IBM Big Data Hub

SEPTEMBER 25, 2023

They set up a couple of clusters and began processing queries at a much faster speed than anything they had experienced with Apache Hive, a distributed data warehouse system, on their data lake. There may be inaccuracy because of sampling, but it allows users to discover new viewpoints within the data.

OLAP 86

OLAP Data Lake Data-driven Online Analytical Processing 86

Sisense

FEBRUARY 16, 2021

Data warehouses have become intensely important in the modern business world. For many organizations, it’s not uncommon for all their data to be extracted, loaded unchanged into data warehouses, and then transformed via cleaning, merging, aggregation, etc. OLTP does not hold historical data, only current data.

Modeling 52

Modeling OLAP Data Warehouse Cost-Benefit 52

Jet Global

OCTOBER 22, 2020

OLAP Cubes vs. Tabular Models. Let’s begin with an overview of how data analytics works for most business applications. This leads to the second option, which is a data warehouse. In this scenario, data are periodically queried from the source transactional system. The first is an OLAP model.

Modeling 52

Modeling OLAP Reporting Sales 52

Jet Global

JANUARY 15, 2021

Virtually every ERP implementation or upgrade requires substantial effort to design, build, or modify, and then to test reports. In many respects, it is more akin to some of the very complex data warehousing and OLAP tools of the past–perhaps with an even steeper learning curve. Reporting as a Key Cost-driver.

Cost-Benefit 72

Cost-Benefit Data Lake Reporting OLAP 72

Domino Data Lab

JULY 2, 2019

Of course, if you use several different data management frameworks within your data science workflows—as just about everybody does these days—much of that RDBMS magic vanishes in a puff of smoke. Some may ask: “Can’t we all just go back to the glory days of business intelligence, OLAP, and enterprise data warehouses?”

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

Jen Stirrup

OCTOBER 30, 2022

One particular technology which is good for summarising and aggregating data is called OLAP (On Line Analytical Processing). Historical analytics can help to support the marketing process, which can also be augmented by predictive analytics, alternatively known as data mining, which can help to identify patterns in customer behavior.

Data mining 52

Data mining Predictive Analytics Cost-Benefit OLAP 52

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

Jet Global

MAY 1, 2023

These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. Quickly link all your data from Amazon Redshift, MongoDB, Hadoop, Snowflake, Apache Solr, Elasticsearch, Impala, and more.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52