Remove Data Science Remove Metadata 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

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

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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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How to Build a Performant Data Warehouse in Redshift

Sisense

Fundamentally they are different than transactional databases we’ve seen in the past, and before we jump into how to build your data warehouse, it’s important to understand that distinction. First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing).

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

AWS Big Data

StarTrees automatic data ingestion framework is ideal for enterprise workloads because it improves scalability and reduces the data maintenance complexity often found in open source Pinot deployments. The data is then modelled to help you organize and structure the data fetched from the selected data source into Pinot tables.