ETL Overview

Extract Transform Load (ETL) generally involves the transferral of data from one (or more) sources to another, including a certain amount of 'transformation' of the data so that it adheres to the required protocols of the destination system.

Some different types of ETL implementation are:

• Extract data from a source, run a simple transformation (e.g. set data type) in Tray.io, then load to a database of your choice

• Extract data from a source, store it in an intermediary (such as AWS EMR) which can act as a staging environment which can perform more 'heavyweight' transformations, then load to a database of your choice

• Extract data from a source, load straight to a database of your choice, then perform transformations directly in the new database environment (this is more of an ELT implementation)

Some common SaaS sources used in ELT are:

• CRM systems e.g. Salesforce

• Automated marketing systems e.g. Marketo, Outreach

• HR systems (employee and payroll info etc.) e.g. BambooHR

• Project management systems e.g. Asana, Workfront

Most commonly the data is finally loaded into databases / data warehouse solutions, i.e.:

• MySQL, PostgreSQL, MSSQL

• BigQuery, Snowflake, Redshift