ETL

Much has changed since ETL was first introduced. However, the core tenets of ETL remain unchanged. The data that is pulled from different sources must be cleaned, transformed, and stored in a data warehouse. The stored data is then analyzed to drive insights. 

An ETL data pipeline consists of three processes:

ETL tools refer to software specifically developed to facilitate ETL processes, which involve extracting data from various sources, cleaning and ensuring data consistency and quality, and then consolidating the gathered information into data warehouses. ETL tools streamline data management strategies and enhance data quality by providing a standardized structure for data intake, sharing, and storage.

ETL worked well when data sources were limited - in other words, when there was much less data out there. The need for more comprehensive and accurate operational analytics sparked the emergence of reverse ETL or rETL tools which extract data from the warehouse, transform it to make it sync with the target destination’s API, and load it into the target app. The target app can be CRM, marketing automation, or promotion software. 

ETL and rETL sit on opposite sides of a data pipeline, powering data integration and data activation respectively. 

Data is the lifeblood of modern businesses. According to McKinsey, retailers need a robust data lake, an analytics environment, and executional tools to achieve personalization at scale. As a powerful component of a modern tech stack, rETL enables businesses to: