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Simplify Online Analytical Processing (OLAP) queries in Amazon Redshift using new SQL constructs such as ROLLUP, CUBE, and GROUPING SETS

AWS Big Data

Solution overview Online Analytical Processing (OLAP) is an effective tool for today’s data and business analysts. An analyst can use OLAP aggregations to analyze buying patterns by grouping customers by demographic, geographic, and psychographic data, and then summarizing the data to look for trends.

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Build a real-time analytics solution with Apache Pinot on AWS

AWS Big Data

Online Analytical Processing (OLAP) is crucial in modern data-driven apps, acting as an abstraction layer connecting raw data to users for efficient analysis. OLAP combines data from various data sources and aggregates and groups them as business terms and KPIs.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

Data repository services Amazon Redshift is the recommended data storage service for OLAP (Online Analytical Processing) workloads such as cloud data warehouses, data marts, and other analytical data stores. Data subscription and access is fully managed with this service. Refer to the respective service documentation for further details.

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Deploy real-time analytics with StarTree for managed Apache Pinot on AWS

AWS Big Data

Like Pinot, StarTree addresses the need for a low-latency, high-concurrency, real-time online analytical processing (OLAP) solution. This post is cowritten with Mayank Shrivastava and Barkha Herman from StarTree. Conclusion In this post, we presented StarTree as a managed solution on AWS for teams seeking the advantages of Apache Pinot.