This document discusses Accenture's methodology for migrating enterprise data platforms to the cloud at scale. It involves establishing a transformation office, standing up the target cloud data platform, migrating data and code in waves with change management, updating skills and operating models, implementing new governance, and decommissioning legacy systems. The key steps are developing a business case and migration strategy through discovery, planning the technology architecture and migration approach, and executing the migration while validating data and code through proofs of concept and migration waves.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Accenture migrated its analytics platform from an on-premise system to Google Cloud's Platform-as-a-Service model to address challenges around scalability, costs, and maintenance. This involved modernizing Accenture's data architecture and migrating over 400 terabytes of data and 50+ applications. The transition unlocked new analytics capabilities, increased cost savings through Google Cloud's pay-as-you-go model, and improved performance. Accenture also focused on developing its employees' cloud skills to support the new platform and drive business value from data insights.
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.
This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Accenture migrated its analytics platform from an on-premise system to Google Cloud's Platform-as-a-Service model to address challenges around scalability, costs, and maintenance. This involved modernizing Accenture's data architecture and migrating over 400 terabytes of data and 50+ applications. The transition unlocked new analytics capabilities, increased cost savings through Google Cloud's pay-as-you-go model, and improved performance. Accenture also focused on developing its employees' cloud skills to support the new platform and drive business value from data insights.
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.
This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
This document discusses the digital transformation of high-tech industries. It notes that profit and market value are migrating away from hardware and components towards internet platforms. It identifies trends like artificial intelligence, internet of things, cloud computing and edge processing driving changes. Few product companies have fully transformed, with internet platform companies outpacing spending on research and development. The document outlines a framework for companies to transform their core business while growing new business models in areas like connected products, living products and services, and ecosystem platforms. It emphasizes the need for digital talent and factories to drive transformation.
Cloud Migration: Moving Data and Infrastructure to the CloudSafe Software
The movement to the cloud is accelerating across industries. This is driven by the maturing of cloud technology, and by the sudden shift to a more distributed and remote workforce.
The cloud has many strengths from no longer having to purchase and manage infrastructure to its ability to grow seamlessly and to scale up and down to meet demands.
With all these benefits, many organizations are preparing cloud migration strategies (such as on-premise to the cloud) and are finding themselves overwhelmed by the process.
There are many things to consider when planning a cloud migration but the process does not have to be complicated or costly due to private services. Join this webinar to learn how you get started with your cloud migration today!
Capgemini Cloud Assessment is a Cloud agnostic, vendor aware methodology that focuses on low risk, high return business transformation. Additionally, it reduces TCO and provides an early view of ROI.
This closed loop assessment leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers utilizing our deep partner ecosystem. We deliver an end state architecture, business case and deployment roadmap in just six to eight weeks.
Capgemini Cloud Assessment - A Pathway to Enterprise Cloud MigrationFloyd DCosta
Capgemini Cloud Assessment offers a methodology and a roadmap for Cloud migration to reduce decision risks, promote rapid user adoption and lower TCO of IT investments. It leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers and provides three powerful deliverables in just six to eight weeks:
This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.
Application modernization involves transitioning existing applications to new approaches on the cloud to achieve business outcomes like speed to market, rapid innovation, flexibility and cost savings. It accelerates digital transformations by improving developer productivity through adoption of cloud native architectures and containerization, and increases operational efficiency through automation and DevOps practices. IBM's application modernization approach provides prescriptive guidance, increased agility, reduced risk, and turnkey benefits through tools, accelerators and expertise to help modernize applications quickly and safely.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Enabling Transformation through Agility & Innovation - AWS Transformation Day...Amazon Web Services
Learn how AWS can help transform your business. With AWS, enterprises are becoming more agile, secure, and scalable. This helps to promote innovation, shorten cycles to respond to business requirements, increase employee productivity, and retain and recruit top talent.
Improve business performance, reduce costs, and reinvent your IT strategies. Topics include how to maximize the value of your Enterprise workloads with AWS, foster a culture of innovation, manage risk and security, and new ways to think about product development, how to modernize the delivery of IT services, and best practices for adopting the cloud at scale.
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This document provides an overview of building a modern cloud analytics solution using Microsoft Azure. It discusses the role of analytics, a history of cloud computing, and a data warehouse modernization project. Key challenges covered include lack of notifications, logging, self-service BI, and integrating streaming data. The document proposes solutions to these challenges using Azure services like Data Factory, Kafka, Databricks, and SQL Data Warehouse. It also discusses alternative implementations using tools like Matillion ETL and Snowflake.
How Can I Build a Landing Zone & Extend my Operations into AWS to Support my ...Amazon Web Services
1) A Landing Zone is a configured, secure AWS environment based on best practices that provides a foundation for an enterprise's migration journey.
2) The document discusses how to structure a Landing Zone, including account structure for billing visibility, environment isolation, and centralized services/logs, as well as identity and access management and VPC design.
3) It also discusses building versus buying a Landing Zone and how pre-migration discovery involves decomposing technologies into families and mapping migration strategies to consider specific implications for the Landing Zone.
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
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.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
Cloud computing is an emerging technology that
offers opportunities for organisations to hire precisely those ICT
services they need (SaaS/PaaS/IaaS). Small and medium sized
enterprises (SMEs) can benefit a lot from software services that
are managed in a professional way. Cloud computing enables
them to overcome restrictions from low budgets and limited
resources for ICT. However, cloud adoption is challenging and
requires a clear cloud roadmap. Organisations lack knowledge of
cloud computing and are usually challenged by the adoption of
cloud services. In most cases, SMEs do not know what aspects
they have to take into consideration for a sound decision in
favour or against the cloud. A cloud readiness assessment is a
general approach to facilitate this decision-making process.
The presented study focuses on the development of an assessment framework for cloud services (SaaS) in the domain of enterprise content management (ECM) and social software (ecollaboration).
Cloud First does not mean you should jump into cloud without a well-constructed plan. The most successful organizations build the foundational capabilities first and create a roadmap that aligns people, technology, platforms, security, governance and culture. Many CIO’s want the benefits associated with cloud technologies but don’t necessarily know where to start and what questions to ask. This session will provide real world examples of the transformation process, challenges and successes that organizations have experienced in their cloud journey. We will define a low risk strategy and approach to achieve success in the cloud quickly. We will describe the sequencing requirements, milestones, and foundational services that need to be in place on Day 1. And finally, we will reveal the most critical and often overlooked component of your transformation strategy. Sponsored by Smartronix.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Azure Cloud Adoption Framework + Governance - Sana Khan and Jay Kumar Timothy McAliley
The document discusses Microsoft's Cloud Adoption Framework for Azure, which provides guidance to help organizations adopt cloud technologies in a controlled and stable manner while also enabling innovation and growth. The framework is modular and covers key areas of Ready, Plan, Adopt, and Govern to help align business and technology strategies. It provides best practices and blueprints for building cloud foundations, migrating workloads, modernizing applications, and establishing governance policies to manage cloud operations and ensure compliance. The goal is to help customers achieve a balance of control, stability, speed and results in their cloud adoption journey.
As technology advances, so does the data stack. Before you go into deploying a modern data stack at your company, here are some important things to know.
2020 Cloud Data Lake Platforms Buyers Guide - White paper | QuboleVasu S
Qubole's buyer guide about how cloud data lake platform helps organizations to achieve efficiency & agility by adopting an open data lake platform and why data lakes are moving to the cloud
https://www.qubole.com/resources/white-papers/2020-cloud-data-lake-platforms-buyers-guide
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
This document discusses the digital transformation of high-tech industries. It notes that profit and market value are migrating away from hardware and components towards internet platforms. It identifies trends like artificial intelligence, internet of things, cloud computing and edge processing driving changes. Few product companies have fully transformed, with internet platform companies outpacing spending on research and development. The document outlines a framework for companies to transform their core business while growing new business models in areas like connected products, living products and services, and ecosystem platforms. It emphasizes the need for digital talent and factories to drive transformation.
Cloud Migration: Moving Data and Infrastructure to the CloudSafe Software
The movement to the cloud is accelerating across industries. This is driven by the maturing of cloud technology, and by the sudden shift to a more distributed and remote workforce.
The cloud has many strengths from no longer having to purchase and manage infrastructure to its ability to grow seamlessly and to scale up and down to meet demands.
With all these benefits, many organizations are preparing cloud migration strategies (such as on-premise to the cloud) and are finding themselves overwhelmed by the process.
There are many things to consider when planning a cloud migration but the process does not have to be complicated or costly due to private services. Join this webinar to learn how you get started with your cloud migration today!
Capgemini Cloud Assessment is a Cloud agnostic, vendor aware methodology that focuses on low risk, high return business transformation. Additionally, it reduces TCO and provides an early view of ROI.
This closed loop assessment leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers utilizing our deep partner ecosystem. We deliver an end state architecture, business case and deployment roadmap in just six to eight weeks.
Capgemini Cloud Assessment - A Pathway to Enterprise Cloud MigrationFloyd DCosta
Capgemini Cloud Assessment offers a methodology and a roadmap for Cloud migration to reduce decision risks, promote rapid user adoption and lower TCO of IT investments. It leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers and provides three powerful deliverables in just six to eight weeks:
This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.
Application modernization involves transitioning existing applications to new approaches on the cloud to achieve business outcomes like speed to market, rapid innovation, flexibility and cost savings. It accelerates digital transformations by improving developer productivity through adoption of cloud native architectures and containerization, and increases operational efficiency through automation and DevOps practices. IBM's application modernization approach provides prescriptive guidance, increased agility, reduced risk, and turnkey benefits through tools, accelerators and expertise to help modernize applications quickly and safely.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Enabling Transformation through Agility & Innovation - AWS Transformation Day...Amazon Web Services
Learn how AWS can help transform your business. With AWS, enterprises are becoming more agile, secure, and scalable. This helps to promote innovation, shorten cycles to respond to business requirements, increase employee productivity, and retain and recruit top talent.
Improve business performance, reduce costs, and reinvent your IT strategies. Topics include how to maximize the value of your Enterprise workloads with AWS, foster a culture of innovation, manage risk and security, and new ways to think about product development, how to modernize the delivery of IT services, and best practices for adopting the cloud at scale.
The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects.
Solution architecture tends to concern itself with functional, technology and software components of the solution
Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation.
Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture
The objective of data design for solutions is the same as that for overall solution design:
• To capture sufficient information to enable the solution design to be implemented
• To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers
• To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey
Solution data architecture avoids problems with solution operation and use:
• Poor and inconsistent data quality
• Poor performance, throughput, response times and scalability
• Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment
• Poor reporting and analysis
• Poor data integration
• Poor solution serviceability and maintainability
• Manual workarounds for data integration, data extract for reporting and analysis
Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This document provides an overview of building a modern cloud analytics solution using Microsoft Azure. It discusses the role of analytics, a history of cloud computing, and a data warehouse modernization project. Key challenges covered include lack of notifications, logging, self-service BI, and integrating streaming data. The document proposes solutions to these challenges using Azure services like Data Factory, Kafka, Databricks, and SQL Data Warehouse. It also discusses alternative implementations using tools like Matillion ETL and Snowflake.
How Can I Build a Landing Zone & Extend my Operations into AWS to Support my ...Amazon Web Services
1) A Landing Zone is a configured, secure AWS environment based on best practices that provides a foundation for an enterprise's migration journey.
2) The document discusses how to structure a Landing Zone, including account structure for billing visibility, environment isolation, and centralized services/logs, as well as identity and access management and VPC design.
3) It also discusses building versus buying a Landing Zone and how pre-migration discovery involves decomposing technologies into families and mapping migration strategies to consider specific implications for the Landing Zone.
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
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.
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
Cloud computing is an emerging technology that
offers opportunities for organisations to hire precisely those ICT
services they need (SaaS/PaaS/IaaS). Small and medium sized
enterprises (SMEs) can benefit a lot from software services that
are managed in a professional way. Cloud computing enables
them to overcome restrictions from low budgets and limited
resources for ICT. However, cloud adoption is challenging and
requires a clear cloud roadmap. Organisations lack knowledge of
cloud computing and are usually challenged by the adoption of
cloud services. In most cases, SMEs do not know what aspects
they have to take into consideration for a sound decision in
favour or against the cloud. A cloud readiness assessment is a
general approach to facilitate this decision-making process.
The presented study focuses on the development of an assessment framework for cloud services (SaaS) in the domain of enterprise content management (ECM) and social software (ecollaboration).
Cloud First does not mean you should jump into cloud without a well-constructed plan. The most successful organizations build the foundational capabilities first and create a roadmap that aligns people, technology, platforms, security, governance and culture. Many CIO’s want the benefits associated with cloud technologies but don’t necessarily know where to start and what questions to ask. This session will provide real world examples of the transformation process, challenges and successes that organizations have experienced in their cloud journey. We will define a low risk strategy and approach to achieve success in the cloud quickly. We will describe the sequencing requirements, milestones, and foundational services that need to be in place on Day 1. And finally, we will reveal the most critical and often overlooked component of your transformation strategy. Sponsored by Smartronix.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Azure Cloud Adoption Framework + Governance - Sana Khan and Jay Kumar Timothy McAliley
The document discusses Microsoft's Cloud Adoption Framework for Azure, which provides guidance to help organizations adopt cloud technologies in a controlled and stable manner while also enabling innovation and growth. The framework is modular and covers key areas of Ready, Plan, Adopt, and Govern to help align business and technology strategies. It provides best practices and blueprints for building cloud foundations, migrating workloads, modernizing applications, and establishing governance policies to manage cloud operations and ensure compliance. The goal is to help customers achieve a balance of control, stability, speed and results in their cloud adoption journey.
As technology advances, so does the data stack. Before you go into deploying a modern data stack at your company, here are some important things to know.
2020 Cloud Data Lake Platforms Buyers Guide - White paper | QuboleVasu S
Qubole's buyer guide about how cloud data lake platform helps organizations to achieve efficiency & agility by adopting an open data lake platform and why data lakes are moving to the cloud
https://www.qubole.com/resources/white-papers/2020-cloud-data-lake-platforms-buyers-guide
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
This document discusses three customer case studies of telecom companies using Cloudera's Enterprise Data Hub:
1) SFR used the data hub to create a centralized data store and 360-degree view of customers, combining structured and unstructured data from multiple sources for real-time search, reporting and analysis. This improved the customer experience and increased data warehouse performance.
2) British Telecom used the data hub to accelerate data processing from 24+ hours to near real-time, addressing issues with disparate customer databases and long ETL windows that limited access to up-to-date customer information.
3) Telkomsel deployed the data hub to gain insights from customer, network and transactional data to
Conventional data warehouses are unable to keep up with today's data needs due to their rigid and costly architectures based on outdated assumptions. Snowflake has reinvented the data warehouse as an elastic cloud service that can scale on demand to handle diverse and rapidly growing data sources while reducing costs by 90% compared to traditional solutions. Snowflake's unique architecture leverages the flexibility of the cloud to independently scale storage, compute, and users without disruption, enabling businesses to focus on analyzing data rather than managing infrastructure.
Accelerate Migration to the Cloud using Data Virtualization (APAC)Denodo
This document summarizes an upcoming webinar from Denodo about data virtualization. The webinar will cover challenges with cloud migration and how data virtualization can help accelerate cloud migration. It will include discussions of cloud use cases, migration strategies, case studies and a product demonstration. The agenda outlines topics on challenges with cloud migration, migration architectures, use cases and case studies, a product demo, and Q&A.
Organizations are facing increasing demands to process data and run mixed workloads across on-premise, cloud, and edge environments. Dell PowerEdge servers provide scalable platforms to optimize infrastructure and support these diverse workloads. PowerEdge servers enable workloads to run efficiently on-premise or in hybrid cloud environments, and provide high performance, security, flexibility and remote management. This allows IT organizations to seamlessly scale infrastructure as needs change.
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Accenture-Cloud-Data-Migration-POV-Final.pdf
1. Migrate data to cloud @ scale while retiring technical debt
Cloud Data
Migration
2. 2
Modernizing Cloud Data Foundations
Executive summary
As data becomes more and more important to modern business,
enterprises recognize that the effective and responsible use of data at
scale determines a company’s present and future success.
Cloud has become a key component of managing data capital at scale.
But most valuable enterprise data is currently locked-in legacy data
warehouses and data lakes in on-premise data centers. By migrating
their data platforms to the cloud, enterprises can not only remove their
data center constraints and lower their data management costs, but
also dramatically increase the
value they get from their data itself.
To successfully migrate to cloud, a partner is needed that provides
deep industry expertise, comprehensive technology solutions and an
industrialized end-to-end approach that accelerates value and enables
data-driven business reinvention.
Get more from cloud, faster.
Data is a new form of capital1
at the
heart of everything an enterprise aspires
to do—from innovative new business
models, to more efficient operations, to
deeper partnerships with its ecosystem.
3. 3
Modernizing Cloud Data Foundations
These companies have built out massive data landscapes
on-premise in order to make data available for so many
business users and use cases. On-premise Data Lakes built on
Cloudera and Hortonworks technology (now merged) have
been populated for Data Scientists and Data Analysts. On-
premise Data Warehouses built on technologies like Teradata,
Netezza, and Exadata have been structured to enable efficient
consumption of analytics and insights by business analysts
and business leads. And on-premise relational databases built
on technologies including Oracle and DB2 have served to
structure and join data sets for a variety of reasons within the
overall enterprise data landscape.
Today, many companies are running into issues with these
large on-premise installations. Some organizations are facing
performance and capacity issues that require expensive
hardware to scale at the rate of enterprise data growth.
Some are unable to effectively incorporate new types of data
sources (e.g. unstructured, streaming) and workloads (e.g. AI/
ML). Most consider their on-premise licensing costs and total
cost of ownership to be too high. And all are watching the
meteoric rise of the public cloud, with most building out new
strategic data assets on the cloud even while their center of
data gravity is on-premise.
Companies have invested heavily
in on-premise data landscapes
Over the past ten years, there has been tremendous growth in enterprise data acquisition,
storage, management, and consumption. Leading companies in all industries have sought to
solve business problems and unlock enterprise value with data and analytics.
4. 4
Modernizing Cloud Data Foundations
As cloud capabilities and adoption continue to increase, becoming
a cloud-first organization has shifted from a future aspiration to an
urgent mandate for today. And given the explosion in the volume and
strategic importance of data available to the enterprise, data on cloud
is a critical part of that mandate.
In particular, for enterprises that have already invested in large on-
premises data platforms, cloud offers the prospect of scale, agility,
significantly lower costs, and the ability to extract even more value.
This can be seen most clearly by looking at four key drivers of a cloud
data migration: infrastructure, skills, architecture, and technology.
Data on cloud
represents a critical
pivot to the future
*Not all providers shown
Data
Sources
Machine
Learning
Natural
Language
Processing
Consumable
Intelligence
Data Capital
Management
Governance
Raw
Data
Integrated
Data
For-Purpose
Data
Supply
Chain
Management Access
Optical
Character
Recognition
Operational
Systems/Apps
Ecosystems
& Networks
Products
& Services
Big Bets
Customer
Experience
Monetization
Augmented
Analytics
Cloud Service
Providers
Databases Files Sensors Devices
Reinvented
Enterprise
Cloud
Ecosystem
5. 5
Modernizing Cloud Data Foundations
Get your data on cloud faster, more
cost effective and with reduced risk.
Infrastructure is fixed and depreciating.
Data center maintenance skills are your
responsibility.
Data architecture typically comprises disparate
point solutions accreted over
years or decades.
Data technologies are increasingly outdated,
incurring ever greater technical debt.
Infrastructure is elastic and available on demand.
That means faster data query performance, reduced future infrastructure investments, greater
business agility, and overall lower total cost of ownership.
Maintenance skills are no longer necessary as data center management is provided by the cloud
provider.
That means you can concentrate your investments in more strategic, value-generating skillsets—people
who can analyze and get insights from data, not just maintain it.
A cloud migration is an opportunity to hit refresh, creating an end-to-end strategic architecture.
That means you can manage your data strategically while optimizing data management costs. You can
also increase business reusability dramatically by breaking down legacy data siloes and converging your
multiple data platforms into one.
You benefit from an ecosystem of cloud first, continuously updated technologies.
That means you can start building your future target technology state today, rationalizing
your expenditure by shifting away from legacy to cloud solutions that support your future business
capabilities.
In fact, in Accenture’s experience, cloud can yield between 20 and 35 percent in cost savings from servers, facilities and labor alone.
From on premises… …to the cloud
6. 6
Modernizing Cloud Data Foundations
To any business getting started, a cloud migration can appear
daunting. That’s understandable: years of legacy code exists in
the data platforms. There are numerous cloud platforms and
cloud first services to choose from—both from cloud providers
themselves and from third parties like Teradata, Snowflake
and Cloudera. What’s more, new services are constantly being
released to the market.
The key to managing this complexity and accelerating a
migration?
Have an end-to-end approach that ensures you plan your
migration effectively first and then use the right delivery
methods and automation tools to reduce cost and risk of
execution @ scale.
Migrating to the cloud
is complex...
...Get your data to cloud faster.
Sources
DATA INGESTION
/ ETL
JOB
ORCHESTRATION
/ SCHEDULER
COTS
ETL
COTS
ETL
PLATFORM ETL – Custom, Stored Procs, SQL
Bi / Visualization
Advanced Analytics
Acquire data from sources
and load into target
- COTS ETL tool - INFA /
Podium / DataStage
- Hadoop - Sqoop
- Teradata – Fastload,
Multiload
- Kafka
- Custom
Schedule & sequence jobs / manage
dependency
- Autosys / Control-M / Tidal
Perform ETL functions within platform /
move data within zones or extract
- Hadoop - HiveQL, Spark
- Teradata – BTEQ, Stored Procs, Teradata
SQL
Perform ETL functions within platform /
move data within zones or extract
- COTS ETL tool - INFA / Talend / DataStage
Database schema and data
stored in platform
- Schema – databases, tables ,
columns, views
- Data
BI Reports / Dashboards that consume data
- BI – Cognos / BO
- Viz – Tableau / Qlik
Advanced Analytics / AI-ML using data
- Data Prep – Alteryx / Trifacta / Paxata
- COTS – SAS / Domino
Extracts
APIs
Data extracts
from database
- Using SQL or
custom scripts
APIs for App-App
access
- Apogee / Mule
IDENTITY /
ACCESS
CONTROL
Identity & Access Policies
- Active Directory
- Hadoop – Sentry
- In-database
Anatomy of a data platform
7. 7
Modernizing Cloud Data Foundations
Accenture’s Data Migration
to Cloud Methodology
1. Transformation office. Establish a transformation office, if needed, and set its
budget and governance arrangements.
2. Platform standup. Stand up the target state cloud data platform, including its
security configuration.
3. Migration execution. Migrate data, code and consumption over a series of
waves in accordance with the plan and roadmap.
4. Change management. Manage the necessary cultural and behavioral change
effectively with a communications plan and marketing campaigns.
5. Talent and skills. Identify skillsets for the cloud, upskilling workers or creating
new roles as needed.
6. Operating model. Define the cloud-first data operating model, plus ways of
working for the duration of the transition.
7. Data governance. Create and operationalize a new data governance
framework for the cloud.
8. Decommissioning. Ensure obsolete data platforms and assets are
decommissioned to release funds and maximize the value of the migration.
Discovery: Migration Strategy & Planning Conversion & Validation: Data Migration @ Scale
1. Business case. Build the strongest case for your move to the cloud, developing
a clear understanding of the financial implications of your multi-million-dollar
data migration. How much will it cost in the cloud? What are my migration
costs? What will be my dual run costs?
2. Discovery.What data sources do you have now? How frequently are they used?
How is ETL used through the data platform? What are your consumption points and
feeds? How are they related? What are the dependencies?
3. Migration approach. How will you migrate your data platform? Lift and Shift
what you have in the data center? Re-platform technologies? Modernize the
architecture post migration? How do current capabilities map to those in cloud?
4. Technology and architecture. Set a target state, plus an interim transition
state, understanding all the moving parts—and how consumption will change—
throughout the transition. What cloud services will be needed?
5. Migration plan and roadmap. Feed all the analysis into a detailed migration
plan and roadmap. How long will it take to migrate? What will be the sequence
of waves? Will we do it by line of business or data domains?
6. Proofs of concepts. Build, test, and iterate components like target state data
warehouses or accelerator tools before deploying at scale.
8. 8
Modernizing Cloud Data Foundations
Enterprises must heavily leverage automation in order to reduce
the time, cost and risk of data migrations. This includes automation
solutions across the phases of Discovery, Conversion, and Validation:
Human + Machine: Data
migration automation tools
reduce migration time and cost
Discovery automation performs in-depth analysis of on-premise
database objects, lineage and dependency, and BI & Analytics with
interactive dashboards providing details needed for the migration
roadmap (e.g. data temperature, dependencies).
Discover
Conversion brings automation to the largest effort area of the
migrations.
For a given set of sources and targets, it can help optimize migration
strategy and data, code, and consumption migration and conversion
at scale.
Convert
Validation automation helps with the data migration last-mile. It can help
to automate data reconciliation, testing and validation post-migration.
Validate
9. 9
Modernizing Cloud Data Foundations
From opportunity to operations
An end-to-end offering means you are uniquely positioned to support a data platform migration at any point along the journey. A trusted partner
can support from the initial business case to proof of concept and from the migration itself or to running day-to-day operations in the cloud.
• Realize value early and often. Use ideation and co-creation teams
to quickly develop use cases, freeing the core team to focus on
getting early value from the migration.
• Focus on decommissioning. Use change management to support
the business in a quick transition to new cloud platforms, enabling
the early decommissioning of legacy technologies.
• Get stakeholders involved. Ensure business leaders and data
users across the organization receive clear communication and are
aligned with the migration.
• Build skills in the cloud. Integrate data users into the process,
encouraging them to gain the new skills they’ll need in the cloud,
ensuring a seamless transition.
• Integrate security and data privacy from the start. Build access
and control policies into the technical design, considering what
controls and permissions will be maintained from the current
platform.
• Minimize disruption to the business. Phase the migration to
ensure minimal disturbance to data users, focusing on moving
common datasets and processes together.
Deep experience is needed to support large enterprises in their data
platform migrations to predict and mitigate many of the delivery risks:
Cloud migration
business case
Tech POCs /
evaluations
Cloud migration
planning
Cloud migration
execution
Cloud platform
operations
10. 10
Modernizing Cloud Data Foundations
Kick start a data-driven
reinvention in the cloud
Cloud enables organizations to break free from the constraints
of on-premises data storage and compute. Its cost-effectiveness
and flexibility, combined with its scalability and innovation
potential, mean you can optimize your data platform far more
effectively while simultaneously opening up the possibility of
new data-driven business models and revenue streams.
Today, Cloud is an essential part of managing data as
strategic capital. Every cloud-first enterprise should now be
looking to migrate its data platforms to the cloud—and fuel
a data-driven reinvention of its business.
Sources
1. Accenture, July 2020, Data is the New Capital, www.accenture.com/us-en/insights/technology/data-new-capital
11. 11
Modernizing Cloud Data Foundations
Authors
Sharad Kumar
CTO, Accenture Cloud First | Data & AI
Prateek Peres da Silva
Growth & Strategy, Accenture Cloud First | Data & AI