The document provides guidance on preparing a data migration plan. It discusses the importance of project scoping, methodology, data preparation, and data security when planning a data migration. Specifically, it recommends thoroughly reviewing all aspects of the project and data in the planning stages to identify risks and issues early. This helps reduce risks and ensures the migration is completed according to best practices.
Data Migration Plan PowerPoint Presentation SlidesSlideTeam
Data transfer is a complex process for every business. Keep this in mind we have created Data Migration Plan PowerPoint Presentation Slides. There are various slides provided in this information transfer plan PowerPoint complete deck such as data migration approach, steps, a simplified illustration of data migration steps, lifecycle, process, data migration on the cloud, and many more. Our team of experts uses all sorts of editable charts, icons and graphs to design these impressive presentation slides. The content ready information transfer PPT visuals are fully editable. You can modify, color, text, and font size. It has relevant templates to cater to your business needs. Outline all the important concepts without any hassle. Furthermore, data migration strategy PPT slides are apt to present related concepts like data conversion, data curation, data preservation, system migration to name a few. Showcase varied ways of data transformation using this professionally designed information migration PPT visual.
Many significant business initiatives and large IT projects depend upon a successful data migration. Your goal is to minimize as much risk as possible through effective planning and scoping. This paper will provide insight into what issues are unique to data migration projects and offer advice on how to best approach them.
Data Migration Strategies PowerPoint Presentation SlidesSlideTeam
Data migration is a key consideration of any system implementation. Discuss the data transfer plans with this content ready Data Migration Strategies PowerPoint Presentation Slides. Data transformation plan PowerPoint complete deck is a systematic presentation which includes PPT slides such as data migration approach, steps, a simplified illustration of data migration steps, lifecycle, process, data migration on the cloud, and many more. Besides this, data transfer plan PPT slides are apt to present related concepts like data conversion, data curation, data preservation, system migration to name a few. The content ready information transfer PPT visuals are fully editable. You can modify, color, text, and font size. It has relevant templates to cater to your business needs. Outline all the important concepts without any hassle. Showcase the process of selecting, preparing, extracting and transforming data using this professionally designed information migration plan presentation design.
Data Migration PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Migration PowerPoint Presentation Slides. The deck constituents are Data Migration, Data Transfers, Information Migration.
Data Migration Steps PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Migration Steps Powerpoint Presentation Slides. This PPT deck displays twenty-six slides with in-depth research. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. When you download this deck by clicking the download button below, you get the presentation in both standard and widescreen format. All slides are fully editable. change the colors, font size, add or delete text if needed. The presentation is fully supported with Google Slides. It can be easily converted into JPG or PDF format.
Data Migration Plan PowerPoint Presentation SlidesSlideTeam
Data transfer is a complex process for every business. Keep this in mind we have created Data Migration Plan PowerPoint Presentation Slides. There are various slides provided in this information transfer plan PowerPoint complete deck such as data migration approach, steps, a simplified illustration of data migration steps, lifecycle, process, data migration on the cloud, and many more. Our team of experts uses all sorts of editable charts, icons and graphs to design these impressive presentation slides. The content ready information transfer PPT visuals are fully editable. You can modify, color, text, and font size. It has relevant templates to cater to your business needs. Outline all the important concepts without any hassle. Furthermore, data migration strategy PPT slides are apt to present related concepts like data conversion, data curation, data preservation, system migration to name a few. Showcase varied ways of data transformation using this professionally designed information migration PPT visual.
Many significant business initiatives and large IT projects depend upon a successful data migration. Your goal is to minimize as much risk as possible through effective planning and scoping. This paper will provide insight into what issues are unique to data migration projects and offer advice on how to best approach them.
Data Migration Strategies PowerPoint Presentation SlidesSlideTeam
Data migration is a key consideration of any system implementation. Discuss the data transfer plans with this content ready Data Migration Strategies PowerPoint Presentation Slides. Data transformation plan PowerPoint complete deck is a systematic presentation which includes PPT slides such as data migration approach, steps, a simplified illustration of data migration steps, lifecycle, process, data migration on the cloud, and many more. Besides this, data transfer plan PPT slides are apt to present related concepts like data conversion, data curation, data preservation, system migration to name a few. The content ready information transfer PPT visuals are fully editable. You can modify, color, text, and font size. It has relevant templates to cater to your business needs. Outline all the important concepts without any hassle. Showcase the process of selecting, preparing, extracting and transforming data using this professionally designed information migration plan presentation design.
Data Migration PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Migration PowerPoint Presentation Slides. The deck constituents are Data Migration, Data Transfers, Information Migration.
Data Migration Steps PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Migration Steps Powerpoint Presentation Slides. This PPT deck displays twenty-six slides with in-depth research. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. When you download this deck by clicking the download button below, you get the presentation in both standard and widescreen format. All slides are fully editable. change the colors, font size, add or delete text if needed. The presentation is fully supported with Google Slides. It can be easily converted into JPG or PDF format.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Data Warehousing in the Cloud: Practical Migration Strategies SnapLogic
Dave Wells of Eckerson Group discusses why cloud data warehousing has become popular, the many benefits, and the corresponding challenges. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema, or rebuilding of data pipelines is needed.
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.
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.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
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.
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
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
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.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Data Warehousing in the Cloud: Practical Migration Strategies SnapLogic
Dave Wells of Eckerson Group discusses why cloud data warehousing has become popular, the many benefits, and the corresponding challenges. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when architectural modernization, restructuring of database schema, or rebuilding of data pipelines is needed.
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.
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.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise.
The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view.
Designing a data fabric enables the enterprise respond to and take advantage of key related data trends:
• Internal and External Digital Expectations
• Cloud Offerings and Services
• Data Regulations
• Analytics Capabilities
It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges
• More and more data of many different types
• Increasingly distributed platform landscape
• Compliance and regulation
• Newer data technologies
• Shadow IT where the IT function cannot deliver IT change and new data facilities quickly
It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.
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.
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
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
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.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
The aim of agile methods is to reduce overheads in the software process (e.g. by limiting documentation) and to be able to respond quickly to changing requirements without excessive rework.
This presentation is about Scrum methodology. First it reviewed traditional SDM and then talk about Agile and Scrum
Session Abstract:
Agile framework is based on iterative development, where requirements and solutions evolve through collaboration between self-organizing cross-functional teams. It’s a set of values and principles that help teams respond to unpredictability through incremental, iterative work cadences and continuous feedback.
Scrum is the most popular methodology under the Agile umbrella. Scrum emphasizes empirical feedback, team self-management, and striving to build shippable product increments within short iterations.
Kanban is another popular flavor of Agile that focuses on visualizing and managing the flow of work, in order to balance demand with available capacity and remove bottlenecks.
Learning Objectives:
> Gain a broad understanding of the Agile framework
> Discover Scrum and Kanban, the two most widely used Agile methodologies, and see how they can be used in construction industry
> Find out how Scrum and Kanban can be combined to have the best of both worlds (Scrumban)
Live migrating a container: pros, cons and gotchasDocker, Inc.
In this talk I will briefly show why you might want to live migrate a container, why you might want to avoid doing this and what can be done instead. The main topic of the talk would to demonstrate why live migrating a container is more complex than live migrating a virtual machines and what can be done with this complexity.
On Wednesday, May 27, Red Hat and its partners Xebia, Ciber, Profict and Sogeti had organized the seminar "Business-critical processes with JBoss'. They have shared what solutions they have developed for customers like the Nationale Postcodeloterij, NXP and NS-HiSpeed and what their benefits are.
This seminar was organized in Utrecht.
Xebia had covered the topic: "Migration to JBoss, Made Cost Effective and Easy"
This presentation will focus on removing the myths about migrations, a guide to the intelligent pre-migration preparation and includes a demonstration of TERMINALFOUR's Automated migration tool in action.
View the presentation in full here: https://youtu.be/NxCfUbvpSDc
This is supposed to be an introductory presentation on Agile.
In this presentation I give some examples of heavy weight methods and their implications on your project. Then I give a quick overview of Agile methods, the rationale behind it, its origin, its values and principles. I move on to describe that what I see happening today in the industry is really waterfall in the name of Agile. I give some reasons why this is happening and then I give some pointers to move away from this flawed thinking.
Bottom line, Agile is not a Silver Bullet and don't fall pray to marketing gimmicks. Question dogmatic claims. Adapt Agile to your needs and take baby steps.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Making the Most of Your Data A Comprehensive Guide to Successful Data Migrati...Onix Cloud
Data migration with Onix Google Cloud. Our comprehensive guide offers expert insights, tools, and strategies for a successful transition. From planning to execution, optimize your data's journey with Onix's trusted solutions. Maximize efficiency, minimize risks, and harness the power of your data with confidence.
2. INFORMATION GATHERING.pptx Computer Applications in PharmacyVedika Narvekar
B.Pharm sem 2
Computer Applications in Pharmacy
requirement and feasibility analysis, data flow diagrams, process
specifications, input/output design, process life cycle, planning and
managing the project
A Brief Introduction to Enterprise Architecture Daljit Banger
Presentation to Metropolitan University (London) on the 16th Feb 2017.
The purpose of the session was to introduce core basic concepts around Enterprise Architecture and discuss the role of the Enterprise Architect .
You have started your asset finance systems implementation. What are the typical pain points ahead? In this third of three articles. Richmond Consulting Group looks at three areas that will need attention if the journey is to be a smooth one!
We welcome comments and would be happy to help you get your project off to a good start.
You have started your asset finance systems implementation. What are the typical pain points ahead? In this third of three articles. Richmond Consulting Group looks at three areas that will need attention if the journey is to be a smooth one!
We welcome comments and would be happy to help you get your project off to a good start.
You have started your asset finance systems implementation. What are the typical pain points ahead? In this third of three articles. Richmond Consulting Group looks at three areas that will need attention if the journey is to be a smooth one!
We welcome comments and would be happy to help you get your project off to a good start.
We offer a guide to change management that enables data quality throughout the organization and a sample operational data quality scorecard. This helps making operational data quality a way of life in your enterprise, from data origination of data sources to transformation
2 System development life cycle has six stages of creating a sys.docxtamicawaysmith
2 System development life cycle has six stages of creating a system. 3 Each step is important as it plays a significant role in a project. The development cycle involves the developing and implementing systems in order to retire the information systems from initiating, analyzing, designing the systems to the implementation and maintenance phases. The process is best used when creating or updating a database system and is most useful when undertaking a large project.
· Planning- Stage where you outline the problem, the main objectives, and all resources which will be required for use. 4 After that, you choose if you will create a new system, make some upgrades to the existing system or just leave the current system as it is.
· System Analysis- Determination of the client’s needs. The client is tangled as they clarify how they need the development to be carried out and in what way it will ensemble their needs. 4 Thus, documents the necessities and gets a sign-off from both the customer and administration to go forward with the system.
· System Design- It is the architectural phase. The members derive the logical plan and construction of flow of information for the system. Concrete coding is not yet underway at this period.
· System Implementation- We begin the actual coding of the system begin. Developing and installing of the system begins here. Maintenance, as well as any other future updates of the system, are carried out in this phase.
4 · System testing and Integration- After coding is comprehensive, the system goes over a severe test to see if it has any excuses and that it is steady. Once it concludes the assessments, the consumer can now use it.
· System maintenance- If a consumer has any inquiry or apprehensions about the system, they can get sustenance from the designers who remain to maintain the system. Operations such as backups and recovery can be performed in this stage as well as issuing of permissions by the system’s administrator.
Methodologies Software methodology is an outline used to assemble, strategize and regulate the development of a system. 4 Agile, RAD and JAD are software procedures, though, vary from each other.
3 Agile methodology is used for taking on software engineering schemes. They try to decrease peril by developing software in repetitions that can take up to 4 weeks. After 4 weeks have gone off each repetition, the members re-evaluate plan significances. It inspires teamwork.
There are several variances between JAD and RAD procedures. While both JAD and RAD employ teams that are contain users, managers, and Information Technology staff, they have quite a few points of dissimilarity. For example, JAD stresses on team-based information-gathering missions, that are only one phase of the growth process. RAD, however, is more of a trampled form of the whole process (Topi & Tucker, 2014). JAD is a prototypical that combines together commercial areas and IT professionals in a highly engrossed workshop. The prime re ...
Detail on the WITSML to PPDM mapping project, a joint initiative betwwen the PPDM Association and Energistics to standardise movement of E&P data in the Oil & Gas industry. We outline the project and place it in the context of a data management approach to E&P data.
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1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Execution from the test manager
Orchestrator execution result
Defect reporting
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
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Major cyber events in 2024
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2. • This is the Powerpoint version of our data
migration eGuide, which aims to help with the
development of a plan for a data migration. The
guide is based on our years of work in the data
movement industry, where we provide off-the-
shelf software and consultancy for
organisations across the world.
• Data migration is a complex undertaking, and
the processes and software used are
continually evolving. The approach in this guide
incorporates data migration best practice, with
the aim of making the data migration process a
little more straightforward.
• Don’t hesitate to get in touch with us at
info@etlsolutions.com if you have any
questions.
Introduction
www.etlsolutions.com
Download this PDF
eGuide for free at:
http://www.etlsolutions.co
m/free-eguide-preparing-
a-data-migration-plan/
3. • We should start with a quick definition of
what we mean by data migration. The
term usually refers to the movement of
data from an old or legacy system to a
new system.
• Data migration is typically part of a larger
programme and is often triggered by a
merger or acquisition, a business decision
to standardise systems, or modernisation
of an organisation’s systems.
• The data migration planning outlined in
this guide dovetails neatly into the overall
requirements of an organisation.
Definitions
www.etlsolutions.com
5. • While staff and systems play an
important role in reducing the risks
involved with data migration, early
stage planning can also help. It
identifies potential issues that may
occur later in the project, enabling
the organisation to plan the
mitigation of risk.
• Our consultants thoroughly
review and scope a project before
it starts. We find it’s practical to
divide the review into two parts: the
project’s structure and its technical
aspects.
Project scoping
www.etlsolutions.com
The project review evaluates these areas:
Are the deliverables and deadlines
clearly defined?
Is the budget sufficient?
Have all potential stakeholders been
included in the plan?
Are there communication plans in
place, and do they include all
stakeholders, senior management
and, if necessary, the wider
organisation?
Are there personnel in the right
number and with the right skills? Will
they be available for the duration of
the project? Specifically, are there
sufficient:
Business domain experts?
System experts?
Data migration experts?
6. The technical review assesses the quality of:
The proposed migration methodology
and workflow
The data security plan
The software available:
Technical features
Flexibility
Fit with the skills of the people working
on the project.
The volume and cleanliness of the
data to be migrated
Project scoping (continued)
www.etlsolutions.com
• Analysing these aspects in
the early stages of a
project will help to reduce
risk and realise best
practice.
• It also provides supporting
evidence when requesting
additional funding or other
resources.
8. • A clear methodology is essential for a staged, well-managed and
robust approach to data migration. According to a 2011 report by
Bloor, 38% of data migration projects run over time or budget. The
report identifies an effective methodology as one of the ways to
minimise these risks.
• However, industry-standard data migration methodologies are
scarce. One option is the Practical Data Migration methodology
developed by industry expert Johny Morris, which consists of training
and certification. Alternatively, most companies who provide data
migration services have their own methodology; ours consists of pre-
migration scoping, project assessments and a core migration
process.
• The complexity of data migration means that a chosen methodology
can seem like a sea of options, which can be difficult to get all the
stakeholders to buy into. Focus on the most startling element of the
migration – the fact that the legacy system will be turned off – and
the attention of the stakeholders is guaranteed.
Methodology
www.etlsolutions.com
9. Methodology (continued)
www.etlsolutions.com
• Standards are used to identify
problem areas early on, making
sure that the project don’t reach
the final stages with a hundred
different issues to sort out.
• For instance, at ETL Solutions
we have the Prince2
management standard, and use
ISO standards where
appropriate to underpin our data
migration methodology.
A robust methodology should include:
Extract design: how the data is
extracted, held and verified
Migration design: how data is
transformed into the target
structure
Mapping rules: the details of the
migration
Test overview: tools, reporting,
structure and constraints
Unit test: unit test specification
Integration test: integration test
specification
Recovery plan: recovery options
for each stage of the migration
Go live plan: actions required to
go live.
11. • It is crucial to thoroughly prepare data and systems before a
migration takes place. In particular, landscape analysis is an
important part of preparing for a data migration. It provides an
overview of the source and target systems, enabling the project
team to understand how each system works and how the data within
each system is structured.
• These areas should be reviewed systematically to ensure that
potential errors are identified in advance of the migration. Ideally,
the team should model the links and interactions between the
different systems involved, along with the data structures within each
system.
• Another important component of thorough preparation is data
assurance. This procedure validates the data discovered in the
landscape analysis and ensures that all data is fit for purpose. By
validating the data, the migration team are then free to focus solely
on structural manipulation and movement. Data assurance has
several phases: data profiling; data quality definition; and data
cleansing.
Data preparation
www.etlsolutions.com
12. • The aim of the data profiling phase is to ensure that any historical
data due to be migrated is suitable for the changes that are taking
place in the organisation. Profiling should be carried out to identify
areas of the data which may not be of sufficient quality. It should
include comprehensive checks of existing model structure, data
format and data conformance.
• A retirement plan should be used to define the data no longer
required. Any data to be retired should be recorded, along with a
description of what replaces it or why it can be removed. The data
that is no longer needed may have to be archived for tax purposes
or to meet the requirements of an industry’s governing bodies.
Data preparation: Data profiling
www.etlsolutions.com
13. • Data quality definitions state the quality that must be attained by
elements, attributes and relationships in the source system.
• The definitions or rules should be used during profiling to identify
whether or not the data is of the correct quality and format.
• All data quality rules should be listed at element level, such as data
table or flat file. All data quality issues and queries should be tracked
and stored.
Data preparation: Data quality definition
www.etlsolutions.com
14. • The first stage in data cleansing is to define which cleansing rules
will be carried out manually and which will be automated. Splitting
the rules into two enables the organisation’s domain experts to
concentrate on the manual process, while the migration experts
design and develop the automated cleansing. Typically, the manual
cleansing will be carried out before the migration, while the
automated cleansing may be carried out before the migration or as
part of the migration’s initial phase.
• Data verification is the part of the data cleansing process that checks
that the data is available, accessible, complete and in the correct
format. Our consultants often continue to carry out verification once
a migration has begun, ensuring that the information is optimised
prior to each stage of the migration.
• We find that data impact analysis is a crucial part of data cleansing.
Because cleansing data adds or alters values, data impact analysis
ensures that these changes do not have a knock-on effect on other
elements within the source and target systems. It also checks the
impact of data cleansing on other systems which currently use the
data, and on systems which may use the data once the migration is
complete.
Data preparation: Data cleansing
www.etlsolutions.com
16. • Data security has become a political and legal issue, particularly with
continuing high-profile data losses. Carrying out a data migration is
likely to require access to corporate or customer data that is likely to
be sensitive and business critical.
• It is crucial that all data is treated with respect. All sensitive
information, including customer data, should have detailed levels of
security in place. Before you start any data migration, check exactly
what levels are in place, and who is allowed access to the data and
when.
• Assess the value of the data to the business, in addition to the costs
that could arise from a security breach. Then make sure that the
security requirements of the migration reflect this value. They
should be cost-effective and not outweigh the risks highlighted in the
assessment.
Data security
www.etlsolutions.com
17. • Legal obligations should be
thoroughly checked.
• Statutory measures covering
data breach and data protection
are now in place in many
sectors.
• These often outline the areas of
security that have to be in place,
as well as stipulating operating
procedures to keep the data
secure.
Data security (continued)
www.etlsolutions.com
• Draw up data security plans early on and
embed them in the data migration plan.
• Areas to consider include:
How to ensure secure data transfer
How to create secure server access
How to ensure secure data access
Whether or not to increase the
number of permissions required to
transfer data
Clearance and vetting of personnel,
including outside consultants and
partners
The training or information sessions
required by personnel
Vetting of the software that will be
used for the migration.
Protocols for the use of email and
portable storage devices.
19. • The backing of senior business
leaders will improve the chances
of a data migration project going
smoothly and ensure that you
have the resources you need.
• The key is to remember that the
purpose of the migration is to
make the overall business more
effective and efficient, and to
ensure that this is communicated
properly.
• Here are some ways to gain buy-
in from senior management…
Business engagement
www.etlsolutions.com
• Align the project with business
priorities: The project results should
reflect the areas on which business
leaders tend to focus. These are
predominantly revenue and cost.
Senior managers need to be
convinced that real, monetary gain lies
in project success.
• Manage expectations: Be honest
about how long the project is going to
take and what will be asked of
management along the way.
• Link the benefits to specific business
issues: Show how current challenges
within the business will be helped by
the data migration project.
• Talk in terminology that management
can understand!
20. Business engagement (continued)
www.etlsolutions.com
Promote best practice: Great
processes can reflect
positively on a company’s
senior management. Show
in the scoping and strategy
documents at the outset how
the migration process uses
best practice and even,
where applicable,
accreditations.
Build in short and long-term
gains: Senior business
leaders are likely to want to
see short-term value added
to their bottom line after
making an investment in
data migration. Create some
quick wins to satisfy
business objectives.
Communicate the system
retirement plan: Be clear
about what will happen to
existing business resources
after the migration. Explain
how any changes can
mitigate the costs of the
migration itself.
21. • Download the PDF copy of this
guide for easy reading and
printing. It’s completely free of
charge!
• Visit us at:
http://www.etlsolutions.com/free
-eguide-preparing-a-data-
migration-plan/ to download
your copy.
Download your free copy of this guide
About us
At ETL Solutions, we tackle difficult data transformations. We deliver
expert data integration services and software for some of the world’s
leading organisations. Find out more at www.etlsolutions.com.
Images from Freedigitalphotos.net
Editor's Notes
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.
To keep things simple when I’m talking, we’ll discuss loading data into PPDM, but a lot of this applies to generic data loading – moving data out of PPDM, or not involving PPDM at all.
Data transformation is mudane from a business perspective, but very important to get right. The less time and trouble it causes, the more time you can spend doing more interesting things directly benefiting your business.
Badly loaded data by definition affects the quality of the data in your MDM store.