Remove Data Lake Remove Events Remove Online Analytical Processing
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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

This post provides guidance on how to build scalable analytical solutions for gaming industry use cases using Amazon Redshift Serverless. This post also discusses the art of the possible with newer innovations in AWS services around streaming, machine learning (ML), data sharing, and serverless capabilities.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. Deriving business insights by identifying year-on-year sales growth is an example of an online analytical processing (OLAP) query.

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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. It organizes data into user-friendly structures, aligning with shared business definitions, ensuring users can analyze data with ease despite changes.

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

AWS Big Data

StarTree overview One of the founders of Apache Pinot, Kishore Gopalakrishna, launched StarTree to equip organizations globally with the power of real-time data and build a fully managed platform for real-time analytics.