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BI Cubed: Data Lineage on OLAP Anyone?

Octopai

This is how the Online Analytical Processing (OLAP) cube was born, which you might call one of the grooviest BI inventions developed in the 70s. OLAP cube is designed as a solution to pre-compute totals and subtotals when the database server is idle. The OLAP cube makes reading data across multiple dimensions manageable.

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How OLAP and AI can enable better business

IBM Big Data Hub

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. As AI techniques continue to evolve, innovative applications in the OLAP domain are anticipated.

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How to Build a Performant Data Warehouse in Redshift

Sisense

OLTP vs OLAP. First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). An OLAP database is best for situations where you read from the database more often than you write to it. OLAP databases excel at queries that require large table scans (e.g.

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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Online analytical processing (OLAP), which enabled users to quickly and easily view data along different dimensions, was coming of age. The challenge with OLAP, however, is that it requires intensive processing power to aggregate data according to various categories or dimensions. Data warehouses have been in widespread use for years.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

Large, untested workloads run the risk of hogging all the resources. As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries. In some cases, the queries run out of memory and do not complete.

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Use the new SQL commands MERGE and QUALIFY to implement and validate change data capture in Amazon Redshift

AWS Big Data

Amazon Redshift has added many features to enhance analytical processing like ROLLUP, CUBE and GROUPING SETS , which were demonstrated in the post Simplify Online Analytical Processing (OLAP) queries in Amazon Redshift using new SQL constructs such as ROLLUP, CUBE, and GROUPING SETS.

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Closing the breach window, from data to action

IBM Big Data Hub

The list of challenges is long: cloud attack surface sprawl, complex application environments, information overload from disparate tools, noise from false positives and low-risk events, just to name a few. For those defending against cyberthreats, things continue to get more complicated.