May 1, 2024
Updated May 11, 2025
20 minute read
Online Analytical Processing, or OLAP, is a powerful technology that enables users to analyze business data from multiple perspectives. Organizations gather data from diverse sources like websites, applications, and internal systems, which OLAP then consolidates and categorizes to provide actionable insights for strategic planning. This capability allows for swift and complex analysis of large datasets, transforming raw data into meaningful information that can drive decision-making.
Working with OLAP can be an engaging experience, particularly for those who enjoy uncovering trends and patterns within data. The ability to interactively explore vast amounts of information, slice and dice it in various ways, and quickly generate reports offers a dynamic way to understand business performance. Furthermore, OLAP plays a crucial role in fostering a data-driven culture within organizations by making complex data analysis more accessible to a broader range of users, not just highly technical data specialists.
Introduction to OLAP
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Find a path to becoming a OLAP. Learn more at:
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Reading list
We've selected 29 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
OLAP.
Is widely considered the authoritative guide to dimensional modeling, a cornerstone of OLAP. It provides comprehensive techniques and best practices for designing data warehouses that are easy to understand and query efficiently. It's an essential reference for anyone serious about OLAP and data warehousing, suitable for students and professionals alike. This book is commonly used as a textbook and reference.
ETL (Extract, Transform, Load) crucial process for populating data warehouses used for OLAP. provides in-depth coverage of ETL techniques, design patterns, and best practices. It's a practical guide for anyone involved in the data integration aspects of a data warehousing and OLAP solution. This book serves as a valuable reference for ETL developers and architects.
Complements The Data Warehouse Toolkit by covering the entire lifecycle of a data warehousing and business intelligence project. It provides practical guidance on project management, requirements gathering, architecture, and deployment. It's a valuable resource for understanding the practical aspects of implementing OLAP solutions within a larger project context. This book is useful as a reference for project execution.
Provides a practical guide to data warehousing and OLAP implementation. It covers all aspects of the data warehousing process, from data modeling to data analysis. It is an essential read for anyone who wants to build a successful data warehouse.
Focuses specifically on the star schema, a fundamental dimensional modeling technique heavily used in OLAP databases. It provides a detailed guide to designing and implementing star schemas. It's a good resource for deepening understanding of this specific modeling approach. This book useful reference for dimensional modelers.
This recent book covers the entire data engineering lifecycle, including data warehousing concepts within the broader data platform. It provides a modern perspective on building data systems that support analytics and BI, including OLAP. It's highly relevant for understanding the contemporary data landscape and how OLAP fits in. valuable resource for aspiring and practicing data engineers.
Offers a broad overview of business intelligence, encompassing data integration, data warehousing, and analytics. It helps connect the technical aspects of OLAP to the broader business goals of BI. It's suitable for gaining a comprehensive understanding of the BI landscape in which OLAP operates. This book is helpful for both technical and business-oriented readers.
Presents an agile approach to dimensional modeling, emphasizing collaboration and iterative development. It offers practical techniques for designing dimensional models interactively. It's relevant for those interested in modern data warehousing practices and provides a different perspective compared to traditional methods. This book is helpful for practitioners adopting agile methodologies.
MDX is the primary query language for multidimensional OLAP cubes in SSAS. provides a comprehensive guide to writing MDX queries, from basic concepts to advanced techniques. It's essential for anyone working directly with SSAS multidimensional models. This book key reference for MDX developers and analysts.
Provides practical data warehouse design solutions for various business areas, illustrating dimensional modeling in real-world scenarios. It helps solidify understanding by showing how OLAP concepts are applied to solve specific business problems. It's a good resource for seeing diverse applications of dimensional design. This book can serve as a reference for industry-specific design patterns.
Compilation of articles and design tips from the Kimball Group, offering practical advice on various aspects of data warehousing and business intelligence. It provides valuable insights and solutions to common challenges encountered in implementing OLAP systems. It's a useful resource for gaining practical knowledge and tips from experts. This book serves as a collection of best practices.
Addresses the challenges and opportunities of integrating big data with traditional data warehousing approaches. It explores how OLAP concepts apply in a big data environment and discusses modern architectures. It is particularly relevant for understanding contemporary topics in the field. This book is valuable for those looking to bridge the gap between traditional DW/BI and big data.
Often referred to as the "father of data warehousing," Bill Inmon presents the enterprise data warehouse approach in this classic text. It focuses on building a subject-oriented, integrated, time-variant, and nonvolatile collection of data. While some of the technology discussed may be dated, the core principles of data integration and structuring for a single source of truth remain highly relevant for understanding the broader data warehousing context around OLAP. is more valuable as historical and foundational reading than a current technical reference.
Covers the fundamental concepts of data warehousing, including data modeling, data extraction, transformation, and loading (ETL), and data analysis. It valuable resource for anyone who wants to learn about the basics of data warehousing.
Provides a comprehensive overview of OLAP solutions. It covers all aspects of the OLAP solution process, from data modeling to data analysis. It is an essential read for anyone who wants to learn about OLAP solutions.
Provides a comprehensive guide to SQL Server Analysis Services 2012 (SSAS). It covers all aspects of SSAS implementation, from data modeling to data analysis. It is an essential read for anyone who wants to build a data warehouse on the SSAS platform.
Provides a comprehensive guide to data mining techniques. It covers all aspects of the data mining process, from data preparation to data analysis. It is an essential read for anyone who wants to learn about data mining.
Provides a comprehensive overview of business intelligence and data warehousing. It covers all aspects of the business intelligence and data warehousing process, from data collection to data analysis. It is an essential read for anyone who wants to learn about business intelligence and data warehousing.
Provides a comprehensive overview of OLAP and business intelligence. It covers all aspects of the OLAP and business intelligence process, from data modeling to data analysis. It is an essential read for anyone who wants to learn about OLAP and business intelligence.
Provides a comprehensive guide to OLAP design for multidimensional databases. It covers all aspects of the OLAP design process, from data modeling to data storage. It is an essential read for anyone who wants to design a successful OLAP system.
While not exclusively about OLAP, this book provides a fundamental understanding of the principles behind modern data systems, including databases and distributed systems. These concepts are highly relevant to the underlying architecture and performance of OLAP systems, especially in large-scale environments. It's valuable for gaining a deeper technical understanding of how these systems work. is an excellent resource for data engineers and architects.
Presents both relational and dimensional modeling techniques for data warehouse design. It offers a comparison and discusses when to use each approach. It provides a broader perspective on data warehouse design beyond just dimensional modeling, which is helpful for a comprehensive understanding. This book is valuable for understanding different design paradigms.
Focuses on data pipelines, which are essential for the ETL process in data warehousing and OLAP. It provides practical guidance on designing and building effective data pipelines. It's a useful reference for understanding the data flow and processing aspects of populating an OLAP system. This book is helpful for data engineers and ETL developers.
For more information about how these books relate to this course, visit:
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