Best Data Warehouse Books to Learn Data Warehousing

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 Learn

• Choose 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 ingestion
• Create advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse

What you will learn

• Implement data governance with Azure services
• Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor
• Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling
• Implement networking with Synapse Analytics and Spark pools
• Create and run Spark jobs with Databricks clusters
• Implement streaming using Azure Functions, a serverless runtime environment on Azure
• Explore the predefined ML services in Azure and use them in your app

This is an extremely accessible book, with the concepts clearly explained and easily understood, even for those without a data warehouse / business intelligence background. I am a backend developer tasked with creating a data warehouse / data mart for reporting and analytics. The techniques described in this book are invaluable. Our application data is in MongoDB, and being streamed into a PostgreSQL database for reporting. I was particularly grateful for Francesco's tips on dealing with JSON data: in particular the dangers of the fan and chasm traps when dealing with JSON arrays. Using the techniques described in this book I have successfully created a self-service data-mart for use by our analysts.

In this book, you'll explore how to use data modeling and navigation techniques to define relationships and create a data model before defining new metrics and performing custom calculations using modeling features. As you advance through the chapters, the book will demonstrate how to create full-fledged data models, enabling you to create efficient data models and simpler DAX code with new data modeling features. With the help of examples, you'll discover how you can solve business challenges by building optimal data models and changing your existing data models to meet evolving business requirements. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks.

What you will learn

• Implement virtual tables and time intelligence functionalities in DAX to build a powerful model
• Identify Dimension and Fact tables and implement them in Power Query Editor
• Deal with advanced data preparation scenarios while building Star Schema
• Explore best practices for data preparation and data modeling
• Discover different hierarchies and their common pitfalls
• Understand complex data models and how to decrease the level of model complexity with different data modeling approaches

With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work.

• Build a self-service portal to support data discovery, quality, lineage, and governance
• Select the best approach for each self-service capability using open source cloud technologies
• Tailor self-service for the people, processes, and technology maturity of your data platform
• Implement capabilities to democratize data and reduce time to insight
• Scale your self-service portal to support a large number of users within your organization

The Data Warehouse Toolkit is a definitive book on Dimensional Modeling and the Kimball methodology. If you need to learn the basics to the advanced method of Dimensional Data Warehouse Modeling and Business Intelligence, this Data Warehouse book is a must-read and will be kept forever as a reference book/manual. Concise, well-written, and easy to follow.

This book features:

• Data warehousing, Business Intelligence, and Dimensional Modeling Primer.
• Kimball Dimensional Modelling Techniques Overview.
• Retail Sales and Inventory Models.
• Procurement Case Study.
• Order Management.
• Accounting Case Study and Bus Matrix.
• Customer Relationship Management.
• Human Resources Management.
• Financial Services.
• Telecommunications Case Study.
• Transportation.
• Education and Healthcare.
• Electronic Commerce and Insurance.
• Kimball DW/BI Lifecycle Overview.
• Dimensional Modeling Process and Tasks.
• ETL Subsystems and Techniques.
• ETL System Design.
• Big Data Analytics.

This Data Warehouse book is a very comprehensive and readable book explaining the ins and outs of the Data Vault methodology and modeling. A very good book and explains everything you need to know to start from scratch about Data Vault and Data Vault 2.0. It covers everything about data vault, including data modeling, ETL processing, error handling, metadata, data quality and more, all explained in-depth with sufficient examples that can be immediately put to use.

This book includes:

• Introduction to Data Warehousing.
• Scalable Data Warehouse Architecture.
• The Data Vault 2.0 Methodology.
• Data Vault 2.0 Modelling.
• Intermediate Data Vault Modelling.
• Advanced-Data Vault Modelling.
• Dimensional Modelling.
• Physical Data Warehouse Design.
• Master Data Management.
• Metadata Management.
• Data Extraction.
• Loading the Data Vault.
• Implementing Data Quality.
• Loading the Dimensional Information Mart.
• Multidimensional Database.

Great source for an introduction to databases and all their related parts. The book includes great explanations and real-world examples to get points across. Well written and gives detailed examples of each concept.

This book contains:

• Development of Database Systems.
• Database Requirements and ER Modelling.
• Relational Database Modelling.
• Update Operations, Anomalies and Normalization.
• SQL Overview.
• Database Implementation and Use.
• Data Warehousing Concepts.
• Data Warehouse Implementation and Use.
• DBMS Functionalities and Database Administration.
  
  

Agile Data Warehouse Design is an eminently useful book and a long-needed complement to the dimensional modeling literature. A good step-by-step guide for capturing data warehousing requirements and building dimensional models from these requirements through model storming with business intelligence stakeholders.

This book delivers:

• How to Model a Data Warehouse.
• Modeling Business Events.
• Modelling Business Dimensions.
• Modelling Business Processes.
• Modelling Star Schemas.
• Design Patterns for People and Organizations, Products and Services.
• Design Patterns for Time and Locations.
• Design Patterns for High-Performance Fact Tables and Flexible Measures.
• Design Patterns for Cause and Effect.
  

The Data Warehouse ETL Toolkit is an amazing book for the newbies into this domain to have an overview of the things happening in the data warehouse. This book gives practical guidelines to follow through the ETL cycle, it does not matter if you are using an Industry Standard ETL tool or writing your own ETL process from scratch, this book will be useful for both. Great resource book with understandable explanations and writing.

This book offers:

• Surrounding the requirements.
• ETL Data Structures.
• Extracting.
• Cleaning and Conforming.
• Delivering Dimension Tables.
• Delivering Fact Tables.
• Development and Operations.
• Metadata and Responsibilities.
• Real-time ETL Systems.
  

Good overview and provided a good working knowledge of the queries for Hadoop. This book will guide you to Apache Hive, Hadoop’s data warehouse infrastructure. Concise and easy to understand.

This book covers:

• Overview of Hadoop and MapReduce.
• Installing a Preconfigured Virtual Machine.
• Data Types and File Format.
• Data Definition.
• Data Manipulation.
• Queries.
• Views, Indexes, and Designs.
• Schema Design and Tuning.
• Other File Formats and Compression.
• Developing and Functions.
• Streaming.
• Customizing Hive File and Record Formats.
• Hive Thrift Service.
• Storage Handlers and NoSQL.
• Security and Locking.
• Hive Integrating and Oozie.
• Hive and Amazon Web Services (AWS).
• HCatalog and Case Studies.
• Glossary and References.
  

Great Book on Data Warehousing For Dummies. This is an excellent book for people who are new to data warehousing and just need an overview. It will teach you how to manage a data warehouse project successfully. 

This book features:

• The Data Warehouse: Home for your Data Assets.
• Data Warehousing Technology.
• Business Intelligence and Data Warehousing.
• Data Warehousing Projects.
• Business Analysis (OLAP).
• Data Mining.
• Dashboards and Scorecards.
• Building a Winning Data Warehousing Team.
• Capturing Requirements.
• Analyzing Data Resources.
• Delivering the Goods.
• User Testing, Feedback, and Acceptance.
• The Information Value Chain.
• Data Warehousing Driving Quality and Integration.
• Working with Data Warehousing Consultants.
• Expanding your Data Warehousing with Unstructured Data.
• 10 Secrets to Managing your Projects successfully.
• 10 Sources of Up-to-date Information about Data Warehousing.
• 10 Mandatory Skills for a Data Warehousing Consultant.
• 10 Subject Areas to Cover with Product Vendors.
  

The Biml book provides some wise advice on the importance of using an ETL framework, its basic components, how to deal with some common sources/targets, and how to get started with the all-important metadata generation needed in order to be effective with BimI.

This book includes:

• Learning BimI.
• Introduction to the BimI Language.
• Basic Staging Operations.
• Importing Metadata.
• Reusing Code, Helper Classes and Methods.
• A Custom BimI Framework.
• Using BimI as an SSIS Design Patterns Engine.
• Integration with a Custom SSIS Execution Framework.
• Metadata Automation.
• Advanced BimI Framework and BimIFlex.
• BimI and Analysis Services.
• BimI for T-SQL and Other Little Helpers.
• Documenting your BimI Solution.
• Troubleshooting Metadata.
• Troubleshooting BimI.
• Appendices.
  

Data Warehouse Automation is a great book for those who want to build a data warehouse ASAP. A detailed, hands-on, step-by-step resource that you can first use to quickly get a data warehouse up and running, but then keep handy on your bookshelf to assist in future improvements. This book also contains helpful advice & tips which are very easy to follow and understand.

This book contains:

• Introduction and Scope.
• Benefits of Data Warehouses and Data Warehouse Automation.
• Proposal Phase.
• Step-by-step Construction of a Rapid Prototype.
• Overview of Data Warehouse Development Goals.
• Installation and Setup of Tools Needed for a Rapid Prototype.
• Create an ETL Group.
• Design Download Tables.
• Flat Table Design.
• Flat Procedure Design.
• Dimension Table Design.
• Dimension Procedure Design.
• Fact Table Design.
• Fact Procedure Design.
• Compiling an ETL Group.
• The 'Queries and Info' Section.
• Junk Dimension Design.
• Bridge Dimension Design.
• Outrigger Dimension Design.
• Pivot Tables.
• Classifying Unknown Tables.

As Data Warehousing technology is the modern trend right now and if you want to get introduced with Data Warehousing and apply it to your business intelligence this book is for you. It will teach you how Data Warehousing reduces complexity in the modern era.

This book offers:

• Basic introduction of Data Warehousing.
• How to implement it into business intelligence.
• What it's core requirements.
• Data Warehouse Architecture.
• Performance Analysis.
• References with real-time examples.
• Easy to follow and understand.
  

The Data Warehouse Toolkit, 3rd Edition Ralph Kimball invented a data warehousing technique called "dimensional modeling" and popularized it in his first Wiley book, The Data Warehouse Toolkit. Since this book was first published in 1996, dimensional modeling has become the most widely accepted technique for data warehouse design. Over the past 10 years, Kimball has improved on his earlier techniques and created many new ones. In this 3rd edition, he will provide a comprehensive collection of all of these techniques, from basic to advanced.

The Data Warehouse Lifecycle Toolkit, 2nd Edition Complete coverage of best practices from data warehouse project inception through on-going program management.  Updates industry best practices to be in sync with current recommendations of Kimball Group.  Streamlines the lifecycle methodology to be more efficient and user-friendly

The Data Warehouse ETL Toolkit shows data warehouse developers how to effectively manage the ETL (Extract, Transform, Load) phase of the data warehouse development lifecycle.  The authors show developers the best methods for extracting data from scattered sources throughout the enterprise, removing obsolete, redundant, and innaccurate data, transforming the remaining data into correctly formatted data structures, and then physically loading them into the data warehouse.  

This book provides complete coverage of proven, time-saving ETL techniques. It begins with a quick overview of ETL fundamentals and the role of the ETL development team.  It then quickly moves into an overview of the ETL data structures, both relational and dimensional.  The authors show how to build useful dimensional stuctures, providing practical examples of beginning through advanced techniques.