OLAP in Data Warehouses By Teresa Wingfield September 27, 2021 Decisions within an organization are made in three possible ways. One way is based on the experience of the individual making the decision. The second way is based on analytics. The third way is based on a combination of one and two, knowledgeable experience and analytics. Based on experience or expertise in a subject, we can often make very well-informed decisions and obtain desired outcomes. To enhance our expertise, we can use metrics based on factual data. The data may reveal information that was absent from our expert opinion. Of course, using both expert opinion and analytics is the best approach for solving problems or thinking strategically for the business. The use of data warehouses is one way to gather analytics to improve decision making within an organization. Use of OLAP in Data Warehouses Online Analytical Processing in Data Warehouses allows rapid calculation of analytical business information using metrics for modeling, planning, or forecasting. OLAP is the foundation of analytics that support many business applications for reporting, simulation models, information-to-knowledge transitions, and trend and performance management. Data contained in a data warehouse is often used for OLAP. OLAP solutions enhance a data warehouse with aggregate data and business calculations. OLAP vs. OLTP Online transaction processing (OLTP) is designed to handle transactions by getting data organized and written to a database as quickly as possible. OLAP, on the other hand, focuses on reading data as quickly as possible to service business analytics. OLTP data is sent to the OLAP data warehouse for computations so as not to affect the real-time online users of the OLTP database that often number in the thousands. OLAP works with large amounts of data stored in a data warehouse. This data is not real-time but is synced to be as relevant as possible to the decision it will support. Techniques such as data mining and big data analytics are used to gather intelligence from all the data stored in the data warehouse. Processing as such for OLAP data is very performance intensive. An online user would experience a degradation in the application’s response time if accessing real-time data. When to use OLAP – when you need help with decisions to analyze the business. Data warehouses are typically used by 100s of people at the same time. What is OLAP Cube in Data Warehouses An OLAP cube is a data structure in the data warehouse that is optimized for improving the performance of data analysis. An OLAP cube is sometimes referred to as a hypercube. OLAP cubes contain multidimensional data and information from different unrelated sources for logical and orderly analysis. The cube could incorporate different data types from multiple data sources that have been transformed. Subsequent analytical operations are performed on the data to create relationships with the other acquired data, including “slicing and dicing” the data to fit specific criteria to enable additional perspectives for decision support. One of the challenges with OLAP is that it requires the use of complex schemas to implement and administer the technology. Managing and administering the cube is very time consuming, but it provides excellent value to the organization when done. Use cases of OLAP in a Data Warehouse How to use OLAP becomes a capability based on the creativity and expertise of the user. With all the data and information available in the data warehouse, including manipulating and viewing the data from many different perspectives, OLAP can become a critical capability needed by the business. OLAP in a data warehouse can help with: Planning Budgeting Reporting Various analysis Asking “what if” questions Business modeling Creating data relationships that did not exist OLAP is used to support the use of data in any way experts see fit for the decision that needs to be made for the organization. Many business applications can take advantage of OLAP capabilities, including different roles in the organization, viewing data and information from unique perspectives to enable dynamic decision making. Actian can help OLAP users looking to simplify the BI life cycle. The Actian Vector analytics database provides a viable alternative to OLAP Cubes with its ground-breaking technology, superior performance and in-database analytic capabilities. About Teresa Wingfield Teresa Wingfield is Director of Product Marketing at Actian where she is responsible for communicating the unique value that the Avalanche Cloud Data Platform delivers, including proven data integration, data management and data analytics. She enjoys applying her extensive knowledge in these areas to help customers find solutions that will help them achieve long-lasting success. Teresa brings a 20-year track record of increasing revenue and awareness for analytics, security, and cloud solutions. Prior to Actian, Teresa managed product marketing at industry-leading companies such as Cisco, McAfee, and VMware. She was also Datameer’s first VP of Marketing for big data analytics built on Hadoop, and has served as VP of Research at Giga Information Group, acquired by Forrester, providing strategic advisory services for data warehousing and analytics. Teresa holds graduate degrees in management from the MIT Sloan School of Management and software engineering from Harvard University.