Download or read book The Cloud Data Lake written by Rukmani Gopalan and published by "O'Reilly Media, Inc.". This book was released on 2022-12-12 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: More organizations than ever understand the importance of data lake architectures for deriving value from their data. Building a robust, scalable, and performant data lake remains a complex proposition, however, with a buffet of tools and options that need to work together to provide a seamless end-to-end pipeline from data to insights. This book provides a concise yet comprehensive overview on the setup, management, and governance of a cloud data lake. Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance. Learn the benefits of a cloud-based big data strategy for your organization Get guidance and best practices for designing performant and scalable data lakes Examine architecture and design choices, and data governance principles and strategies Build a data strategy that scales as your organizational and business needs increase Implement a scalable data lake in the cloud Use cloud-based advanced analytics to gain more value from your data

Download or read book The Enterprise Big Data Lake written by Alex Gorelik and published by "O'Reilly Media, Inc.". This book was released on 2019-02-21 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book The Cloud Data Lake written by Rukmani Gopalan and published by . This book was released on 2022-12-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: More organizations than ever understand the importance of data lake architectures for deriving value from their data. Building a robust, scalable, and performant data lake remains a complex proposition, however, with a buffet of tools and options that need to work together to provide a seamless end-to-end pipeline from data to insights. This book provides a concise yet comprehensive overview on the setup, management, and governance of a cloud data lake. Author Rukmani Gopalan, product management leader at Microsoft, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance. Learn the benefits of a cloud-based big data strategy for your organization Get guidance and best practices for designing performant and scalable data lakes Examine architecture and design choices, and data governance principles and strategies Build a data strategy that scales as your organizational and business needs increase Implement a scalable data lake in the cloud Use cloud-based advanced analytics to gain more value from your data

Download or read book Cloud Data Lakes for Dummies Snowflake Special Edition Custom written by David Baum and published by For Dummies. This book was released on 2019-11-19 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is a modern cloud data lake? How it compares to other analytics solutions Tips for choosing a cloud data lake Get insights fast from all your data by all your users with a cloud data lake The concept of first-generation data lakes aimed to create a single repository for storing, integrating, and analyzing all of an organization's data. As years passed, reality set in and most data lake initiatives failed. Today, organizations still want to achieve that aim: a cloud data lake that is simple yet powerful, flexible and affordable, and provides unparalleled business value. Read this book to learn how the modern cloud data lake provides all of this and more to enable data-driven decision-making across your organization. Inside... Why the cloud data lake emerged How to evaluate different data lakes How to easily enable a modern data lake with the modern data platform How to maximize scale and lower costs Why data security, governance, and sovereignty are data lake essentials How a data lake enables data sharing

Download or read book Designing Cloud Data Platforms written by Danil Zburivsky and published by Simon and Schuster. This book was released on 2021-04-20 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Lakes For Dummies written by Alan R. Simon and published by John Wiley & Sons. This book was released on 2021-07-14 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a dive into data lakes “Data lakes” is the latest buzz word in the world of data storage, management, and analysis. Data Lakes For Dummies decodes and demystifies the concept and helps you get a straightforward answer the question: “What exactly is a data lake and do I need one for my business?” Written for an audience of technology decision makers tasked with keeping up with the latest and greatest data options, this book provides the perfect introductory survey of these novel and growing features of the information landscape. It explains how they can help your business, what they can (and can’t) achieve, and what you need to do to create the lake that best suits your particular needs. With a minimum of jargon, prolific tech author and business intelligence consultant Alan Simon explains how data lakes differ from other data storage paradigms. Once you’ve got the background picture, he maps out ways you can add a data lake to your business systems; migrate existing information and switch on the fresh data supply; clean up the product; and open channels to the best intelligence software for to interpreting what you’ve stored. Understand and build data lake architecture Store, clean, and synchronize new and existing data Compare the best data lake vendors Structure raw data and produce usable analytics Whatever your business, data lakes are going to form ever more prominent parts of the information universe every business should have access to. Dive into this book to start exploring the deep competitive advantage they make possible—and make sure your business isn’t left standing on the shore.

Download or read book Mastering Azure Analytics written by Zoiner Tejada and published by "O'Reilly Media, Inc.". This book was released on 2017-04-06 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Helps users understand the breadth of Azure services by organizing them into a reference framework they can use when crafting their own big-data analytics solution.

Download or read book Data Lake for Enterprises written by Tomcy John and published by Packt Publishing Ltd. This book was released on 2017-05-31 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to implementing your enterprise data lake using Lambda Architecture as the base About This Book Build a full-fledged data lake for your organization with popular big data technologies using the Lambda architecture as the base Delve into the big data technologies required to meet modern day business strategies A highly practical guide to implementing enterprise data lakes with lots of examples and real-world use-cases Who This Book Is For Java developers and architects who would like to implement a data lake for their enterprise will find this book useful. If you want to get hands-on experience with the Lambda Architecture and big data technologies by implementing a practical solution using these technologies, this book will also help you. What You Will Learn Build an enterprise-level data lake using the relevant big data technologies Understand the core of the Lambda architecture and how to apply it in an enterprise Learn the technical details around Sqoop and its functionalities Integrate Kafka with Hadoop components to acquire enterprise data Use flume with streaming technologies for stream-based processing Understand stream- based processing with reference to Apache Spark Streaming Incorporate Hadoop components and know the advantages they provide for enterprise data lakes Build fast, streaming, and high-performance applications using ElasticSearch Make your data ingestion process consistent across various data formats with configurability Process your data to derive intelligence using machine learning algorithms In Detail The term "Data Lake" has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake. Style and approach The book takes a pragmatic approach, showing ways to leverage big data technologies and lambda architecture to build an enterprise-level data lake.

Download or read book The Journey Continues From Data Lake to Data Driven Organization written by Mandy Chessell and published by IBM Redbooks. This book was released on 2018-02-19 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM RedguideTM publication looks back on the key decisions that made the data lake successful and looks forward to the future. It proposes that the metadata management and governance approaches developed for the data lake can be adopted more broadly to increase the value that an organization gets from its data. Delivering this broader vision, however, requires a new generation of data catalogs and governance tools built on open standards that are adopted by a multi-vendor ecosystem of data platforms and tools. Work is already underway to define and deliver this capability, and there are multiple ways to engage. This guide covers the reasons why this new capability is critical for modern businesses and how you can get value from it.

Download or read book Data Lake Development with Big Data written by Pradeep Pasupuleti and published by Packt Publishing Ltd. This book was released on 2015-11-26 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Lakehouse in Action written by Pradeep Menon and published by Packt Publishing Ltd. This book was released on 2022-03-17 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patterns Key FeaturesUnderstand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner. What you will learnUnderstand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.

Download or read book The Modern Data Warehouse in Azure written by Matt How and published by Apress. This book was released on 2020-06-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast—fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision making that drives your business forward toward a pattern of success. This book teaches you how to employ the Azure platform in a strategy to dramatically improve implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure such as choosing which type of SQL engine (from at least three options) best meets the needs of your organization. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based implementations. What You Will LearnChoose the appropriate Azure SQL engine for implementing a given data warehouse Develop smart, reusable ETL/ELT processes that are resilient and easily maintained Automate mundane development tasks through tools such as PowerShell Ensure consistency of data by creating and enforcing data contracts Explore streaming and event-driven architectures for data ingestionCreate advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse Who This Book Is For Data warehouse or ETL/ELT developers who wish to implement a data warehouse project in the Azure cloud, and developers currently working in on-premise environments who want to move to the cloud, and for developers with Azure experience looking to tighten up their implementation and consolidate their knowledge

Download or read book Data Engineering with Google Cloud Platform written by Adi Wijaya and published by Packt Publishing Ltd. This book was released on 2022-03-31 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.

Download or read book Building the Data Lakehouse written by Bill Inmon and published by Technics Publications. This book was released on 2021-10 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Building Cloud Data Platforms Solutions written by Anouar BEN ZAHRA and published by Anouar BEN ZAHRA. This book was released on with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud" comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.

Download or read book Cloud Data Design Orchestration and Management Using Microsoft Azure written by Francesco Diaz and published by Apress. This book was released on 2018-06-28 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use Microsoft Azure to optimally design your data solutions and save time and money. Scenarios are presented covering analysis, design, integration, monitoring, and derivatives. This book is about data and provides you with a wide range of possibilities to implement a data solution on Azure, from hybrid cloud to PaaS services. Migration from existing solutions is presented in detail. Alternatives and their scope are discussed. Five of six chapters explore PaaS, while one focuses on SQL Server features for cloud and relates to hybrid cloud and IaaS functionalities. What You'll Learn Know the Azure services useful to implement a data solution Match the products/services used to your specific needs Fit relational databases efficiently into data design Understand how to work with any type of data using Azure hybrid and public cloud features Use non-relational alternatives to solve even complex requirements Orchestrate data movement using Azure services Approach analysis and manipulation according to the data life cycle Who This Book Is For Software developers and professionals with a good data design background and basic development skills who want to learn how to implement a solution using Azure data services

Download or read book Building a Scalable Data Warehouse with Data Vault 2 0 written by Daniel Linstedt and published by Morgan Kaufmann. This book was released on 2015-09-15 with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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