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

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.

OLAP 56

OLAP Metadata Online Analytical Processing Data Quality 56

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.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

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.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

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.

Metadata 119

Metadata Cost-Benefit OLAP Modeling 119

AWS Big Data

MARCH 7, 2023

ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar. Unlike ingestion processes, data can be transformed as per business rules before loading. You can apply technical or business data quality rules and load raw data as well.

Analytics 74

Analytics Data Warehouse Data Lake Metadata 74

Smarten

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40