Jet Global
SEPTEMBER 4, 2020
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.
AWS Big Data
MARCH 7, 2023
This post also discusses the art of the possible with newer innovations in AWS services around streaming, machine learning (ML), data sharing, and serverless capabilities. This service is the core of this reference architecture on AWS and can address most analytical needs out of the box.
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CIO Business Intelligence
JANUARY 20, 2023
BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.
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. Next-generation cloud OLAP database engines are expected to bring significant advancements.
AWS Big Data
APRIL 4, 2024
Amazon Redshift is a recommended service for online analytical processing (OLAP) workloads such as cloud data warehouses, data marts, and other analytical data stores.
AWS Big Data
JUNE 21, 2023
Deriving business insights by identifying year-on-year sales growth is an example of an online analytical processing (OLAP) query. For example, the same dataset could be used to build machine learning (ML) models to identify trends and predict sales. These types of queries are suited for a data warehouse.
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