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

Jet Global

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. Online analytical processing (OLAP), which enabled users to quickly and easily view data along different dimensions, was coming of age.

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

AWS Big Data

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. A data hub contains data at multiple levels of granularity and is often not integrated.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI). Vijay Bagur is a Sr. Technical Account Manager.

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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. Next-generation cloud OLAP database engines are expected to bring significant advancements.

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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.

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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.