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Simplify Online Analytical Processing (OLAP) queries in Amazon Redshift using new SQL constructs such as ROLLUP, CUBE, and GROUPING SETS

AWS Big Data

Solution overview Online Analytical Processing (OLAP) is an effective tool for today’s data and business analysts. You can remove this filter in your test to view data for all regions. In this post, we discuss how to use these extensions to simplify your queries in Amazon Redshift.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

If the exploratory work needs to move on to testing and production, they can plan appropriately. As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries.

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Use the new SQL commands MERGE and QUALIFY to implement and validate change data capture in Amazon Redshift

AWS Big Data

Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Without the MERGE command, you needed to test the new dataset against the existing dataset using a business key. Amazon Redshift has recently added many SQL commands and expressions.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

Thorough testing and performance optimization will facilitate a smooth transition with minimal disruption to end-users, fostering exceptional user experiences and satisfaction. Depending on each migration wave and what is being done in the wave (development, testing, or performance tuning), the right people will be engaged.

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

Jet Global

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. Online analytical processing (OLAP), which enabled users to quickly and easily view data along different dimensions, was coming of age.

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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. Pinot has been tested at very large scale in large enterprises, serving over 70 LinkedIn data products , handling over 120,000 Queries Per Second (QPS), ingesting over 1.5

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Master Your Power BI Environment with Tabular Models

Jet Global

It updates a dedicated database against which you can perform reporting and analytics. That stands for “Online Analytical Processing,” and it’s a paradigm that goes back a little more than two decades, to a time when database performance and computational power were far less robust than they are today.