![]() The following graphic shows the OLAP cube for sales data in multiple dimensions - by region, by quarter and by product: This historical, aggregated data for OLAP is usually stored in a star schema or snowflake schema. For example, while the top layer of the cube might organize sales by region, data analysts can also “drill-down” into layers for sales by state/province, city and/or specific stores. The OLAP cube extends the row-by-column format of a traditional relational database schema and adds layers for other data dimensions. For example, sales figures might have several dimensions related to region, time of year, product models and more. What’s a data dimension? It’s simply one element of a particular dataset. The core of most OLAP databases is the OLAP cube, which allows you to quickly query, report on and analyze multidimensional data. OLAP is ideal for data mining, business intelligence and complex analytical calculations, as well as business reporting functions like financial analysis, budgeting and sales forecasting. Typically, this data is from a data warehouse, data mart or some other centralized data store. ![]() Online analytical processing (OLAP) is a system for performing multi-dimensional analysis at high speeds on large volumes of data. ![]() The question isn’t which to choose, but how to make the best use of both processing types for your situation. However, there are meaningful ways to use both systems to solve data problems. The main difference is that one uses data to gain valuable insights, while the other is purely operational. ![]() Within the data science field, there are two types of data processing systems: online analytical processing (OLAP) and online transaction processing (OLTP). You can see this data at work in new service offerings (such as ride-sharing apps) as well as the powerhouse systems that drive retail (both e-commerce and in-store transactions). We live in a data-driven age, where the organizations that use data to make smarter decisions and respond faster to changing needs are more likely to come out on top. These terms are often confused for one another, so what are their key differences and how do you choose the right one for your situation? ![]()
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