several ways.
Join Bob Seiner for this month’s installment of the RWDG webinar series where he will compare and contrast three distinct approaches to data stewardship. The approach you select and follow will heavily influence how data governance results will be achieved.
In this webinar Bob will discuss:
- Three approaches to data stewardship
- The influence of each approach on program results
- Factors to assist in the selection of the approach to follow
- Obstacles to being successful with each approach
- Benefits of following each approach
The document discusses best practices for capturing data requirements for projects that are data-rich. It emphasizes the importance of taking a top-down requirements approach and maintaining traceability between requirements. While use cases are useful, they often do not fully capture data needs. The document advocates looking beyond immediate needs to plan for business intelligence by capturing additional relevant data elements upfront.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
1. It is important to define data quality metrics that are purpose-fit and meaningful to customers. Dashboards should focus more on driving outcomes than just design.
2. Commonly used data quality dimensions include completeness, conformity, consistency, duplication, integrity, and accuracy. Specific metrics are then defined within each dimension tied to business objectives and rules.
3. Targets and trends provide valuable insights, with traffic light targets highlighting priority areas in red and trends showing progress over time.
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
RWDG Webinar: Data Steward Definition and Other Data Governance RolesDATAVERSITY
1. The document discusses defining data steward roles and responsibilities in a data governance program. It describes different approaches to defining data stewards and levels of data stewards, from operational to tactical.
2. The webinar will cover selecting the right approach to data stewardship for an organization and discussing an operating model of data governance roles at different levels, from executive to operational.
3. The role of the data steward is critical to data governance success and there are various ways to identify and recognize data stewards based on their existing responsibilities and relationships to the data they define, produce and use.
The business dimensional life cycle. Summarized from the second chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
The document provides guidance on designing a data and analytics strategy. It discusses why data and analytics are important for business success in the digital age. It outlines 13 approaches to a data and analytics strategy organized by core business strategy and value proposition. It emphasizes the importance of data literacy, governance, and quality. It provides examples of how organizations have used data and analytics to improve outcomes. The overall message is that a clear strategy is needed to communicate the business value of data and maximize its impact.
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, customer centricity, 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.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
Your favorite data modeling tool, your partner in crime for Data Warehouse Au...FrederikN
The document discusses using a data modeling tool to automate the process of converting a logical data model to a physical data vault model. It provides examples of using the tool's built-in functionality to define properties, generate models, and make modifications without custom code. The goal is to leverage the tool's standard features to benefit from automation while supporting an incremental, multi-user approach to data vault modeling.
The document discusses software design patterns for distributed applications. It introduces common patterns like Model-View-Controller (MVC), layers (presentation, business, data), and data access patterns (table data gateway, active record). It also provides examples of applying these patterns to problems like representing business entities and data, handling distributed transactions, and implementing specific business logic like revenue recognition. The goal of patterns is to provide reusable solutions to common problems in software architecture and design.
The document discusses software design patterns for distributed applications. It begins with introductions and definitions of patterns, then discusses specific patterns like Table Module, Table Data Gateway, and Active Record that address problems like representing business entities, data access, and application distribution. The document also provides examples of applying these patterns to a revenue recognition problem domain.
Greenfield Development with CQRS and Windows AzureDavid Hoerster
You have the dream situation - the ability to create a brand new product from the ground up. You want it to be scalable, performant and easily updatable (since you're building in an incremental fashion). But since you're a start-up, you don't want to deal with the headache and costs of hosting. Well, one possibility is taking advantage of cloud platforms (Windows Azure, for instance) and architecting your system using a CQRS-style pattern. See how you can create a system patterned on CQRS in the cloud. We can take a look at a product I created (BrainCredits) and also see how others would take advantage of Azure services to create CQRS-patterned systems.
SAP PowerDesigner Masterclass for the UK SAP Database & Technology User Group...George McGeachie
An opportunity I could not miss - a 2-hour presentation on modelling to an audience of database experts!
Starting with a brief look at using Visio and/or Excel for data modelling and governance, I talked about the extras we can gain by using PowerDesigner to design databases.
Of course, it's not 'just' databases we're concerned with, the relationships those databases have with our business and technical architecture is also important.
The next key topic is the role of Data Models and others (such as the Requirements model) in Governance and Design.
Next it's mapping data sources and targets to demonstrate and create data lineage, showing how PowerDesigner supports multiple DBMS Versions (and what you can do to change how it does that), creating a CUBE for Business Objects, finally (almost) focusing on the support provided for SAP IQ.
Finally, I described some real-world uses of PowerDesigner.
This document describes TIRTA ERP, an ERP system designed for the bottled water industry. It discusses master data management of customer, employee, and vehicle data. It also outlines business questions in various categories like finance, sales, shipments, purchasing and customer service. Dimensional models and tables are proposed for a sales data mart using a star schema. Finally, data integration from the TIRTA ERP database to the data warehouse and dimensional models is described. Visualization of sales reports using Jpivot is also mentioned.
The document discusses dimensional modeling for data warehousing. Dimensional modeling uses a star schema with fact and dimension tables. Facts are numeric measures located in large fact tables with keys linking to smaller dimension tables containing descriptive, relatively static data. Dimensional modeling provides an intuitive structure for high performance querying to answer business questions by analyzing facts across dimensions like product, time, customer etc. The document outlines characteristics of dimensions and facts and considerations for determining grain, dimensions and measures in dimensional modeling.
This document discusses designing HIBCC 2D barcodes. HIBCC is a barcode standard identified by the FDA for its UDI program and is widely used in the medical device industry. HIBCC barcodes can encode data to the ISO 15434 standard and allow inclusion of key information like the Labeler Identification Code, part number, lot number, and expiration dates in a 2D barcode format like Datamatrix. The document demonstrates creating a sample HIBCC 2D barcode for a product using barcode design software.
This document summarizes tips for using MongoDB and F# functional programming. It discusses how MongoDB allows for flexible querying of unstructured data using JavaScript and how F# focuses on reasoning through less code and avoiding side effects. It then describes an architecture using F# for data access and processing with MongoDB for storage. Formulas for calculating metrics are refactored into reusable functions for improved testability and understandability. Overall it presents strategies for improving performance, development, and the user experience when integrating functional programming with MongoDB.
The document discusses the logical design of a data warehouse using the ROLAP model. It describes star and snowflake schemas as two common approaches. Star schemas have fact and dimension tables with the fact table connected to normalized dimension tables via foreign keys. Snowflake schemas further normalize the dimension tables. Two exercises provide examples of designing schemas for a wine company and real estate agency data warehouses. SQL queries are also presented that could be run on the schemas.
The document provides an introduction to the ISPCMS project. It will have 4 team members working over 6 weeks. The project uses a Pentium IV processor with 256MB RAM and 40GB hard drive running Windows NT. It will be developed using Visual Basic 6.0 and Oracle 9i. The software engineering paradigm will use a prototype model. The objective of ISPCMS is to provide fast processing of consumer applications, automatic billing, easier payment acceptance, and computerized plan setup with data security. It will have various forms like new connection, disconnection, billing, payment, customer details, and payment details. There is potential to further expand the project into a complete ISPCMS system.
1. Dr. Lyndon Nixon presented on semantics enhancing augmented reality and making reality smarter through annotation of things of interest using image recognition.
2. Annotations are saved in a data model and directly integrated with content provider databases to retrieve additional metadata and representations.
3. The SmartReality platform uses image recognition services and a JSON data model to identify objects and return relevant annotations and content to display for users in an augmented reality client application.
Webinar: How Banks Use MongoDB as a Tick DatabaseMongoDB
Learn why MongoDB is spreading like wildfire across capital markets (and really every industry) and then focus in particular on how financial firms are enjoying the developer productivity, low TCO, and unlimited scale of MongoDB as a tick database for capturing, analyzing, and taking advantage of opportunities in tick data. This webinar illustrates how MongoDB can easily and quickly store variable data formats, like top and depth of book, multiple asset classes, and even news and social networking feeds. It will explore aggregating and analyzing tick data in real-time for automated trading or in batch for research and analysis and how auto-sharding enables MongoDB to scale with commodity hardware to satisfy unlimited storage and performance requirements.
This document provides an overview of data warehousing and data mining. It discusses key topics such as the definition of a data warehouse, its characteristics and architectures. It also describes how data is stored and modeled in a data warehouse. The document then covers data mining, outlining its process and elements. It discusses advantages of both data warehousing and data mining such as enhanced business intelligence and improved decision making. Disadvantages like privacy and security issues are also presented.
The document discusses Open Text Capture Center (OCC), a platform that collects documents from various sources, classifies and extracts data from them, and feeds the information into business applications. It can automatically process documents with optical character recognition, intelligent character recognition, and intelligent document recognition. The platform supports use cases like digital mailrooms, backfile conversion, and transaction processing. It recognizes different types of structured, semi-structured, and unstructured documents. Key components include monitoring, configuration, enterprise scan clients, and various recognition and validation modules.
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: TutorialMongoDB
For 30 years, developers have been taught that relational data modeling was THE way to model, but as more companies adopt MongoDB as their data platform, the approaches that work well in relational design actually work against you in a document model design. In this talk, we will discuss how to conceptually approach modeling data with MongoDB, focusing on practical foundational techniques, paired with tips and tricks, and wrapping with discussing design patterns to solve common real world problems.
[WSO2Con Asia 2018] Patterns for Building Streaming AppsWSO2
This slide deck explains how to enable digital transformation through streaming analytics and how easily streaming applications can be implemented
Learn more: https://wso2.com/library/conference/2018/08/wso2con-asia-2018-patterns-for-building-streaming-apps/
Simplify Feature Engineering in Your Data WarehouseFeatureByte
Feature Engineering is critical to successful delivery of AI solutions. Crafting relevant features from organization data requires business domain knowledge and creativity, powered by human capital in data science teams.
With growing adoption of machine learning and AI in organizations, there is a pressing need to develop processes around ML development and deployment to maximize productivity with limited resources. While there is no lack of tools for ML model management, solutions for feature engineering remains inadequate.
In this presentation, we outline our approach and design to make feature engineering efficient, repeatable and enjoyable for data science practitioners so they can experiment and iterate fast, without overlooking important issues such as scalability, deployment and auditability.
Keyword Services Platform (KSP) from Microsoft adCentergoodfriday
The document introduces Microsoft's Keyword Services Platform (KSP), which provides a set of web services for keyword research and analysis. KSP includes interfaces and APIs for term extraction, suggestion, forecasting, classification and other services. It allows researchers to develop keyword algorithms and providers, and makes them available to developers and applications through a common platform and standard interfaces. KSP provides benefits like server-side computing, combining outputs of different algorithms, and enabling AB testing of different providers for the same task.
Italya Posta Teskilatı Sybase Afaria KullaniyotSybase Türkiye
Poste Italiane equipped 44,000 mail carriers in Italy with mobile devices using SAP Afaria for mobile device management. This allowed the carriers to provide new services directly to customers and improved operations. SAP Afaria helped Poste Italiane maintain over 25,000 mobile devices with 99% availability.
SAP REAL TIME DATA PLATFORM WITH SYBASE SUPPORTSybase Türkiye
The document describes SAP's real-time data platform, which provides a single architecture for data management and applications. It offers benefits like IT landscape simplification, flexibility to innovate at an organization's own pace, and lower total cost of ownership. The platform includes the SAP HANA in-memory database, Sybase databases, and data management products to transact, store, process and analyze data in real time.
The document provides an overview of the SAP Sybase Event Stream Processor (ESP). Key points include:
- ESP allows for continuous insight and immediate response by analyzing events as they occur in real-time.
- It enables rapid application development through reduced dependence on specialist programming skills and faster implementation/deployment times.
- ESP has a non-intrusive deployment model that can adapt to existing data models and event-driven architectures.
- Key concepts of ESP include input streams, derived streams, windows, and continuous queries to filter, aggregate, and join streaming data.
- ESP Studio provides both visual and textual authoring capabilities using the Continuous Computation Language (CCL).
SAP Sybase IQ uses a technique called distributed query processing (DQP) that can improve query performance by breaking queries into pieces and distributing the pieces across multiple SAP Sybase IQ servers. DQP provides both intra-query and inter-query parallelism. It dynamically manages resources to balance workloads and avoid saturating the system. For DQP to be effective, the storage area network must have sufficient performance to support the increased parallelism.
To develop for the Android platform, developers need the Android SDK, which includes tools for developing, testing, and debugging apps. The primary programming language is Java. Developers create apps by writing code and designing user interfaces in XML layout files. Apps are tested on emulators and devices before being distributed via the Google Play Store.
Mobile device management provides security, visibility, and control for organizations using mobile devices. Without proper management, mobile devices face challenges including limited security, inconsistent connectivity, and a lack of centralized visibility and control for IT. Effective device management is needed to overcome these challenges, protect sensitive data, enforce compliance with regulations, and maximize the benefits of an organization's mobile workforce.
Sybase IQ is an analytic database management system designed for advanced analytics, data warehousing, and business intelligence environments. It can handle massive volumes of structured and unstructured data. Sybase IQ uses a column-oriented approach that stores and retrieves data by column rather than row, providing a 10-100 times performance boost over row-oriented databases. It also uses a shared-everything architecture that allows for massively parallel processing across an elastic computing grid for high scalability.
1. The customer asked the author to build an analytical platform to store data in a database and perform statistical analysis from a front-end interface.
2. The author chose an SAP Sybase IQ column-store database to store data, the open-source R programming language to perform statistical analysis, and RStudio as the front-end interface.
3. The solution provided a simple way to load and query large amounts of data, automated running of statistical models, and could be deployed in the cloud.
The Q2 2012 Appcelerator/IDC Mobile Developer Survey Report found:
1) Apple's iOS opened a 16% lead over Google's Android among developers who said which platform would win in the enterprise, at 53% to 37%.
2) Interest in developing for Android stabilized after declining in previous surveys, with 78% of developers saying they were very interested in Android phones and 69% in tablets.
3) Developers were cautiously optimistic about Windows 8 tablets, seeing opportunity for Microsoft to displace Android as the number two platform, though interest in Windows phones dropped sharply.
This document provides a comprehensive analysis comparing the data modeling capabilities of Sybase PowerDesigner 16.0 InformationArchitect and CA ERwin Data Modeler r8.1 Standard Edition. It examines how each tool supports key data modeling activities like creating different types of data models (conceptual, logical, physical), impact analysis across model levels, and model integration. The analysis finds that while both tools allow creating different model types and linking models, PowerDesigner provides more robust, integrated support through dedicated model types and built-in impact/lineage analysis. It concludes PowerDesigner better enables managing relationships across complex data modeling projects.
This document provides an executive summary of a white paper that reviews SAP Sybase IQ 15.4, a database platform designed to support business analytics and big data workloads. The white paper was sponsored by Sybase Inc. and conducted by independent analyst firm WinterCorp. Key points covered in the executive summary include:
- SAP Sybase IQ 15.4 aims to make the entire analytics process work smoothly and cost-effectively for both structured and unstructured data.
- It features a new analytic services layer, parallel processing with Hadoop, support for the R language, and expanded ecosystem support from third parties.
- At its core is a mature columnar database with data compression and query optimization capabilities designed for
14 Haziran 2012 tarihinde Sybase Türkiye tarafından yapılan PowerDesigner etkinliğinde, PowerDesigner'in SAP içindeki stratejik yol haritası hakkında temel bilgiler
This document discusses the importance of modeling and metadata management for businesses. It notes that 60% of IT projects fail or only partially succeed due to poor alignment between business and IT. Effective modeling helps ensure business goals, rules and requirements are met and that changes can be implemented with minimal risk, time and cost. Metadata management provides business agility, aids regulatory compliance, and forms the foundation for service-oriented architectures. The document promotes PowerDesigner as the leading tool for conceptual, logical and physical data modeling as well as enterprise architecture and metadata management.
This document discusses Replication Server - Real Time Loading (RTL) for replicating data from a source database in real-time to Sybase IQ for analytics purposes. It provides dial-in numbers and passcode for a presentation on the topic. The presentation will cover limitations of pre-RS 15.5 replication solutions to IQ, an overview of RTL, and the new RTL update capabilities in RS.
This document discusses the challenges of managing mobile applications at an enterprise scale. As more employees use mobile devices for work, the number of applications a company needs to support grows exponentially. Managing hundreds or thousands of applications across different devices and operating systems is a significant challenge. The document recommends adopting a common application development platform to simplify management. It also advocates for tools that can remotely distribute, configure, update and remove applications over a device's lifecycle.
Mobile devices are proliferating globally, with over 1 billion smartphones and tablets expected by 2016. This rapid adoption of mobile represents a shift to new systems of engagement that empower customers, partners and employees through context-aware apps and services. For CIOs, developing a formal mobile strategy including designating a chief mobility officer is critical to coordinating investments to build these new systems of engagement across the enterprise through a "design for mobile first" approach.
Sybase SUP Mobil Uygulama Geliştirme Genel BilgilendirmeSybase Türkiye
Sybase Unwired Platform 2.0 enables companies to leverage web developers to easily and securely extend enterprise data to various mobile devices. It features a hybrid web container with Blackberry support and SAP security integration. The platform allows quick development of mobile apps for common business processes with lower total cost of ownership compared to traditional approaches. It provides integration with backend systems, manages the full app lifecycle, and enables push notifications across platforms.
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.