Books To Read About Data Warehouse
A data warehouse is a significant and centralized data repository used for analysis and reporting purposes, designed to support business intelligence and decision-making by providing a single, consistent view of data from multiple sources. It is designed to help business intelligence and decision-making by providing a consistent view of data from various sources. Data warehouses are used in numerous industries, including finance, healthcare, retail, and manufacturing. Data warehouses are essential for business intelligence, enabling decision-makers to access the data required to make informed decisions and gain a competitive advantage.
The top 10 derivative books cover a broad spectrum of topics related to data warehousing, including data modeling, ETL processes, project management, and implementation with various technologies. They are here to guide you so you can invest your valuable rupees in books worth your time.
10 Different Data Warehouse Books
| # | Books | Author | Published | Rating |
| 1. | The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition | Ralph Kimball | 2013 | Amazon: 4.7
Goodreads: 4.16 |
| 2. | Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema | Lawrence Corr, Jim Stagnitto
|
2011 | Amazon:4.6
Goodreads:4.1 |
| 3. | Database Systems: Introduction to Databases and Data Warehouses | Nenad Jukic, Susan Vrbsky, Svetlozar Nestorov
|
2016 | Amazon: 4.5
Goodreads:3.9 |
| 4. | Building a Scalable Data Warehouse with Data Vault 2.0 | Daniel Linstedt, Michael Olschimke | 2015 | Amazon:4.4
Goodreads: 4.1 |
| 5. | The Kimball Group Reader: Relentlessly Practical for Data Warehousing and Business Intelligence Remastered Collection
|
Ralph Kimball, Margy Ross
|
2010 | Amazon:4.6
Goodreads: 4.3
|
| 6. | Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series) | Ken Collier
|
2011
|
Amazon: 4.3
Goodreads: 3.8
|
| 7. | Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale | Tom White | 2015
|
Amazon 4.5
Goodreads- 3.9
|
| 8. | Building a Data Warehouse: With Examples in SQL Server (Expert’s voice) | Vincent Rainardi | 2008 | Amazon 4.4 Goodreads -3.8
|
| 9. | Big Data: Principles and best practices of scalable real-time data systems | Nathan Marz and James Warren
|
2012 | Amazon 4.2 Goodreads- 3.8
|
| 10. | Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems | Martin Kleppmann | 2015 | Amazon: 4.8
Goodreads: 4.7
|
Let us look at the Data Warehouse books and see which one best suits your needs:-
1. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition
Authors: Ralph Kimball and Margy Ross
Get this Book here
Book Review
A great addition to a businessman’s most used tools for knowing about BI/DW. The Book does well to promise to be helpful to both students and professionals alike. This reference book is necessary if you are thinking about repetitive analogies that will permanently reside in your brain.
Key Takeaway from that Book
- The reading material includes well-made case studies from retail sales, financial services, etc., to derive the meaning of the Book’s concept in real life.
- It contains value chain introduction, procurement transactions, bus matrix, order management, accounting, etc.
2. Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
Authors: Lawrence Corr and Jim Stagnitto
Get this Book here
Book Review
The authors have done an excellent job presenting the topics of the agile manifesto with the BEAM approach that is perfect for people who want another feather in their cap through data warehousing. The author uses visual representation tools such as diagrams and little characters to make the text seem less daunting. The readers get a glimpse of modeling a data warehouse from working on the whiteboard to star schema and various other topics throughout the Book.
Key Takeaway from that Book
- The topics of modeling business events, modeling star schemas, and design patterns for high-performance fact tables are explained in great detail for users to understand completely.
- The Book is excellent for you if you want to brush up your skills as an advanced agile dimensional modeler or just a bushy-tailed beginner.
- A unique feature of the Book is that we get summaries at the end of each chapter to glance back at the topics just read.
3. Database Systems: Introduction to Databases and Data Warehouses
Authors: Nenad Jukic, Susan Vrbsky, and Svetlozar Nestorov
Get this Book here
Book Review
This Book has a good command over operational and analytical database systems and creating ERDplus, relational and dimensional models. This Book is an introductory guide to use in university classes.
Key Takeaway from that Book
- Ideal for students looking to start as novices in data warehousing who want to practice with many exercises and practice sheets.
- The excellently portrayed topics are ERD diagrams and relational entity models.
4. Building a Scalable Data Warehouse with Data Vault 2.0
Author: Daniel Linstedt and Michael Olschimke
Get this Book here
Book Review
Welcoming you to a world of data vaults in data warehousing, this Book takes you on a wild ride touching the concepts of SQL Server Integration Services, data quality services, and master data services. Many topics are discussed in great detail with suitable diagrams and required references at the end.
Key Takeaway from that Book
- This Book is for anyone wanting to dive into the data vault though it was a little outdated in 2022.
- It covers topics from data vault 2.0 modeling, advanced data vault modeling, master data management, etc.
5. The Kimball Group Reader: Relentlessly Practical for Data Warehousing and Business Intelligence Remastered Collection
Authors: Ralph Kimball, Margy Ross, Bob Becker, Joy Mundy, and Warren Thornthwaite
Get this Book here
Book Review
The authors have incorporated their year’s knowledge of data warehousing in the final series of the anthology, which is more comprehensive and expanded from the previous editions. The Book requires a prerequisite knowledge of the basics of the data warehouse discussed.
Key Takeaway from that Book
- The Book covers 65 new articles and distinguishable features which were not there in the previous editions.
- The book website has content with design tips available and is easily accessible for the general public to consume.
- Outlines topics such as fact tables, ETL subsystems, data quality, BI application best practices, etc., briefly.
6. Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)
Author: Ken Collier
Get this Book here
Book Review
The Book is easy to follow and precise, with information about agile analytics. The Book contains topics such as filibuster analytics and some truths about agile analytics, which are easy to understand.
The key takeaway from that Book
- The topics of management methods and technical methods are heavily focused On Agile analytics.
- The primary topics of these books are modeling business events, modeling business dimensions and facts, and measures and KPIs.
- The Book is a complete meal for someone hungry to know much about agile for evolving excellent design, version control for data warehousing, and user stories for BI systems.
7. Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
Author: Tom White
Get this Book here
Book review
Tom White does an excellent job at capturing the core of Hadoop fair and square without meandering about nonsense. This book is a guide for everyone new to Hadoop open-source software. This Book is for anyone with some prerequisite knowledge of the open source.
Key Takeaway from that Book
- It will be a comprehensive read, so going through it multiple times is advisable.
- The Book covers topics like how MapReduce works and administering Hadoop, Avro, parquet, and Hbase.
- The Book contains ample code and theory to get you invested in Hadoop.
8. Building a Data Warehouse: With Examples in SQL Server (Expert’s Voice)
Author: Vincent Rainardi
Get this Book here
Book Review
A thorough guide to building a data warehouse simply yet eloquently. Promises an exhaustive list of data extraction, metadata, the architecture of data warehousing, etc. Experienced developers, managers working in the data warehouse field, and admins of DW are the main target this Book has been aimed for.
Key Takeaway from that Book
- The Book thoroughly discusses building reports, populating the data warehouse, and data modeling.
- The Book is built around a cluster of real-world cases with which the author had a chance to interact.
- A must-have if you want access to practical implementation in a DW book using Microsoft SQL.
9. Big Data: Principles and best practices of Scalable real-time data Systems
Author: Nathan Marz
Get this Book here
Book Review
It is essentially a guide for people interested to know how technologies fit together in big data and how to choose the ideal tech stack for a specific problem. Recommended for anyone who wants to skim over the surface of the data warehouse.
Key Takeaway from that Book
- An organized accumulated read-on data model for big data, serving layers, and stream processing.
- Lambda architecture is the major attraction of this Book, and the code blocks of teaching Hadoop are written in Java, which can confuse some readers.
10. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Author: Martin Kleppmann
Get this Book here
Book Review
A contemporary guide to unlocking concepts of modern networked applications that will stay in your mind long after you finish the Book. A primary focus is on the functionality of each model rather than the practical implementation.
Key Takeaway from that Book
- The Book is divided into three parts: foundations of data systems, distributed data, and derived data.
- The illustrations make the chapters enjoyable, while the text is helpful with accurate code snippets.
- Batch processing, trouble with distributed systems, data models and query languages, and replication are some topics that are heavily focused on.
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