Octopai
JANUARY 21, 2020
This is how the Online Analytical Processing (OLAP) cube was born, which you might call one of the grooviest BI inventions developed in the 70s. OLAP cube is designed as a solution to pre-compute totals and subtotals when the database server is idle. The OLAP cube makes reading data across multiple dimensions manageable.
IBM Big Data Hub
DECEMBER 7, 2023
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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Ontotext
JUNE 10, 2021
To handle such scenarios you need a transalytical graph database – a database engine that can deal with both frequent updates (OLTP workload) as well as with graph analytics (OLAP). They should be able to continuously integrate data across multiple internal systems and link it to data from external sources.
AWS Big Data
AUGUST 1, 2024
Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI). The data warehouse is highly business critical with minimal allowable downtime.
Jet Global
MAY 28, 2019
The good news is, nowadays you can find business intelligence solutions with pre-built data warehouses to eliminate complexity, significantly reduce cost, and decrease risk. Enhancing a Data Warehouse with Cubes. A cube is a multi-dimensional section of data built from tables in your data warehouse.
BizAcuity
APRIL 1, 2023
Several large organizations have faltered on different stages of BI implementation, from poor data quality to the inability to scale due to larger volumes of data and extremely complex BI architecture. Without a strong BI infrastructure, it can be difficult to effectively collect, store, and analyze data.
BizAcuity
JANUARY 31, 2023
Several large organizations have faltered on different stages of BI implementation, from poor data quality to the inability to scale due to larger volumes of data and extremely complex BI architecture. Without a strong BI infrastructure, it can be difficult to effectively collect, store, and analyze data.
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