| data platform architecture diagram: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| data platform architecture diagram: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-03-17 In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. Summary Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you’ll maximize performance no matter which cloud vendor you use. About the book In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. What's inside Best practices for structured and unstructured data sets Cloud-ready machine learning tools Metadata and real-time analytics Defensive architecture, access, and security About the reader For data professionals familiar with the basics of cloud computing, and Hadoop or Spark. About the author Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. Table of Contents 1 Introducing the data platform 2 Why a data platform and not just a data warehouse 3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google 4 Getting data into the platform 5 Organizing and processing data 6 Real-time data processing and analytics 7 Metadata layer architecture 8 Schema management 9 Data access and security 10 Fueling business value with data platforms |
| data platform architecture diagram: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data platform architecture diagram: Java By Comparison Simon Harrer, Jörg Lenhard, Linus Dietz, 2018-03-22 Write code that's clean, concise, and to the point: code that others will read with pleasure and reuse. Comparing your code to that of expert programmers is a great way to improve your coding skills. Get hands-on advice to level up your coding style through small and understandable examples that compare flawed code to an improved solution. Discover handy tips and tricks, as well as common bugs an experienced Java programmer needs to know. Make your way from a Java novice to a master craftsman. This book is a useful companion for anyone learning to write clean Java code. The authors introduce you to the fundamentals of becoming a software craftsman, by comparing pieces of problematic code with an improved version, to help you to develop a sense for clean code. This unique before-and-after approach teaches you to create clean Java code. Learn to keep your booleans in check, dodge formatting bugs, get rid of magic numbers, and use the right style of iteration. Write informative comments when needed, but avoid them when they are not. Improve the understandability of your code for others by following conventions and naming your objects accurately. Make your programs more robust with intelligent exception handling and learn to assert that everything works as expected using JUnit5 as your testing framework. Impress your peers with an elegant functional programming style and clear-cut object-oriented class design. Writing excellent code isn't just about implementing the functionality. It's about the small important details that make your code more readable, maintainable, flexible, robust, and faster. Java by Comparison teaches you to spot these details and trains you to become a better programmer. What You Need: You need a Java 8 compiler, a text editor, and a fresh mind.That's it. |
| data platform architecture diagram: Architectural Diagrams Mi Young Pyo, 2015 The trendsetting architect Rem Koolhaas has carried it out to perfection, whereas the next generation of international stars refined it even more, giving us the unconventional presentation of designs and ideas in the form of diagrams. This method of presentation is easy to understand when dealing with the client and can be communicated internationally, beyond language and cultural barriers - a product of our globalised world. However, diagrams are now much more than explanations and form their own discipline in creative professions connected to design and construction. What looks simple is in fact a complex matter. This title in the series Construction and Design Manual is in its second edition and assembles 384 pages of diagrams by avant-garde architects and designers who specialise in public space, landscape architecture and urban planning. |
| data platform architecture diagram: Business Intelligence Guidebook Rick Sherman, 2014-11-04 Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources. |
| data platform architecture diagram: Clean Architecture Robert C. Martin, 2017-09-12 Practical Software Architecture Solutions from the Legendary Robert C. Martin (“Uncle Bob”) By applying universal rules of software architecture, you can dramatically improve developer productivity throughout the life of any software system. Now, building upon the success of his best-selling books Clean Code and The Clean Coder, legendary software craftsman Robert C. Martin (“Uncle Bob”) reveals those rules and helps you apply them. Martin’s Clean Architecture doesn’t merely present options. Drawing on over a half-century of experience in software environments of every imaginable type, Martin tells you what choices to make and why they are critical to your success. As you’ve come to expect from Uncle Bob, this book is packed with direct, no-nonsense solutions for the real challenges you’ll face–the ones that will make or break your projects. Learn what software architects need to achieve–and core disciplines and practices for achieving it Master essential software design principles for addressing function, component separation, and data management See how programming paradigms impose discipline by restricting what developers can do Understand what’s critically important and what’s merely a “detail” Implement optimal, high-level structures for web, database, thick-client, console, and embedded applications Define appropriate boundaries and layers, and organize components and services See why designs and architectures go wrong, and how to prevent (or fix) these failures Clean Architecture is essential reading for every current or aspiring software architect, systems analyst, system designer, and software manager–and for every programmer who must execute someone else’s designs. Register your product for convenient access to downloads, updates, and/or corrections as they become available. |
| data platform architecture diagram: Data Architecture: A Primer for the Data Scientist W.H. Inmon, Daniel Linstedt, Mary Levins, 2019-04-30 Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the bigger picture and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. - New case studies include expanded coverage of textual management and analytics - New chapters on visualization and big data - Discussion of new visualizations of the end-state architecture |
| data platform architecture diagram: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-04-20 Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. |
| data platform architecture diagram: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data platform architecture diagram: Modern Data Architectures with Python Brian Lipp, 2023-09-29 Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features Develop modern data skills used in emerging technologies Learn pragmatic design methodologies such as Data Mesh and data lakehouses Gain a deeper understanding of data governance Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market. By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What you will learn Understand data patterns including delta architecture Discover how to increase performance with Spark internals Find out how to design critical data diagrams Explore MLOps with tools such as AutoML and MLflow Get to grips with building data products in a data mesh Discover data governance and build confidence in your data Introduce data visualizations and dashboards into your data practice Who this book is forThis book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples. |
| data platform architecture diagram: Azure Data and AI Architect Handbook Olivier Mertens, Breght Van Baelen, 2023-07-31 Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions Purchase of the print or Kindle book includes a free PDF eBook Key Features Translate and implement conceptual architectures with the right Azure services Inject artificial intelligence into data solutions for advanced analytics Leverage cloud computing and frameworks to drive data science workloads Book DescriptionWith data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.What you will learn Design scalable and cost-effective cloud data platforms on Microsoft Azure Explore architectural design patterns with various use cases Determine the right data stores and data warehouse solutions Discover best practices for data orchestration and transformation Help end users to visualize data using interactive dashboarding Leverage OpenAI and custom ML models for advanced analytics Manage security, compliance, and governance for the data estate Who this book is forThis book is for anyone looking to elevate their skill set to the level of an architect. Data engineers, data scientists, business intelligence developers, and database administrators who want to learn how to design end-to-end data solutions and get a bird’s-eye view of the entire data platform will find this book useful. Although not required, basic knowledge of databases and data engineering workloads is recommended. |
| data platform architecture diagram: ICPDI 2023 Md Rabiul Islam, Rongjuan Chen, Jing Ma, 2023-11-21 The 2nd International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2023) was successfully held on 1-3 September 2023 in Chongqing, China. This conference aimed to bring together researchers, scholars, and practitioners from various fields to exchange ideas and discuss advancements in the areas of public management, digital economy, and internet technology. The conference featured a diverse range of research topics, including but not limited to Public Management, Digital Economy and Internet Technology. The conference fostered a rich and stimulating intellectual environment. The program included keynote speeches by renowned experts in the field, parallel sessions for paper presentations, and panel discussions addressing emerging trends and challenges. The conference proceedings showcased a wide array of research papers, providing valuable insights into the latest theoretical and practical developments in the field of public management, digital economy, and internet technology. Participants had the opportunity to engage in constructive discussions, offer feedback, and establish potential collaborations for future research endeavors. We extend our gratitude to all participants, presenters, organizers, and sponsors for their contributions in making this conference a resounding success. We look forward to the 3rd edition of this conference, where we can further explore the dynamic intersections of public management, digital economy, and internet technology. |
| data platform architecture diagram: TOGAF® Version 9.1 The Open Group, 2011-01-01 For trainers free additional material of this book is available. This can be found under the Training Material tab. Log in with your trainer account to access the material. TOGAF is a framework - a detailed method and a set of supporting tools - for developing an enterprise architecture, developed by members of The Open Group Architecture Forum. TOGAF Version 9.1 is a maintenance update to TOGAF 9, addressing comments raised since the introduction of TOGAF 9 in 2009. It retains the major features and structure of TOGAF 9, thereby preserving existing investment in TOGAF, and adds further detail and clarification to what is already proven.It may be used freely by any organization wishing to develop an enterprise architecture for use within that organization (subject to the Conditions of Use). This Book is divided into seven parts: Part I - Introduction This part provides a high-level introduction to the key concepts of enterprise architecture and in particular the TOGAF approach. It contains the definitions of terms used throughout TOGAF and release notes detailing the changes between this version and the previous version of TOGAF. Part II - Architecture Development Method This is the core of TOGAF. It describes the TOGAF Architecture Development Method (ADM) a step-by-step approach to developing an enterprise architecture. Part III - ADM Guidelines & Techniques This part contains a collection of guidelines and techniques available for use in applying TOGAF and the TOGAF ADM. Part IV - Architecture Content Framework This part describes the TOGAF content framework, including a structured metamodel for architectural artifacts, the use of re-usable architecture building blocks, and an overview of typical architecture deliverables. Part V - Enterprise Continuum & Tools This part discusses appropriate taxonomies and tools to categorize and store the outputs of architecture activity within an enterprise. Part VI - TOGAF Reference Models This part provides a selection of architectural reference models, which includes the TOGAF Foundation Architecture, and the Integrated Information Infrastructure Reference Model (III-RM). Part VII Architecture Capability Framework This section looks at roles, Governance, compliance skills and much more practical guidance |
| data platform architecture diagram: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| data platform architecture diagram: Rise of the Data Cloud Frank Slootman, Steve Hamm, 2020-12-18 The rise of the Data Cloud is ushering in a new era of computing. The world’s digital data is mass migrating to the cloud, where it can be more effectively integrated, managed, and mobilized. The data cloud eliminates data siloes and enables data sharing with business partners, capitalizing on data network effects. It democratizes data analytics, making the most sophisticated data science tools accessible to organizations of all sizes. Data exchanges enable businesses to discover, explore, and easily purchase or sell data—opening up new revenue streams. Business leaders have long dreamed of data driving their organizations. Now, thanks to the Data Cloud, nothing stands in their way. |
| data platform architecture diagram: Foundations for Architecting Data Solutions Ted Malaska, Jonathan Seidman, 2018-08-29 While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect |
| data platform architecture diagram: SOA Source Book The Open Group, 2020-06-11 Software services are established as a programming concept, but their impact on the overall architecture of enterprise IT and business operations is not well-understood. This has led to problems in deploying SOA, and some disillusionment. The SOA Source Book adds to this a collection of reference material for SOA. It is an invaluable resource for enterprise architects working with SOA.The SOA Source Book will help enterprise architects to use SOA effectively. It explains: What SOA is How to evaluate SOA features in business terms How to model SOA How to use The Open Group Architecture Framework (TOGAF ) for SOA SOA governance This book explains how TOGAF can help to make an Enterprise Architecture. Enterprise Architecture is an approach that can help management to understand this growing complexity. |
| data platform architecture diagram: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
| data platform architecture diagram: Software Architecture: The Hard Parts Neal Ford, Mark Richards, Pramod Sadalage, Zhamak Dehghani, 2021-09-23 There are no easy decisions in software architecture. Instead, there are many hard parts--difficult problems or issues with no best practices--that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures. Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture. By interweaving a story about a fictional group of technology professionals--the Sysops Squad--they examine everything from how to determine service granularity, manage workflows and orchestration, manage and decouple contracts, and manage distributed transactions to how to optimize operational characteristics, such as scalability, elasticity, and performance. By focusing on commonly asked questions, this book provides techniques to help you discover and weigh the trade-offs as you confront the issues you face as an architect. Analyze trade-offs and effectively document your decisions Make better decisions regarding service granularity Understand the complexities of breaking apart monolithic applications Manage and decouple contracts between services Handle data in a highly distributed architecture Learn patterns to manage workflow and transactions when breaking apart applications |
| data platform architecture diagram: Architecting Modern Data Platforms Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George, 2018-12-05 There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability |
| data platform architecture diagram: Microsoft Power Platform Enterprise Architecture Robert Rybaric, 2020-09-25 Publisher's Note: This edition from 2020 is outdated and is not compatible with the new standards of Microsoft Power Platform. A new Second edition has been published to cover the latest patterns, models, and methodologies leveraging the Microsoft ecosystem to create tailor-made enterprise applications. It combines the powers of Power Apps, Power BI, Azure, and Dynamics 365 to create enterprise applications. Who this book is for This book is for enterprise architects and technical decision makers who want to craft complex solutions using Microsoft Power Platform to serve growing business needs and to stay competitive in the modern IT world. A basic understanding of Microsoft Power Platform will help you to get started with this book. |
| data platform architecture diagram: ArchiMate® 3.0.1 Specification The Open Group, 2017-09-28 The ArchiMate® Specification, an Open Group Standard, defines an open and independent modeling language for Enterprise Architecture that is supported by different tool vendors and consulting firms. The ArchiMate language enables Enterprise Architects to describe, analyze, and visualize the relationships among business domains in an unambiguous way. This book is the official specification of the ArchiMate 3.0.1 modeling language from The Open Group. ArchiMate 3.0.1 is a minor update to ArchiMate 3.0, containing the set of corrections from ArchiMate 3.0 Technical Corrigendum No. 1 (U172). This addresses inconsistencies and errors identified since the publication of Version 3.0 in June 2016. The ArchiMate Specification supports modeling throughout the TOGAF® Architecture Development Method (ADM). New features in Version 3 include elements for modeling the enterprise at a strategic level, such as capability, resource, and outcome. It also includes support to model the physical world of materials and equipment. Furthermore, the consistency and structure of the language have been improved, definitions have been aligned with other standards, and its usability has been enhanced in various other ways. The intended audience is threefold: • Enterprise Architecture practitioners, such as architects (e.g., business, application, information, process, infrastructure, and, obviously, enterprise architects), senior and operational management, project leaders, and anyone committed to work within the reference framework defined by the Enterprise Architecture. • Those who intend to implement the ArchiMate language in a software tool; they will find a complete and detailed description of the language in this book. • The academic community, on which we rely for amending and improving the language, based on state-of-the-art research results in the Enterprise Architecture field. |
| data platform architecture diagram: Building Evolutionary Architectures Neal Ford, Rebecca Parsons, Patrick Kua, 2017-09-18 The software development ecosystem is constantly changing, providing a constant stream of new tools, frameworks, techniques, and paradigms. Over the past few years, incremental developments in core engineering practices for software development have created the foundations for rethinking how architecture changes over time, along with ways to protect important architectural characteristics as it evolves. This practical guide ties those parts together with a new way to think about architecture and time. |
| data platform architecture diagram: Patterns of Enterprise Application Architecture Martin Fowler, 2012-03-09 The practice of enterprise application development has benefited from the emergence of many new enabling technologies. Multi-tiered object-oriented platforms, such as Java and .NET, have become commonplace. These new tools and technologies are capable of building powerful applications, but they are not easily implemented. Common failures in enterprise applications often occur because their developers do not understand the architectural lessons that experienced object developers have learned. Patterns of Enterprise Application Architecture is written in direct response to the stiff challenges that face enterprise application developers. The author, noted object-oriented designer Martin Fowler, noticed that despite changes in technology--from Smalltalk to CORBA to Java to .NET--the same basic design ideas can be adapted and applied to solve common problems. With the help of an expert group of contributors, Martin distills over forty recurring solutions into patterns. The result is an indispensable handbook of solutions that are applicable to any enterprise application platform. This book is actually two books in one. The first section is a short tutorial on developing enterprise applications, which you can read from start to finish to understand the scope of the book's lessons. The next section, the bulk of the book, is a detailed reference to the patterns themselves. Each pattern provides usage and implementation information, as well as detailed code examples in Java or C#. The entire book is also richly illustrated with UML diagrams to further explain the concepts. Armed with this book, you will have the knowledge necessary to make important architectural decisions about building an enterprise application and the proven patterns for use when building them. The topics covered include · Dividing an enterprise application into layers · The major approaches to organizing business logic · An in-depth treatment of mapping between objects and relational databases · Using Model-View-Controller to organize a Web presentation · Handling concurrency for data that spans multiple transactions · Designing distributed object interfaces |
| data platform architecture diagram: Remote Mob Programming Jochen Christ, Martin Huber, 2020-03-17 Remote Mob Programming combines two ways of working: Mob Programming and working as a distributed team. Jochen Christ, Simon Harrer and Martin Huber share their experience with their working model - and why they don't want to work differently anymore. |
| data platform architecture diagram: Microservices Patterns Chris Richardson, 2018-10-27 A comprehensive overview of the challenges teams face when moving to microservices, with industry-tested solutions to these problems. - Tim Moore, Lightbend 44 reusable patterns to develop and deploy reliable production-quality microservices-based applications, with worked examples in Java Key Features 44 design patterns for building and deploying microservices applications Drawing on decades of unique experience from author and microservice architecture pioneer Chris Richardson A pragmatic approach to the benefits and the drawbacks of microservices architecture Solve service decomposition, transaction management, and inter-service communication Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Microservices Patterns teaches you 44 reusable patterns to reliably develop and deploy production-quality microservices-based applications. This invaluable set of design patterns builds on decades of distributed system experience, adding new patterns for composing services into systems that scale and perform under real-world conditions. More than just a patterns catalog, this practical guide with worked examples offers industry-tested advice to help you design, implement, test, and deploy your microservices-based application. What You Will Learn How (and why!) to use microservices architecture Service decomposition strategies Transaction management and querying patterns Effective testing strategies Deployment patterns This Book Is Written For Written for enterprise developers familiar with standard enterprise application architecture. Examples are in Java. About The Author Chris Richardson is a Java Champion, a JavaOne rock star, author of Manning’s POJOs in Action, and creator of the original CloudFoundry.com. Table of Contents Escaping monolithic hell Decomposition strategies Interprocess communication in a microservice architecture Managing transactions with sagas Designing business logic in a microservice architecture Developing business logic with event sourcing Implementing queries in a microservice architecture External API patterns Testing microservices: part 1 Testing microservices: part 2 Developing production-ready services Deploying microservices Refactoring to microservices |
| data platform architecture diagram: SAP Data Intelligence Dharma Teja Atluri, Devraj Bardhan, Santanu Ghosh, Snehasish Ghosh, Arindom Saha, 2021 Manage your data landscape with SAP Data Intelligence! Begin by understanding its architecture and capabilities and then see how to set up and install SAP Data Intelligence with step-by-step instructions. Walk through SAP Data Intelligence applications and learn how to use them for data governance, orchestration, and machine learning. Integrate with ABAP-based systems, SAP Vora, SAP Analytics Cloud, and more. Manage, secure, and operate SAP Data Intelligence with this all-in-one guide!In this book, you'll learn about:a. Configuration Build your SAP Data Intelligence landscape! Use SAP Cloud Appliance Library for cloud deployment, including provisioning, sizing, and accessing the launchpad. Perform on-premise installations using tools like the maintenance planner. b. Capabilities Put the core capabilities of SAP Data Intelligence to work! Manage and govern your data with the metadata explorer, use the modeler application to create data processing pipelines, create apps with the Jupyter Notebook, and more. c. Integration and Administration Integrate, manage, and operate SAP Data Intelligence! Get step-by-step instructions for integration with SAP and non-SAP systems. Learn about key administration tasks and make sure your landscape is secure and running smoothly. Highlights include:1) Configuration and installation2) Data governance3) Data processing pipelines4) Docker images5) ML Scenario Manager6) Jupyter Notebook7) Python SDK8) Integration9) Administration10) Security11) Application lifecycle management12) Use cases |
| data platform architecture diagram: Big Data Application Architecture Q&A Nitin Sawant, Himanshu Shah, 2014-01-24 Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'. The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real–time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your next big data application. The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution. |
| data platform architecture diagram: Hybrid Cloud Infrastructure and Operations Explained Mansura Habiba, Mihai Criveti, 2022-08-29 Modernize and migrate smoothly to hybrid cloud infrastructure and successfully mitigate complexities relating to the infrastructure, platform, and production environment Key FeaturesPresents problems and solutions for application modernization based on real-life use casesHelps design and implement efficient, highly available, and scalable cloud-native applicationsTeaches you how to adopt a cloud-native culture for successful deployments on hybrid cloud platformsBook Description Most organizations are now either moving to the cloud through modernization or building their apps in the cloud. Hybrid cloud is one of the best approaches for cloud migration and the modernization journey for any enterprise. This is why, along with coding skills, developers need to know the big picture of cloud footprint and be aware of the integration models between apps in a hybrid and multi-cloud infrastructure. This book represents an overview of your end-to-end journey to the cloud. To be future agnostic, the journey starts with a hybrid cloud. You'll gain an overall understanding of how to approach migration to the cloud using hybrid cloud technologies from IBM and Red Hat. Next, you'll be able to explore the challenges, requirements (both functional and non-functional), and the process of app modernization for enterprises by analyzing various use cases. The book then provides you with insights into the different reference solutions for app modernization on the cloud, which will help you to learn how to design and implement patterns and best practices in your job. By the end of this book, you'll be able to successfully modernize applications and cloud infrastructure in hyperscaler public clouds such as IBM and hybrid clouds using Red Hat technologies as well as develop secure applications for cloud environments. What you will learnStrategize application modernization, from the planning to the implementation phaseApply cloud-native development concepts, methods, and best practicesSelect the right strategy for cloud adoption and modernizationExplore container platforms, storage, network, security, and operationsManage cloud operations using SREs, FinOps, and MLOps principlesDesign a modern data insight hub on the cloudWho this book is for This book is for cloud-native application developers involved in modernizing legacy applications by refactoring and rebuilding them. Cloud solution architects and technical leaders will also find this book useful. It will be helpful to have a basic understanding of cloud-native application development and cloud providers before getting started with this book. |
| data platform architecture diagram: Modern Data Architecture on AWS Behram Irani, 2023-08-31 Discover all the essential design and architectural patterns in one place to help you rapidly build and deploy your modern data platform using AWS services Key Features Learn to build modern data platforms on AWS using data lakes and purpose-built data services Uncover methods of applying security and governance across your data platform built on AWS Find out how to operationalize and optimize your data platform on AWS Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.What you will learn Familiarize yourself with the building blocks of modern data architecture on AWS Discover how to create an end-to-end data platform on AWS Design data architectures for your own use cases using AWS services Ingest data from disparate sources into target data stores on AWS Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services Find out how to implement data governance using AWS services Who this book is for This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud. |
| data platform architecture diagram: Advances in Computer Science for Engineering and Education III Zhengbing Hu, Sergey Petoukhov, Ivan Dychka, Matthew He, 2020-08-05 This book comprises high-quality refereed research papers presented at the Third International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2020), held in Kyiv, Ukraine, on 21–22 January 2020, organized jointly by National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, National Aviation University, and the International Research Association of Modern Education and Computer Science. The topics discussed in the book include state-of-the-art papers in computer science, artificial intelligence, engineering techniques, genetic coding systems, deep learning with its medical applications, and knowledge representation with its applications in education. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in computer science and their applications in engineering and education. |
| data platform architecture diagram: Customer Data Platforms Martin Kihn, Christopher B. O'Hara, 2020-11-05 Master the hottest technology around to drive marketing success Marketers are faced with a stark and challenging dilemma: customers demand deep personalization, but they are increasingly leery of offering the type of personal data required to make it happen. As a solution to this problem, Customer Data Platforms have come to the fore, offering companies a way to capture, unify, activate, and analyze customer data. CDPs are the hottest marketing technology around today, but are they worthy of the hype? Customer Data Platforms takes a deep dive into everything CDP so you can learn how to steer your firm toward the future of personalization. Over the years, many of us have built byzantine “stacks” of various marketing and advertising technology in an attempt to deliver the fabled “right person, right message, right time” experience. This can lead to siloed systems, disconnected processes, and legacy technical debt. CDPs offer a way to simplify the stack and deliver a balanced and engaging customer experience. Customer Data Platforms breaks down the fundamentals, including how to: Understand the problems of managing customer data Understand what CDPs are and what they do (and don't do) Organize and harmonize customer data for use in marketing Build a safe, compliant first-party data asset that your brand can use as fuel Create a data-driven culture that puts customers at the center of everything you do Understand how to use AI and machine learning to drive the future of personalization Orchestrate modern customer journeys that react to customers in real-time Power analytics with customer data to get closer to true attribution In this book, you’ll discover how to build 1:1 engagement that scales at the speed of today’s customers. |
| data platform architecture diagram: Data Science Xiaohui Cheng, Weipeng Jing, Xianhua Song, Zeguang Lu, 2019-09-13 This two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application. |
| data platform architecture diagram: Data Products and the Data Mesh Alberto Artasanchez, Data Products and the Data Mesh is a comprehensive guide that explores the emerging paradigm of the data mesh and its implications for organizations navigating the data-driven landscape. This book equips readers with the knowledge and insights needed to design, build, and manage effective data products within the data mesh framework. The book starts by introducing the core concepts and principles of the data mesh, highlighting the shift from centralized data architectures to decentralized, domain-oriented approaches. It delves into the key components of the data mesh, including federated data governance, data marketplaces, data virtualization, and adaptive data products. Each chapter provides in-depth analysis, practical strategies, and real-world examples to illustrate the application of these concepts. Readers will gain a deep understanding of how the data mesh fosters a culture of data ownership, collaboration, and innovation. They will explore the role of modern data architectures, such as data marketplaces, in facilitating decentralized data sharing, access, and monetization. The book also delves into the significance of emerging technologies like blockchain, AI, and machine learning in enhancing data integrity, security, and value creation. Throughout the book, readers will discover practical insights and best practices to overcome challenges related to data governance, scalability, privacy, and compliance. They will learn how to optimize data workflows, leverage domain-driven design principles, and harness the power of data virtualization to drive meaningful insights and create impactful data products. Data Products and the Data Mesh is an essential resource for data professionals, architects, and leaders seeking to navigate the complex world of data products within the data mesh paradigm. It provides a comprehensive roadmap for building a scalable, decentralized, and innovative data ecosystem that empowers organizations to unlock the full potential of their data assets and drive data-driven success. |
| data platform architecture diagram: Information Systems Transformation William M. Ulrich, Philip Newcomb, 2010-02-04 Every major enterprise has a significant installed base of existing software systems that reflect the tangled IT architectures that result from decades of patches and failed replacements. Most of these systems were designed to support business architectures that have changed dramatically. At best, these systems hinder agility and competitiveness and, at worst, can bring critical business functions to a halt. Architecture-Driven Modernization (ADM) restores the value of entrenched systems by capturing and retooling various aspects of existing application environments, allowing old infrastructures to deliver renewed value and align effectively with enterprise strategies and business architectures. Information Systems Transformation provides a practical guide to organizations seeking ways to understand and leverage existing systems as part of their information management strategies. It includes an introduction to ADM disciplines, tools, and standards as well as a series of scenarios outlining how ADM is applied to various initiatives. Drawing upon lessons learned from real modernization projects, it distills the theory and explains principles, processes, and best practices for every industry. Acts as a one-stop shopping reference and complete guide for implementing various modernization models in myriad industries and departments Every concept is illustrated with real-life examples from various modernization projects, allowing you to immediately apply tested solutions and see results Authored by the Co-chair of the Object Management Group (OMG) Architecture-Driven Modernization (ADM) Task Force, which sets definitive systems modernization standards for the entire IT industry A web site supports the book with up to date coverage of evolving ADM Specifications, Tutorials, and Whitepapers, allowing you to remain up to date on modernization topics as they develop |
| data platform architecture diagram: NoSQL Distilled Pramod J. Sadalage, Martin Fowler, 2013 'NoSQL Distilled' is designed to provide you with enough background on how NoSQL databases work, so that you can choose the right data store without having to trawl the whole web to do it. It won't answer your questions definitively, but it should narrow down the range of options you have to consider. |
| data platform architecture diagram: The Enterprise Big Data Lake Alex Gorelik, 2019-02-21 The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries |
| data platform architecture diagram: Enterprise Integration Patterns Gregor Hohpe, Bobby Woolf, 2012-03-09 Enterprise Integration Patterns provides an invaluable catalog of sixty-five patterns, with real-world solutions that demonstrate the formidable of messaging and help you to design effective messaging solutions for your enterprise. The authors also include examples covering a variety of different integration technologies, such as JMS, MSMQ, TIBCO ActiveEnterprise, Microsoft BizTalk, SOAP, and XSL. A case study describing a bond trading system illustrates the patterns in practice, and the book offers a look at emerging standards, as well as insights into what the future of enterprise integration might hold. This book provides a consistent vocabulary and visual notation framework to describe large-scale integration solutions across many technologies. It also explores in detail the advantages and limitations of asynchronous messaging architectures. The authors present practical advice on designing code that connects an application to a messaging system, and provide extensive information to help you determine when to send a message, how to route it to the proper destination, and how to monitor the health of a messaging system. If you want to know how to manage, monitor, and maintain a messaging system once it is in use, get this book. |
| data platform architecture diagram: Architecting Data and Machine Learning Platforms Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner, 2023-10-12 All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach |
Modern Data Platform using AWS and Snowflake
Data is collected from multiple data sources across the enterprise, software as a service (SaaS) applications, edge devices, logs, streaming data, and social media networks.
Architectural Patterns to Build End-to-End Data Driven …
In this whitepaper, we present some commonly used data-driven applications and proven architectural patterns based on successful customer implementation. This enables customers …
Cloud Data Management Platform Architecture - Informatica
Modern cloud-native end to end data management architecture enables organizations to control business data, both in the cloud and in a combination of on-premises and cloud applications …
Modern Data Architecture Accelerator - Implementation Guide
The Modern Data Architecture Accelerator (MDAA) on AWS helps customers rapidly deploy and manage sophisticated data platform architectures on AWS. This solution provides a flexible …
Guidance for Collaborative, Unified Data and AI Development …
This architecture diagram shows how Amazon SageMaker provides a unified, collaborative experience for ML and data engineers, data stewards, and generative AI developers to …
Architecting for Cloud Data Warehouse and Data Lakes
Organizations are rapidly adopting cloud data warehouse and data lakes to extend their existing data architectures and expand their analytical capabilities to support advanced analytics and …
Cloud Data Platform Architecture Primer - GlobalLogic
The data platform architecture can be realized on different cloud platforms entirely natively or using hybrid clouds. In this paper, we will reference the implementation of various data …
Databricks Data Intelligence Platform
Data Intelligence Platform 3rd party Business Apps Sources Ingest Transform Query / Process Serve Analyse Integrate Data Analysis Spark / Photon Delta Live Tables Databricks SQL …
SAP Datasphere: The All-in-One Business Data Platform
Modeling on top of remote, local tables or views. Calculation, Join, and SQL engines. Multi-dimensional to answer business questions faster, and efficiently. OLAP Engine and MDS. …
Customer Data Platform for Airlines with Master Data …
Use this data platform architecture to relieve or replace your on-premises data platform load, increasing development agility and cost savings. This approach allows for the development of …
InterSystems IRIS Data Platform Architecture Guide
InterSystems IRIS data platform is often used for demanding, high-performance data intensive applications. Examples include: • Ingesting and analyzing 2.5M real-time events per second …
Data Architecture Series The Unified Data Fabric - Cloudera
As a result, the platform is perfectly placed to implement modern data architectures: A unified Data Fabric which centrally orchestrates disparate data sources intelligently and securely …
D4.1 Data Management Platform Architecture
DMP (Data Management Platform) - is a platform for managing data from multiple sources for storing, processing, analyzing and provisioning. In this project, the platform is built over Azure …
BUILDING A MODERN DATA PLATFORM - Qubole
Diagram 2 above shows a modern data platform architecture where a cloud data lake enhances the traditional data warehouse. Note the smaller footprint of a traditional data warehouse …
Big Data & Analytics Reference Architecture - Oracle
Oracle products are mapped to the architecture in order to illustrate how the architecture can be implemented and deployed. Organizations can use this reference architecture as a starting …
ARCHIVED: Modern Data Analytics Reference Architecture on …
Modern Data Analytics Reference Architecture on AWS This architectureenables customersto build data analytics pipelines using a Modern Data Analytics approachto derive insights from …
Databricks Data Intelligence Platform on AWS
Data Consumer Business/AI App ID Provider Enterprise Catalog BI ETL Orchestration External Orchestrator Amazon S3 Amazon Redshift Amazon Bedrock AWS DMS AWS IoT Core …
Customer Data Platform for Airlines - d1.awsstatic.com
Build data products for relevant domains (like flight, passenger, and loyalty), separating storage from compute. In the operational data store, use managed services and purpose-built …
Guidance for Customer Data Platform on AWS
This Guidance shows how to build a customer data platform with a full, 360 degree profile view of customer data. It explores each stage of building the platform, including data ingestion, identity …
Modern Data Platform using AWS and Snowflake
Data is collected from multiple data sources across the enterprise, software as a service (SaaS) applications, edge devices, logs, streaming data, and social media networks.
Modern Data Analytics Reference Architecture on AWS
May 31, 2022 · Modern Data Analytics Reference Architecture on AWS 1. Data is collected from multiple data sources across the enterprise, SaaS applications, edge devices, logs, streaming …
Architectural Patterns to Build End-to-End Data Driven …
In this whitepaper, we present some commonly used data-driven applications and proven architectural patterns based on successful customer implementation. This enables customers …
Cloud Data Management Platform Architecture - Informatica
Modern cloud-native end to end data management architecture enables organizations to control business data, both in the cloud and in a combination of on-premises and cloud applications …
Modern Data Architecture Accelerator - Implementation Guide
The Modern Data Architecture Accelerator (MDAA) on AWS helps customers rapidly deploy and manage sophisticated data platform architectures on AWS. This solution provides a flexible …
Guidance for Collaborative, Unified Data and AI Development …
This architecture diagram shows how Amazon SageMaker provides a unified, collaborative experience for ML and data engineers, data stewards, and generative AI developers to …
Architecting for Cloud Data Warehouse and Data Lakes
Organizations are rapidly adopting cloud data warehouse and data lakes to extend their existing data architectures and expand their analytical capabilities to support advanced analytics and …
Cloud Data Platform Architecture Primer - GlobalLogic
The data platform architecture can be realized on different cloud platforms entirely natively or using hybrid clouds. In this paper, we will reference the implementation of various data …
Databricks Data Intelligence Platform
Data Intelligence Platform 3rd party Business Apps Sources Ingest Transform Query / Process Serve Analyse Integrate Data Analysis Spark / Photon Delta Live Tables Databricks SQL …
SAP Datasphere: The All-in-One Business Data Platform
Modeling on top of remote, local tables or views. Calculation, Join, and SQL engines. Multi-dimensional to answer business questions faster, and efficiently. OLAP Engine and MDS. …
Customer Data Platform for Airlines with Master Data …
Use this data platform architecture to relieve or replace your on-premises data platform load, increasing development agility and cost savings. This approach allows for the development of …
InterSystems IRIS Data Platform Architecture Guide
InterSystems IRIS data platform is often used for demanding, high-performance data intensive applications. Examples include: • Ingesting and analyzing 2.5M real-time events per second …
Data Architecture Series The Unified Data Fabric - Cloudera
As a result, the platform is perfectly placed to implement modern data architectures: A unified Data Fabric which centrally orchestrates disparate data sources intelligently and securely …
D4.1 Data Management Platform Architecture
DMP (Data Management Platform) - is a platform for managing data from multiple sources for storing, processing, analyzing and provisioning. In this project, the platform is built over Azure …
BUILDING A MODERN DATA PLATFORM - Qubole
Diagram 2 above shows a modern data platform architecture where a cloud data lake enhances the traditional data warehouse. Note the smaller footprint of a traditional data warehouse …
Big Data & Analytics Reference Architecture - Oracle
Oracle products are mapped to the architecture in order to illustrate how the architecture can be implemented and deployed. Organizations can use this reference architecture as a starting …
ARCHIVED: Modern Data Analytics Reference Architecture …
Modern Data Analytics Reference Architecture on AWS This architectureenables customersto build data analytics pipelines using a Modern Data Analytics approachto derive insights from …
Databricks Data Intelligence Platform on AWS
Data Consumer Business/AI App ID Provider Enterprise Catalog BI ETL Orchestration External Orchestrator Amazon S3 Amazon Redshift Amazon Bedrock AWS DMS AWS IoT Core …
Customer Data Platform for Airlines - d1.awsstatic.com
Build data products for relevant domains (like flight, passenger, and loyalty), separating storage from compute. In the operational data store, use managed services and purpose-built …
Guidance for Customer Data Platform on AWS
This Guidance shows how to build a customer data platform with a full, 360 degree profile view of customer data. It explores each stage of building the platform, including data ingestion, identity …
