ETL / ELT vs. Data Warehouse Automation

Traditionally ETL was the term most commonly associated with such data integration projects and encompasses three procedures: 

• Extract – Exporting data from specified data sources.
• Transform – Modifying the source data (as needed), using rules, merges, lookup tables, or other conversion methods, to match the target.
• Load – Importing the resulting transformed data into a target database.

However, more recently the term “ELT” is being used as more organizations recognize that transformation does not need to be performed before loading, particularly for more modern approaches like Snowflake. and Data Vault.

Whilst ETL and ELT solutions can be helpful to data integration teams for some data movement scenarios, data warehouse automation software provides teams with much broader, far-reaching capabilities and benefits across the entire enterprise data ecosystem.

Review this comparison guide titled Modern Data Warehousing – How Data Warehouse Automation Differs from ETL and ELT Tools to learn:

• the origins of ETL and ELT tools
• what data warehouse automation is and what it provides data warehousing teams
• the key differences between data warehouse automation and ETL/ELT solutions
• the 7 scenarios where data warehouse automation provides value beyond ETL/ELT solutions
• three key reasons to choose data warehouse automation over ETL/ELT approaches

WhereScape is the perfect technology to utilize in any data integration or data warehouse project as it eliminates hand-coding and other repetitive, time-intensive tasks to fast-track data integration and data management projects. To show how automation can boost productivity and improve collaboration with the business, we have produced a guide that explains how automation adds value to data projects.

To request this guide, complete the above registration form. If you are ready to see WhereScape in action, please request a demo here.