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 Lakes.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

AWS Big Data

AUGUST 6, 2024

Online Analytical Processing (OLAP) is crucial in modern data-driven apps, acting as an abstraction layer connecting raw data to users for efficient analysis. It organizes data into user-friendly structures, aligning with shared business definitions, ensuring users can analyze data with ease despite changes.

OLAP 83

OLAP Analytics Visualization Dashboards 83

BizAcuity

AUGUST 1, 2022

TIBCO Jaspersoft offers a complete BI suite that includes reporting, online analytical processing (OLAP), visual analytics , and data integration. The web-scale platform enables users to share interactive dashboards and data from a single page with individuals across the enterprise. Online Analytical Processing (OLAP).

Reporting 52

Reporting OLAP Online Analytical Processing Dashboards 52

Jet Global

OCTOBER 7, 2020

For anyone that needs to develop custom reports and dashboards, it all begins with understanding data entities. What Are Data Entities? In the future, customers will be able to deploy Data Entities and replicate transactional tables in an Azure Data Lake. Microsoft is currently developing this capability.

Data Warehouse 90

Data Warehouse OLAP Reporting Finance 90

Jet Global

OCTOBER 22, 2020

Power BI provides users with some very nice dashboarding and reporting capabilities. 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. The first is an OLAP model.

Modeling 52

Modeling OLAP Reporting Sales 52

AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 67

Analytics Data Warehouse Data Lake Metadata 67

BizAcuity

APRIL 1, 2023

The BI infrastructure: This includes designing and implementing data warehouses, data lakes, data marts, and OLAP cubes along with data mining, and modeling. 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