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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. Identifying best practices and benefits In the realm of OLAP, AI’s role is increasingly important.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. This blog takes you on a journey into the world of Uber’s analytics and the critical role that Presto, the open source SQL query engine, plays in driving their success.

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Data Model Development Using Jinja

Sisense

Every aspect of analytics is powered by a data model. A data model presents a “single source of truth” that all analytics queries are based on, from internal reports and insights embedded into applications to the data underlying AI algorithms and much more. The benefits of DBT with Jinja. DBT + Sisense: A powerful combination.

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Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Sisense

The traditional data warehouses solved the problem of processing and synthesizing large data volumes, but they presented new challenges for the analytics process. Cloud data warehouses took the benefits of the cloud and applied them to data warehouses — bringing massive parallel processing to data teams of all sizes.

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How to Build a Performant Data Warehouse in Redshift

Sisense

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. Redshift, like BigQuery and Snowflake, is a cloud-based distributed multi-parallel processing (MPP) database, built for big data sets and complex analytical workflows.