Building The Data Lakehouse
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|Author||: Bill Inmon,Ranjeet Srivastava,Mary Levins|
|Publsiher||: Technics Publications|
|Total Pages||: 256|
|Genre||: Electronic Book|
Download Building the Data Lakehouse Book in PDF, Epub and Kindle
The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.
|Author||: Manoj Kukreja,Danil Zburivsky|
|Publsiher||: Packt Publishing Ltd|
|Total Pages||: 480|
Download Data Engineering with Apache Spark Delta Lake and Lakehouse Book in PDF, Epub and Kindle
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key Features Become well-versed with the core concepts of Apache Spark and Delta Lake for building data platforms Learn how to ingest, process, and analyze data that can be later used for training machine learning models Understand how to operationalize data models in production using curated data Book Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learn Discover the challenges you may face in the data engineering world Add ACID transactions to Apache Spark using Delta Lake Understand effective design strategies to build enterprise-grade data lakes Explore architectural and design patterns for building efficient data ingestion pipelines Orchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIs Automate deployment and monitoring of data pipelines in production Get to grips with securing, monitoring, and managing data pipelines models efficiently Who this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
|Author||: W. H. Inmon|
|Publsiher||: John Wiley & Sons|
|Total Pages||: 435|
Download Building the Data Warehouse Book in PDF, Epub and Kindle
The data warehousing bible updated for the new millennium Updated and expanded to reflect the many technological advances occurring since the previous edition, this latest edition of the data warehousing "bible" provides a comprehensive introduction to building data marts, operational data stores, the Corporate Information Factory, exploration warehouses, and Web-enabled warehouses. Written by the father of the data warehouse concept, the book also reviews the unique requirements for supporting e-business and explores various ways in which the traditional data warehouse can be integrated with new technologies to provide enhanced customer service, sales, and support-both online and offline-including near-line data storage techniques.
|Author||: Bill Inmon|
|Publsiher||: Technics Publications|
|Total Pages||: 166|
Download Data Lake Architecture Book in PDF, Epub and Kindle
Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.
|Author||: Alex Gorelik|
|Publsiher||: "O'Reilly Media, Inc."|
|Total Pages||: 224|
Download The Enterprise Big Data Lake Book in PDF, Epub and Kindle
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
|Author||: Bill Inmon,Francesco Puppini|
|Publsiher||: Technics Publications|
|Total Pages||: 294|
Download The Unified Star Schema An Agile and Resilient Approach to Data Warehouse and Analytics Design Book in PDF, Epub and Kindle
Master the most agile and resilient design for building analytics applications: the Unified Star Schema (USS) approach. The USS has many benefits over traditional dimensional modeling. Witness the power of the USS as a single star schema that serves as a foundation for all present and future business requirements of your organization. Data warehouse legend Bill Inmon and business intelligence innovator, Francesco Puppini, explain step-by-step why the Unified Star Schema is the recommended approach for business intelligence designs today, and show through many examples how to build and use this new solution. This book contains two parts. Part I, Architecture, explains the benefits of data marts and data warehouses, covering how organizations progressed to their current state of analytics, and to the challenges that result from current business intelligence architectures. Chapter 1 covers the drivers behind and the characteristics of the data warehouse and data mart. Chapter 2 introduces dimensional modeling concepts, including fact tables, dimensions, star joins, and snowflakes. Chapter 3 recalls the evolution of the data mart. Chapter 4 explains Extract, Transform, and Load (ETL), and the value ETL brings to reporting. Chapter 5 explores the Integrated Data Mart Approach, and Chapter 6 explains how to monitor this environment. Chapter 7 describes the different types of metadata within the data warehouse environment. Chapter 8 progresses through the evolution to our current modern data warehouse environment. Part II, the Unified Star Schema, covers the Unified Star Schema (USS) approach and how it solves the challenges introduced in Part I. There are eight chapters within Part II: · Chapter 9, Introduction to the Unified Star Schema: Learn about its architecture and use cases, as well as how the USS approach differs from the traditional approach. · Chapter 10, Loss of Data: Learn about the loss of data and the USS Bridge. Understand that the USS approach does not create any join, and for this reason, it has no loss of data. · Chapter 11, The Fan Trap: Get introduced to the Oriented Data Model convention, and learn the dangers of a fan trap through an example. Differentiate join and association, and realize that an “in-memory association” is the preferred solution to the fan trap. · Chapter 12, The Chasm Trap: Become familiar with the Cartesian product, and then follow along with an example based on LinkedIn, which illustrates that a chasm trap produces unwanted duplicates. See that the USS Bridge is based on a union, which does not create any duplicates. · Chapter 13, Multi-Fact Queries: Distinguish between multiple facts “with direct connection” versus multiple facts “with no direct connection”. Explore how BI tools are capable of building aggregated virtual rows. · Chapter 14, Loops: Learn more about loops and five traditional techniques to solve them. Follow along with an implementation, which will illustrate the solution based on the USS approach. · Chapter 15, Non-Conformed Granularities: Learn about non-conformed granularities, and learn that the Unified Star Schema introduces a solution called “re-normalization”. · Chapter 16, Northwind Case Study. Witness how easy it is to detect the pitfalls of Northwind using the ODM convention. Follow along with an implementation of the USS approach on the Northwind database with various BI tools.
|Author||: Zhamak Dehghani|
|Publsiher||: "O'Reilly Media, Inc."|
|Total Pages||: 379|
Download Data Mesh Book in PDF, Epub and Kindle
We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh
|Author||: Jon Tate,Pall Beck,Hector Hugo Ibarra,Shanmuganathan Kumaravel,Libor Miklas,IBM Redbooks|
|Publsiher||: IBM Redbooks|
|Total Pages||: 300|
Download Introduction to Storage Area Networks Book in PDF, Epub and Kindle
The superabundance of data that is created by today's businesses is making storage a strategic investment priority for companies of all sizes. As storage takes precedence, the following major initiatives emerge: Flatten and converge your network: IBM® takes an open, standards-based approach to implement the latest advances in the flat, converged data center network designs of today. IBM Storage solutions enable clients to deploy a high-speed, low-latency Unified Fabric Architecture. Optimize and automate virtualization: Advanced virtualization awareness reduces the cost and complexity of deploying physical and virtual data center infrastructure. Simplify management: IBM data center networks are easy to deploy, maintain, scale, and virtualize, delivering the foundation of consolidated operations for dynamic infrastructure management. Storage is no longer an afterthought. Too much is at stake. Companies are searching for more ways to efficiently manage expanding volumes of data, and to make that data accessible throughout the enterprise. This demand is propelling the move of storage into the network. Also, the increasing complexity of managing large numbers of storage devices and vast amounts of data is driving greater business value into software and services. With current estimates of the amount of data to be managed and made available increasing at 60% each year, this outlook is where a storage area network (SAN) enters the arena. SANs are the leading storage infrastructure for the global economy of today. SANs offer simplified storage management, scalability, flexibility, and availability; and improved data access, movement, and backup. Welcome to the cognitive era. The smarter data center with the improved economics of IT can be achieved by connecting servers and storage with a high-speed and intelligent network fabric. A smarter data center that hosts IBM Storage solutions can provide an environment that is smarter, faster, greener, open, and easy to manage. This IBM® Redbooks® publication provides an introduction to SAN and Ethernet networking, and how these networks help to achieve a smarter data center. This book is intended for people who are not very familiar with IT, or who are just starting out in the IT world.