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.

OLAP 59

OLAP Slice and Dice Cost-Benefit Data Warehouse 59

IBM Big Data Hub

SEPTEMBER 25, 2023

Uber’s DNA as an analytics company At its core, Uber’s business model is deceptively simple: connect a customer at point A to their destination at point B. With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price.

OLAP 88

OLAP Data Lake Data-driven Snapshot 88

FineReport

JUNE 24, 2021

However, along with the diffusion of digital technology, the amount of data is getting larger and larger, and data collection and cleaning work have become more and more time-consuming. Business intelligence solutions are a whole combination of technology and strategy, used to handle the existing data of the enterprises effectively.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

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. We explore why Aura chose this solution and what technological challenges it helped solve.

Big Data 68

Big Data Data Warehouse Advertising OLAP 68

Sisense

SEPTEMBER 3, 2019

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.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

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.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

Jet Global

APRIL 20, 2020

Finance leaders that were quick to recognize the new paradigm got a head start, using the new technology to make their organizations more efficient and profitable. Over the past few decades, however, technology has been closing that gap. Today’s technology takes this evolution a step further.

Business Intelligence 78

Business Intelligence Finance Data Warehouse OLAP 78