Jet Global

SEPTEMBER 4, 2020

Its solution was to replicate data from the production database, using data entities, into a traditional relational database. Microsoft referred to this approach as “bring your own database” (BYOD). There is an established body of practice around creating, managing, and accessing OLAP data (known as “cubes”). Data Lakes.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

AWS Big Data

MARCH 7, 2023

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. 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.

Analytics 94

Analytics Data Warehouse Data Lake Metadata 94

AWS Big Data

JUNE 21, 2023

A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. Deriving business insights by identifying year-on-year sales growth is an example of an online analytical processing (OLAP) query.

Data Warehouse 104

Data Warehouse Data Lake OLAP Cost-Benefit 104

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 108

OLAP Analytics Visualization Dashboards 108

AWS Big Data

AUGUST 1, 2024

The data warehouse is highly business critical with minimal allowable downtime. For an example, refer to How JPMorgan Chase built a data mesh architecture to drive significant value to enhance their enterprise data platform. Organic strategy – This strategy uses a lift and shift data schema using migration tools.

Data Warehouse 113

Data Warehouse KPI Optimization Cost-Benefit 113