Data warehouse products
An ODS is used between databases, and the data warehouse will perform the analytical processing and data cleaning.
The data lake will perform all analysis and data cleaning 'in-house. '.
Data warehouse products
Whereas data warehouses and data lakes exist primarily to support analytics and machine learning, data hubs enable data integration, sharing and governance.
Accordingly, businesses are increasingly applying this architecture as a focal point of mediation and governance..
What is difference between data lake and data warehouse?
Data lakes primarily store raw, unprocessed data, often including multimedia files, log files, and other very large files, while data warehouses mostly store structured, processed, and refined data that tends to be text and numbers..
What is the difference between a data warehouse and a data lake?
Data lakes primarily store raw, unprocessed data, often including multimedia files, log files, and other very large files, while data warehouses mostly store structured, processed, and refined data that tends to be text and numbers..
What is the difference between data lake and data factory?
A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis.
Azure Data Factory is a managed cloud service built for extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects..
What is the difference between data warehouse and data lake and data mesh?
Unlike data warehouses and data lakes, the data mesh decentralizes data ownership.
Companies that adopt data mesh architecture view data as a product, and this view empowers domain teams (typically a business department like marketing) to own their own data pipelines..
