Remove OLAP Remove Online Analytical Processing Remove Risk Remove Strategy
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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. When using multiple statements to update or insert data, there is a risk of inconsistencies between the different operations. Amazon Redshift has recently added many SQL commands and expressions.

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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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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. As AI techniques continue to evolve, innovative applications in the OLAP domain are anticipated.

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How to Build a Performant Data Warehouse in Redshift

Sisense

OLTP vs OLAP. First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). An OLAP database is best for situations where you read from the database more often than you write to it. Redshift is a type of OLAP database.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

They stood up a file-based data lake alongside their analytical database. While this side-by-side strategy enabled data capture, they quickly discovered that the data lake worked well for long-running queries, but it was not fast enough to support the near-real time engagement necessary to maintain a competitive advantage.

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