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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. An analyst can use OLAP aggregations to analyze buying patterns by grouping customers by demographic, geographic, and psychographic data, and then summarizing the data to look for trends.

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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. OLAP databases excel at queries that require large table scans (e.g.

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Octopai Users Do More with Enhanced Data Lineage Capabilities + Complete BI Data Catalog

Octopai

Download upper and column-to-column lineage to Excel/CSV in order to document, verify development and change requests. When looking to change ETLs for instance, or understand or design an ETL process, it is now possible to download the lineage to Excel in order to document changes as part of the change management process, do sign offs etc.

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Welcome To The Digital Age: BI Meets Social Media

Smart Data Collective

In the 1990s, OLAP tools allowed multidimensional data analysis. This integration provides a comprehensive view of their online presence and audience engagement, enabling businesses to detect trends, track key performance indicators, and measure the impact of their social media efforts. Let’s break it down for you.

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The Future of AI in the Enterprise

Jet Global

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions.

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AI vs. BI for Business, What Do You Need?

Jet Global

Data modeling can be performed at the conceptual (high-level, related to business objectives), logical (mapping to each business function), and physical (how the actual dimensions, measures, and hierarchies are related within a data cube). What are some of the core components of business intelligence?

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

AWS Big Data

Nonetheless, many of the same customers using DynamoDB would also like to be able to perform aggregations and ad hoc queries against their data to measure important KPIs that are pertinent to their business. Deriving business insights by identifying year-on-year sales growth is an example of an online analytical processing (OLAP) query.