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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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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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The Future of AI in the Enterprise

Jet Global

Enterprise businesses cannot survive without robust data warehousing—data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.

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

AWS Big Data

Data repository services Amazon Redshift is the recommended data storage service for OLAP (Online Analytical Processing) workloads such as cloud data warehouses, data marts, and other analytical data stores. Data subscription and access is fully managed with this service.

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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. A successful migration can be accomplished through proactive planning, continuous monitoring, and performance fine-tuning, thereby aligning with and delivering on business objectives.

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The Future of AI in the Enterprise

Jet Global

Enterprise businesses cannot survive without robust data warehousing—data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.