Jet Global

NOVEMBER 5, 2020

For more powerful, multidimensional OLAP-style reporting, however, it falls short. OLAP reporting has traditionally relied on a data warehouse. OLAP reporting based on a data warehouse model is a well-proven solution for companies with robust reporting requirements. Option 3: Azure Data Lakes.

Data Lake 98

Data Lake OLAP Data Warehouse Unstructured Data 98

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

Sisense

NOVEMBER 12, 2020

While the architecture of traditional data warehouses and cloud data warehouses does differ, the ways in which data professionals interact with them (via SQL or SQL-like languages) is roughly the same. The primary differentiator is the data workload they serve.

Data Warehouse 52

Data Warehouse Data Lake OLAP Data-driven 52

FineReport

JUNE 24, 2021

Business intelligence solutions are a whole combination of technology and strategy, used to handle the existing data of the enterprises effectively. Technicals such as data warehouse, online analytical processing (OLAP) tools, and data mining are often binding. Data preparation and data processing.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

Domino Data Lab

JULY 2, 2019

To build a SQL query, one must describe the data sources involved and the high-level operations (SELECT, JOIN, WHERE, etc.) 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.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105