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
This section delves into the foundational aspects of Online Analytical Processing, exploring its definition, historical context, and core objectives in the realm of data analysis. Understanding these fundamentals is key to appreciating OLAP's significance in modern business intelligence and its power to transform raw data into actionable insights.
Definition and Purpose of OLAP
Online Analytical Processing (OLAP) is a computing method designed to allow users to easily and selectively extract and query data to analyze it from different points of view. Its primary purpose is to support business intelligence activities by enabling multidimensional analysis of large volumes of data, often stored in a data warehouse or data lake. Unlike Online Transaction Processing (OLTP) systems that are optimized for managing and recording daily operational transactions, OLAP systems are designed for complex queries and analysis to identify trends, patterns, and insights that inform strategic decisions.
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Find a path to becoming a OLAP. Learn more at:
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Reading list
We've selected 14 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.
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.
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.
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.
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.
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.
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.
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.
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.
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 Google BigQuery, a modern cloud-based data warehouse that supports analytical queries similar to OLAP. It provides practical knowledge of using a contemporary platform for data warehousing and analytics. It's relevant for understanding how OLAP concepts are implemented in cloud environments. This book good resource for learning about specific cloud data warehousing technologies.
Understanding the internal workings of databases is beneficial for optimizing OLAP system performance. provides a detailed look at how data systems function internally, covering topics like storage engines and indexing. It's suitable for those seeking a deep technical understanding of the underlying technologies supporting OLAP. This book is for advanced readers interested in database architecture.
Provides a high-level, accessible introduction to business intelligence concepts, including data warehousing and OLAP. It's suitable for beginners, high school students, or those who need a non-technical overview of the topic. It helps in gaining a broad understanding of what BI and OLAP are and their purpose in an organization. This book good first read for newcomers.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/qjfgf5/ola
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