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

Jet Global

Consultants and developers familiar with the AX data model could query the database using any number of different tools, including a myriad of different report writers. Data Entities. The SQL query language used to extract data for reporting could also potentially be used to insert, update, or delete records from the database.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

Amazon DynamoDB is a fully managed NoSQL service that delivers single-digit millisecond performance at any scale. Deriving business insights by identifying year-on-year sales growth is an example of an online analytical processing (OLAP) query. These types of queries are suited for a data warehouse.

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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. These campaigns are optimized by using an AI-based bid process that requires running hundreds of analytical queries per campaign.

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

AWS Big Data

Decoupled and scalable – Serverless, auto scaled, and fully managed services are preferred over manually managed services. Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. They should also provide optimal performance with low or no tuning.

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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. This siloed approach often resulted in data redundancy and complexity, hampering integration with other business systems.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

Uber’s DNA as an analytics company At its core, Uber’s business model is deceptively simple: connect a customer at point A to their destination at point B. With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price.

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