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| data hub 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 hub 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 hub architecture diagram: Service Oriented Architecture For Dummies Judith S. Hurwitz, Robin Bloor, Carol Baroudi, Marcia Kaufman, 2006-10-25 SOA is the most important initiative facing IT and is difficult to grasp. This book demystifies the topic of SOA and makes it accessible to those people who hear the term but aren't really sure what it means. It covers what SOA is, why it matters, how it can impact businesses, and how to take steps to implement SOA in a corporate environment. |
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| data hub 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 hub 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 hub architecture diagram: Data Lake Development with Big Data Pradeep Pasupuleti, Beulah Salome Purra, 2015-11-26 Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies. What You Will Learn Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios Find out the key considerations to be taken into account while building each tier of the Data Lake Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies Enable data discovery on the Data Lake to allow users to discover the data Discover how data is packaged and provisioned for consumption Comprehend the importance of including data governance disciplines while building a Data Lake In Detail A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data. Style and approach Data Lake Development with Big Data provides architectural approaches to building a Data Lake. It follows a use case-based approach where practical implementation scenarios of each key component are explained. It also helps you understand how these use cases are implemented in a Data Lake. The chapters are organized in a way that mimics the sequential data flow evidenced in a Data Lake. |
| data hub architecture diagram: Enterprise Data Architecture: How to navigate its landscape Dave Knifton, 2014-10-16 Are you looking to make better use of data captured within your organisation or want to learn more about how Data Architecture can transform your operations? Answering these questions is at the very heart of Navigating the Data Architecture Landscape. By reading this book you will learn how to: Introduce or improve the Data Architecture function of your organisation Enhance your skills in this domain to deliver more from your data. You may be wondering how a book can do this if it knows nothing about where you are now, or where you want to be? It can, because by leveraging its principles you will discover how to create optimised potential routes to achieve your own Data Architectural objectives. Basic building blocks, concepts and models are defined, enabling you to create new or adapt existing frameworks appropriate for any data landscape. Practical tips and suggestions are also detailed throughout, helping you gain immediate improvements from the way you work and enhance the benefits your organisation can derive from its data. So if you are a Data Architect or deal with data in your organisation and want to learn how to transform the positive yield from its data, then this book is a must read for you! “David has been there and dealt with the issues, which is why this book is an outstanding resource for Data Architects and indeed anyone dealing with the serious challenges of an enterprise data landscape.” – Richard Rendell, Technical Services Director, AgeSmart “An essential read for anyone wishing to practically achieve more benefit from data for their organisation within today’s constraints.” – Reem Zahran - Director, Offering Development, IMS Health “This book provides a comprehensive set of tools enabling you to improve the business outcomes from your organisation’s use of data.” – Andrew Rowland, Global Head Database Engineering, UBS This book is an essential read for Data Architects or indeed anyone wanting to improve the benefit that their organisation can derive from its data usage. It does this by providing principles and models that are appropriate to use within any framework, or even the absence of one. The book is designed to be practical and contains many tips and suggestions as well as examples that can be used as the basis for the reader's own Data Architectural definitions. The breadth of the book covers contemporary themes for Data Architecture and the chapters include; Data Modelling, Enterprise Data Models, Data Governance, Master Data Management and Big Data |
| data hub architecture diagram: Master Data Management and Customer Data Integration for a Global Enterprise Alex Berson, Larry Dubov, 2007-05-22 Transform your business into a customer-centric enterprise Gain a complete and timely understanding of your customers using MDM-CDI and the real-world information contained in this comprehensive volume. Master Data Management and Customer Data Integration for a Global Enterprise explains how to grow revenue, reduce administrative costs, and improve client retention by adopting a customer-focused business framework. Learn to build and use customer hubs and associated technologies, secure and protect confidential corporate and customer information, provide personalized services, and set up an effective data governance team. You'll also get full details on regulatory compliance and the latest pre-packaged MDM-CDI software solutions. Design and implement a dynamic MDM-CDI architecture that fits the needs of your business Implement MDM-CDI holistically as an integrated multi-disciplinary set of technologies, services, and processes Improve solution agility and flexibility using SOA and Web services Recognize customers and their relationships with the enterprise across channels and lines of business Ensure compliance with local, state, federal, and international regulations Deploy network, perimeter, platform, application, data, and user-level security Protect against identity and data theft, worm infection, and phishing and pharming scams Create an Enterprise Information Governance Group Perform development, QA, and business acceptance testing and data verification |
| data hub architecture diagram: Linked Data and User Interaction H. Frank Cervone, Lars G. Svensson, 2015-07-01 This collection of research papers provides extensive information on deploying services, concepts, and approaches for using open linked data from libraries and other cultural heritage institutions. With a special emphasis on how libraries and other cultural heritage institutions can create effective end user interfaces using open, linked data or other datasets. These papers are essential reading for any one interesting in user interface design or the semantic web. |
| data hub architecture diagram: IBM Reference Architecture for Genomics, Power Systems Edition Dino Quintero, Luis Bolinches, Marcelo Correia Lima, Katarzyna Pasierb, William dos Santos, IBM Redbooks, 2016-04-05 This IBM® Redbooks® publication introduces the IBM Reference Architecture for Genomics, IBM Power SystemsTM edition on IBM POWER8®. It addresses topics such as why you would implement Life Sciences workloads on IBM POWER8, and shows how to use such solution to run Life Sciences workloads using IBM PlatformTM Computing software to help set up the workloads. It also provides technical content to introduce the IBM POWER8 clustered solution for Life Sciences workloads. This book customizes and tests Life Sciences workloads with a combination of an IBM Platform Computing software solution stack, Open Stack, and third party applications. All of these applications use IBM POWER8, and IBM Spectrum ScaleTM for a high performance file system. This book helps strengthen IBM Life Sciences solutions on IBM POWER8 with a well-defined and documented deployment model within an IBM Platform Computing and an IBM POWER8 clustered environment. This system provides clients in need of a modular, cost-effective, and robust solution with a planned foundation for future growth. This book highlights IBM POWER8 as a flexible infrastructure for clients looking to deploy life sciences workloads, and at the same time reduce capital expenditures, operational expenditures, and optimization of resources. This book helps answer clients' workload challenges in particular with Life Sciences applications, and provides expert-level documentation and how-to-skills to worldwide teams that provide Life Sciences solutions and support to give a broad understanding of a new architecture. |
| data hub architecture diagram: Team of Teams Gen. Stanley McChrystal, Tantum Collins, David Silverman, Chris Fussell, 2015-05-12 From the New York Times bestselling author of My Share of the Task and Leaders, a manual for leaders looking to make their teams more adaptable, agile, and unified in the midst of change. When General Stanley McChrystal took command of the Joint Special Operations Task Force in 2004, he quickly realized that conventional military tactics were failing. Al Qaeda in Iraq was a decentralized network that could move quickly, strike ruthlessly, then seemingly vanish into the local population. The allied forces had a huge advantage in numbers, equipment, and training—but none of that seemed to matter. To defeat Al Qaeda, they would have to combine the power of the world’s mightiest military with the agility of the world’s most fearsome terrorist network. They would have to become a team of teams—faster, flatter, and more flexible than ever. In Team of Teams, McChrystal and his colleagues show how the challenges they faced in Iraq can be relevant to countless businesses, nonprofits, and organizations today. In periods of unprecedented crisis, leaders need practical management practices that can scale to thousands of people—and fast. By giving small groups the freedom to experiment and share what they learn across the entire organization, teams can respond more quickly, communicate more freely, and make better and faster decisions. Drawing on compelling examples—from NASA to hospital emergency rooms—Team of Teams makes the case for merging the power of a large corporation with the agility of a small team to transform any organization. |
| data hub architecture diagram: Building a Scalable Data Warehouse with Data Vault 2.0 Daniel Linstedt, Michael Olschimke, 2015-09-15 The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. Building a Scalable Data Warehouse covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: - How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. - Important data warehouse technologies and practices. - Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. - Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast - Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse - Demystifies data vault modeling with beginning, intermediate, and advanced techniques - Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0 |
| data hub architecture diagram: Business Modeling and Software Design Boris Shishkov, 2020-07-06 This book constitutes the refereed proceedings of the 10th International Symposium on Business Modeling and Software Design, BMSD 2020, which took place in Berlin, Germany, in July 2020. BMSD is a leading international forum that brings together researchers and practitioners interested in business modeling and its relation to software design. Particular areas of interest are: Business Processes and Enterprise Engineering; Business Models and Requirements; Business Models and Services; Business Models and Software; Information Systems Architectures and Paradigms; Data Aspects in Business Modeling and Software Development; Blockchain-Based Business Models and Information Systems; IoT and Implications for Enterprise Information Systems. The theme of BMSD 2020 was: Towards Knowledge-Driven Enterprise Information Systems. |
| data hub architecture diagram: Fast Data Processing with Spark 2 Krishna Sankar, 2016-10-24 Learn how to use Spark to process big data at speed and scale for sharper analytics. Put the principles into practice for faster, slicker big data projects. About This Book A quick way to get started with Spark – and reap the rewards From analytics to engineering your big data architecture, we've got it covered Bring your Scala and Java knowledge – and put it to work on new and exciting problems Who This Book Is For This book is for developers with little to no knowledge of Spark, but with a background in Scala/Java programming. It's recommended that you have experience in dealing and working with big data and a strong interest in data science. What You Will Learn Install and set up Spark in your cluster Prototype distributed applications with Spark's interactive shell Perform data wrangling using the new DataFrame APIs Get to know the different ways to interact with Spark's distributed representation of data (RDDs) Query Spark with a SQL-like query syntax See how Spark works with big data Implement machine learning systems with highly scalable algorithms Use R, the popular statistical language, to work with Spark Apply interesting graph algorithms and graph processing with GraphX In Detail When people want a way to process big data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it's unsurprising that it's becoming popular with data analysts and engineers everywhere. Beginning with the fundamentals, we'll show you how to get set up with Spark with minimum fuss. You'll then get to grips with some simple APIs before investigating machine learning and graph processing – throughout we'll make sure you know exactly how to apply your knowledge. You will also learn how to use the Spark shell, how to load data before finding out how to build and run your own Spark applications. Discover how to manipulate your RDD and get stuck into a range of DataFrame APIs. As if that's not enough, you'll also learn some useful Machine Learning algorithms with the help of Spark MLlib and integrating Spark with R. We'll also make sure you're confident and prepared for graph processing, as you learn more about the GraphX API. Style and approach This book is a basic, step-by-step tutorial that will help you take advantage of all that Spark has to offer. |
| data hub 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 hub architecture diagram: Remote Sensing Handbook, Volume I Prasad S. Thenkabail, 2024-11-29 Volume I of the Six Volume Remote Sensing Handbook, Second Edition, is focused on satellites and sensors including radar, light detection and ranging (LiDAR), microwave, hyperspectral, unmanned aerial vehicles (UAVs), and their applications. It discusses data normalization and harmonization, accuracies, and uncertainties of remote sensing products, global navigation satellite system (GNSS) theory and practice, crowdsourcing, cloud computing environments, Google Earth Engine, and remote sensing and space law. This thoroughly revised and updated volume draws on the expertise of a diverse array of leading international authorities in remote sensing and provides an essential resource for researchers at all levels interested in using remote sensing. It integrates discussions of remote sensing principles, data, methods, development, applications, and scientific and social context. FEATURES Provides the most up-to-date comprehensive coverage of remote sensing science. Discusses and analyzes data from old and new generations of satellites and sensors. Provides comprehensive methods and approaches for remote sensing data normalization, standardization, and harmonization. Includes numerous case studies on advances and applications at local, regional, and global scales. Introduces advanced methods in remote sensing such as machine learning, cloud computing, and AI. Highlights scientific achievements over the last decade and provides guidance for future developments. This volume is an excellent resource for the entire remote sensing and GIS community. Academics, researchers, undergraduate and graduate students, as well as practitioners, decision-makers, and policymakers, will benefit from the expertise of the professionals featured in this book, and their extensive knowledge of new and emerging trends. |
| data hub architecture diagram: Beyond Big Data Martin Oberhofer, Eberhard Hechler, Ivan Milman, Scott Schumacher, Dan Wolfson, 2015 Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult--often, because it's so difficult to integrate new and legacy data sources. In Beyond Big Data, five of IBM's leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM's enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels. Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends |
| data hub architecture diagram: Scalable Data Architecture with Java Sinchan Banerjee, 2022-09-30 Orchestrate data architecting solutions using Java and related technologies to evaluate, recommend and present the most suitable solution to leadership and clients Key FeaturesLearn how to adapt to the ever-evolving data architecture technology landscapeUnderstand how to choose the best suited technology, platform, and architecture to realize effective business valueImplement effective data security and governance principlesBook Description Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data. This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert. You'll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you'll understand how to architect a batch and real-time data processing pipeline. You'll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you'll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics. By the end of this book, you'll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients. What you will learnAnalyze and use the best data architecture patterns for problemsUnderstand when and how to choose Java tools for a data architectureBuild batch and real-time data engineering solutions using JavaDiscover how to apply security and governance to a solutionMeasure performance, publish benchmarks, and optimize solutionsEvaluate, choose, and present the best architectural alternativesUnderstand how to publish Data as a Service using GraphQL and a REST APIWho this book is for Data architects, aspiring data architects, Java developers and anyone who wants to develop or optimize scalable data architecture solutions using Java will find this book useful. A basic understanding of data architecture and Java programming is required to get the best from this book. |
| data hub architecture diagram: Corporate Information Factory W. H. Inmon, Claudia Imhoff, Ryan Sousa, 2002-03-14 The father of data warehousing incorporates the latesttechnologies into his blueprint for integrated decision supportsystems Today's corporate IT and data warehouse managers are required tomake a small army of technologies work together to ensure fast andaccurate information for business managers. Bill Inmon created theCorporate Information Factory to solve the needs ofthese managers. Since the First Edition, the design of the factoryhas grown and changed dramatically. This Second Edition, revisedand expanded by 40% with five new chapters, incorporates thesechanges. This step-by-step guide will enable readers to connecttheir legacy systems with the data warehouse and deal with a hostof new and changing technologies, including Web access mechanisms,e-commerce systems, ERP (Enterprise Resource Planning) systems. Thebook also looks closely at exploration and data mining servers foranalyzing customer behavior and departmental data marts forfinance, sales, and marketing. |
| data hub 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 |
| data hub architecture diagram: Azure Data Engineer Associate Certification Guide Newton Alex, 2022-02-28 Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certification Key Features Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification exam Explore the various Azure services for building end-to-end data solutions Gain a solid understanding of building secure and sustainable data solutions using Azure services Book DescriptionAzure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What you will learn Gain intermediate-level knowledge of Azure the data infrastructure Design and implement data lake solutions with batch and stream pipelines Identify the partition strategies available in Azure storage technologies Implement different table geometries in Azure Synapse Analytics Use the transformations available in T-SQL, Spark, and Azure Data Factory Use Azure Databricks or Synapse Spark to process data using Notebooks Design security using RBAC, ACL, encryption, data masking, and more Monitor and optimize data pipelines with debugging tips Who this book is for This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack. The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book. |
| data hub architecture diagram: HoloLens Blueprints Abhijit Jana, Manish Sharma, Mallikarjuna Rao, 2017-06-20 Unveil the world of mixed reality with HoloLens About This Book Bring holographic insights to existing line-of-business applications, tools, and workflows Focus on developing end-to-end realistic holographic application. Build interactive model scripts and test them in Unity3D and holographic emulators Who This Book Is For This book is targeted at developers and designers working on mixed-reality developments for complex integrated scenarios using HoloLens. What You Will Learn Interact with holograms using different interaction models Develop your first holographic app Integrate holographic applications with cloud systems Visualize data feeds coming from the cloud through holograms Manage the application distribution of enterprise-enabled HoloLens Integrate HoloLens applications with services deployed on Azure Identify and create 3D Assets and Scenes Use HoloLens to explore the Internet of Things In Detail Do you want to create stunning applications with HoloLens? Are you a developer who is fascinated with Microsoft HoloLens and its capabilities? If so, this is the book for you. This book introduces and demystifies the HoloLens platform and shows you different ways of interaction with computers (mixed-reality). You will start your mixed-reality journey by understanding different types of digital reality. You will learn to build your first holographic app. Also, you will understand holographic application integration possibilities within Line of Business Applications using Azure. Moving ahead, you will create Integrated Solutions using IoT with HoloLens. Gradually you'll learn how to create and deploy apps on a device. You will learn to publish application to the store; if you are an enterprise developer, you will also manage and distribute applications for enterprise-enabled or domain-joined HoloLens. Finally, you will develop an end-to-end realistic holographic app, ranging from scenario identification to sketching, development, deployment, and, finally, production. Style and approach The book is a project-based guide to help you to create some really astonishing mixed-reality applications. It will provide end-to-end solutions and enable you to build stunning applications for HoloLens. |
| data hub architecture diagram: Security Architecture for Hybrid Cloud Mark Buckwell, Stefaan Van daele, Carsten Horst, 2024-07-25 As the transformation to hybrid multicloud accelerates, businesses require a structured approach to securing their workloads. Adopting zero trust principles demands a systematic set of practices to deliver secure solutions. Regulated businesses, in particular, demand rigor in the architectural process to ensure the effectiveness of security controls and continued protection. This book provides the first comprehensive method for hybrid multicloud security, integrating proven architectural techniques to deliver a comprehensive end-to-end security method with compliance, threat modeling, and zero trust practices. This method ensures repeatability and consistency in the development of secure solution architectures. Architects will learn how to effectively identify threats and implement countermeasures through a combination of techniques, work products, and a demonstrative case study to reinforce learning. You'll examine: The importance of developing a solution architecture that integrates security for clear communication Roles that security architects perform and how the techniques relate to nonsecurity subject matter experts How security solution architecture is related to design thinking, enterprise security architecture, and engineering How architects can integrate security into a solution architecture for applications and infrastructure using a consistent end-to-end set of practices How to apply architectural thinking to the development of new security solutions About the authors Mark Buckwell is a cloud security architect at IBM with 30 years of information security experience. Carsten Horst with more than 20 years of experience in Cybersecurity is a certified security architect and Associate Partner at IBM. Stefaan Van daele has 25 years experience in Cybersecurity and is a Level 3 certified security architect at IBM. |
| data hub architecture diagram: Data Warehousing Fundamentals Paulraj Ponniah, 2004-04-07 Geared to IT professionals eager to get into the all-importantfield of data warehousing, this book explores all topics needed bythose who design and implement data warehouses. Readers will learnabout planning requirements, architecture, infrastructure, datapreparation, information delivery, implementation, and maintenance.They'll also find a wealth of industry examples garnered from theauthor's 25 years of experience in designing and implementingdatabases and data warehouse applications for majorcorporations. Market: IT Professionals, Consultants. |
| data hub architecture diagram: Designing and Operating a Data Reservoir Mandy Chessell, Nigel L Jones, Jay Limburn, David Radley, Kevin Shank, IBM Redbooks, 2015-05-26 Together, big data and analytics have tremendous potential to improve the way we use precious resources, to provide more personalized services, and to protect ourselves from unexpected and ill-intentioned activities. To fully use big data and analytics, an organization needs a system of insight. This is an ecosystem where individuals can locate and access data, and build visualizations and new analytical models that can be deployed into the IT systems to improve the operations of the organization. The data that is most valuable for analytics is also valuable in its own right and typically contains personal and private information about key people in the organization such as customers, employees, and suppliers. Although universal access to data is desirable, safeguards are necessary to protect people's privacy, prevent data leakage, and detect suspicious activity. The data reservoir is a reference architecture that balances the desire for easy access to data with information governance and security. The data reservoir reference architecture describes the technical capabilities necessary for a system of insight, while being independent of specific technologies. Being technology independent is important, because most organizations already have investments in data platforms that they want to incorporate in their solution. In addition, technology is continually improving, and the choice of technology is often dictated by the volume, variety, and velocity of the data being managed. A system of insight needs more than technology to succeed. The data reservoir reference architecture includes description of governance and management processes and definitions to ensure the human and business systems around the technology support a collaborative, self-service, and safe environment for data use. The data reservoir reference architecture was first introduced in Governing and Managing Big Data for Analytics and Decision Makers, REDP-5120, which is available at: http://www.redbooks.ibm.com/redpieces/abstracts/redp5120.html. This IBM® Redbooks publication, Designing and Operating a Data Reservoir, builds on that material to provide more detail on the capabilities and internal workings of a data reservoir. |
| data hub 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 hub architecture diagram: Designing and Building Solid Microservice Ecosystems Guillermo Leo Wrba, 2023-05-12 It's not new to us that microservices are changing the way we conceive digital transformation, as organizations embrace digital transformation. Every day, more and more companies are betting on microservice adoption, and there is a strong reason for this: business needs to evolve and change at a fast pace, in order to adapt itself to satisfy a demanding 2.0 digital customer's experience in terms of overall service quality. Ensuring that such a change occurs seamlessly and progressively is one of the goals for microservices, and designing and building a solid microservice architecture is the way to guarantee that this happens from inception, by observing principles, best practices, design patterns, and reference models. This book provides a comprehensive walkthrough across the different concepts, frameworks, methodologies, and architecture building blocks that make up a microservice ecosystem and constitute a reference architecture from which you can get to multiple sub-architectures and implementations. Being an architect, you'll learn how to better design microservice-led and event-centric architectures in the right way from the early beginning, by showcasing learned lessons, best-practices do's, and don'ts. If you are starting your architecture career, it's the right place to get introduced to concepts and methodologies that you will then grow over time, as you acquire more experience. If you are a developer, but willing to jump into the exciting architecture world, this can also be good reading, however, be warned that some basic architectural understandings and concepts need to be first incorporated before walking through the advanced concepts presented throughout this book. This book requires you to have some minimal background around Docker and Microservices to better understand the more advanced concepts that are being explained. |
| data hub 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 hub architecture diagram: Hadoop Application Architectures Mark Grover, Ted Malaska, Jonathan Seidman, Gwen Shapira, 2015-06-30 Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case. To reinforce those lessons, the book’s second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications. Whether you’re designing a new Hadoop application, or planning to integrate Hadoop into your existing data infrastructure, Hadoop Application Architectures will skillfully guide you through the process. This book covers: Factors to consider when using Hadoop to store and model data Best practices for moving data in and out of the system Data processing frameworks, including MapReduce, Spark, and Hive Common Hadoop processing patterns, such as removing duplicate records and using windowing analytics Giraph, GraphX, and other tools for large graph processing on Hadoop Using workflow orchestration and scheduling tools such as Apache Oozie Near-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache Flume Architecture examples for clickstream analysis, fraud detection, and data warehousing |
| data hub architecture diagram: Data Architecture William H. Inmon, Daniel Linstedt, 2015 |
| data hub architecture diagram: Dear Data Giorgia Lupi, Stefanie Posavec, 2016-09-13 Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates the infinitesimal, incomplete, imperfect, yet exquisitely human details of life, in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere. |
| data hub architecture diagram: Enterprise Application Architecture with .NET Core Ganesan Senthilvel, Ovais Mehboob Ahmed Khan, Habib Ahmed Qureshi, 2017-04-25 Architect and design highly scalable, robust, clean and highly performant applications in .NET Core About This Book Incorporate architectural soft-skills such as DevOps and Agile methodologies to enhance program-level objectives Gain knowledge of architectural approaches on the likes of SOA architecture and microservices to provide traceability and rationale for architectural decisions Explore a variety of practical use cases and code examples to implement the tools and techniques described in the book Who This Book Is For This book is for experienced .NET developers who are aspiring to become architects of enterprise-grade applications, as well as software architects who would like to leverage .NET to create effective blueprints of applications. What You Will Learn Grasp the important aspects and best practices of application lifecycle management Leverage the popular ALM tools, application insights, and their usage to monitor performance, testability, and optimization tools in an enterprise Explore various authentication models such as social media-based authentication, 2FA and OpenID Connect, learn authorization techniques Explore Azure with various solution approaches for Microservices and Serverless architecture along with Docker containers Gain knowledge about the recent market trends and practices and how they can be achieved with .NET Core and Microsoft tools and technologies In Detail If you want to design and develop enterprise applications using .NET Core as the development framework and learn about industry-wide best practices and guidelines, then this book is for you. The book starts with a brief introduction to enterprise architecture, which will help you to understand what enterprise architecture is and what the key components are. It will then teach you about the types of patterns and the principles of software development, and explain the various aspects of distributed computing to keep your applications effective and scalable. These chapters act as a catalyst to start the practical implementation, and design and develop applications using different architectural approaches, such as layered architecture, service oriented architecture, microservices and cloud-specific solutions. Gradually, you will learn about the different approaches and models of the Security framework and explore various authentication models and authorization techniques, such as social media-based authentication and safe storage using app secrets. By the end of the book, you will get to know the concepts and usage of the emerging fields, such as DevOps, BigData, architectural practices, and Artificial Intelligence. Style and approach Filled with examples and use cases, this guide takes a no-nonsense approach to show you the best tools and techniques required to become a successful software architect. |
| data hub architecture diagram: Achieving Service-Oriented Architecture Rick Sweeney, 2010-04-20 A complete, comprehensive methodology and framework for adopting and managing a successful service oriented architecture environment Achieving Service-Oriented Architecture helps to set up an SOA Architecture Practice defining the policies, procedures, and standards that apply not just to IT developers but to the entire corporation as it relates to business applications. Why a new architectural approach is necessary for your business to achieve all the value SOA has to offer Focuses on setting up an enterprise architecture practice for service-oriented architecture Discusses the implementation and governance processes for SOA Defines and describes an overall architectural framework for managing SOA assets at an enterprise architecture level Shows how to set up and run an SOA Enterprise Architecture Practice using the methodology and framework presented Defining how an Architecture Practice can transform itself and your corporation to maximize the benefits of the SOA approach, Achieving Service-Oriented Architecture provides a pragmatic enterprise architecture approach and framework for implementing and managing service oriented architecture from a business organization and business practices perspective. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file. |
| data hub architecture diagram: Knowledge Discovery And Data Design Innovation - Proceedings Of The International Conference On Knowledge Management (Ickm 2017) Daniel Alemneh, Jeff Allen, Suliman Hawamdeh, 2017-10-19 To navigate the complex ecosystem of societal challenges, the International Conference on Knowledge Management Conference (ICKM 2017) focused on big data and data analytics as part of the relationship to the wider concept of knowledge management processes and practices.This book includes top papers presenting the major, and diverse, topics discussed at the conference. The papers covered various aspects of big data ranging from enhancing access to the big data to facilitating its wide applications in healthcare, social media, library and information centers, governments, and corporations. |
| data hub architecture diagram: Algorithms of Education Kalervo N. Gulson, Sam Sellar, P. Taylor Webb, 2022-05-17 A critique of what lies behind the use of data in contemporary education policy While the science fiction tales of artificial intelligence eclipsing humanity are still very much fantasies, in Algorithms of Education the authors tell real stories of how algorithms and machines are transforming education governance, providing a fascinating discussion and critique of data and its role in education policy. Algorithms of Education explores how, for policy makers, today’s ever-growing amount of data creates the illusion of greater control over the educational futures of students and the work of school leaders and teachers. In fact, the increased datafication of education, the authors argue, offers less and less control, as algorithms and artificial intelligence further abstract the educational experience and distance policy makers from teaching and learning. Focusing on the changing conditions for education policy and governance, Algorithms of Education proposes that schools and governments are increasingly turning to “synthetic governance”—a governance where what is human and machine becomes less clear—as a strategy for optimizing education. Exploring case studies of data infrastructures, facial recognition, and the growing use of data science in education, Algorithms of Education draws on a wide variety of fields—from critical theory and media studies to science and technology studies and education policy studies—mapping the political and methodological directions for engaging with datafication and artificial intelligence in education governance. According to the authors, we must go beyond the debates that separate humans and machines in order to develop new strategies for, and a new politics of, education. |
| data hub architecture diagram: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data hub architecture diagram: PCI Express System Architecture Ravi Budruk, Don Anderson, Tom Shanley, 2004 ••PCI EXPRESS is considered to be the most general purpose bus so it should appeal to a wide audience in this arena.•Today's buses are becoming more specialized to meet the needs of the particular system applications, building the need for this book.•Mindshare and their only competitor in this space, Solari, team up in this new book. |
| data hub architecture diagram: Data Management at Scale Piethein Strengholt, 2023-04-10 As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data hub architecture diagram: Mastering Azure Synapse Analytics , 2023-04-15 A practical guide that will help you transform your data into actionable insights with Azure Synapse Analytics KEY FEATURES ● Explore the different features in the Azure Synapse Analytics workspace. ● Learn how to integrate Power BI and Data Governance capabilities with Azure Synapse Analytics. ● Accelerate your analytics journey with the no-code/low-code capabilities of Azure Synapse. DESCRIPTION Cloud analytics is a crucial aspect of any digital transformation initiative, and the capabilities of the Azure Synapse analytics platform can simplify and streamline this process. By mastering Azure Synapse Analytics, analytics developers across organizations can boost their productivity by utilizing low-code, no-code, and traditional code-based analytics frameworks. This book starts with a comprehensive introduction to Azure Synapse Analytics and its limitless cloud-scale analytics capabilities. You will then learn how to explore and work with data warehousing features in Azure Synapse. Moving on, the book will guide you on how to effectively use Synapse Spark for data engineering and data science. It will help you learn how to gain insights from your data through Observational analytics using Synapse Data Explorer. You will also discover the seamless data integration capabilities of Synapse Pipeline, and delve into the benefits of Synapse Analytics' low-code and no-code pipeline development features. Lastly the book will show you how to create network topology and implement industry-specific architecture patterns in Azure Synapse Analytics. By the end of the book, you will be able to process and analyze vast amounts of data in real-time to gain insights quickly and make informed decisions. WHAT YOU WILL LEARN ● Leverage Synapse Spark for machine learning tasks. ● Use Synapse Data Explorer for telemetry analysis. ● Take advantage of Synapse's common data model-based database templates. ● Query data using T-SQL, KQL, and Spark SQL within Synapse. ● Integrate Microsoft Purview with Synapse for enhanced data governance. WHO THIS BOOK IS FOR This book is designed for Cloud data engineers with prior experience in Azure cloud computing, as well as Chief Data Officers (CDOs) and Data professionals, who want to use this unified platform for data ingestion, data warehousing, and big data analytics. TABLE OF CONTENTS 1. Cloud Analytics Concept 2. Introduction to Azure Synapse Analytics 3. Modern Data Warehouse with the Synapse SQL Pool 4. Query as a Service- Synapse Serverless SQL 5. Synapse Spark Pool Capability 6. Synapse Spark and Data Science 7. Learning Synapse Data Explorer 8. Synapse Data Integration 9. Synapse Link for HTAP 10. Azure Synapse -Unified Analytics Service 11. Synapse Workspace Ecosystem Integration 12. Azure Synapse Network Topology 13. Industry Cloud Analytics |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
