Remove Analytics Remove Machine Learning Remove Online Analytical Processing Remove Structured Data
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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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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible. Jet Analytics provides for a one-to-one match with source tables and a set of pre-defined data entities out of the box.

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How Aura from Unity revolutionized their big data pipeline with Amazon Redshift Serverless

AWS Big Data

Amazon Redshift is a recommended service for online analytical processing (OLAP) workloads such as cloud data warehouses, data marts, and other analytical data stores. These campaigns are optimized by using an AI-based bid process that requires running hundreds of analytical queries per campaign.

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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.