Remove Analytics Remove Big Data Remove Data Lake Remove Online Analytical Processing
article thumbnail

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. Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. Data hubs and data lakes can coexist in an organization, complementing each other.

article thumbnail

TIBCO JasperSoft for BI and Reporting

BizAcuity

TIBCO Jaspersoft offers a complete BI suite that includes reporting, online analytical processing (OLAP), visual analytics , and data integration. The web-scale platform enables users to share interactive dashboards and data from a single page with individuals across the enterprise. Data Security.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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. Early OLAP systems were separate, specialized databases with unique data storage structures and query languages.

OLAP 58
article thumbnail

Master Your Power BI Environment with Tabular Models

Jet Global

The world of business analytics is evolving rapidly. As ERP moves to the cloud, software vendors are developing more sophisticated, interconnected ways of gathering, organizing, and analyzing business data. Let’s begin with an overview of how data analytics works for most business applications. The first is an OLAP model.

article thumbnail

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.

article thumbnail

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.

article thumbnail

Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Every day, millions of riders use the Uber app, unwittingly contributing to a complex web of data-driven decisions. Consider the magnitude of Uber’s footprint.

OLAP 87