Remove Business Objectives Remove Online Analytical Processing Remove Testing
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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

The data warehouse is highly business critical with minimal allowable downtime. Thorough testing and performance optimization will facilitate a smooth transition with minimal disruption to end-users, fostering exceptional user experiences and satisfaction. Additional considerations – Factor in additional tasks beyond schema conversion.

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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Each data entity provides an abstract representation of business objects within the database, such as, customers, general ledger accounts, or purchase orders. 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.

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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

Like Pinot, StarTree addresses the need for a low-latency, high-concurrency, real-time online analytical processing (OLAP) solution. Developers interested in learning more about managed Pinot can deploy real-time analytics with StarTree to test it out or join a session with StarTrees head of product.