ETL Pipeline and Data Pipeline are two concepts growing increasingly important as businesses keep adding applications to their tech stacks. More and more data is moving between systems, and this is where Data and ETL Pipelines play a crucial role.
ETL is an acronym for Extract, Transform and Load. An ETL pipeline is a series of processes extracting data from a source, then transforming it, and finally, loading it into a destination. The source can be, for example, business systems, APIs, marketing tools, or transaction databases, and the destination can be a database, data warehouse, or a cloud-hosted database from providers like Amazon RedShift, Google BigQuery, and Snowflake.
- To centralize your company's data, pull from all your data sources into a database or data warehouse
- To move and transform data internally between different data stores
- To enrich your CRM system with additional data
ETL Pipelines are useful when there is a need to extract, transform, and load data. This is often necessary to enable deeper analytics and business intelligence. Whenever data needs to move from one place to another, and be altered in the process, and ETL Pipeline will do the job. ETL Pipelines are also helpful for data migration, for example, when new systems replace legacy applications.
In the extraction part of the ETL Pipeline, the data is sourced and extracted from different systems like CSVs, web services, social media platforms, CRMs, and other business systems. In the transformation part of the process, the data is then molded into a format that makes reporting easy. Sometimes data cleansing is also a part of this step. In the loading process, the transformed data is loaded into a centralized hub to make it easily accessible for all stakeholders.
The purpose of the ETL Pipeline is to find the right data, make it ready for reporting, and store it in a place that allows for easy access and analysis. An ETL tool will enable developers to put their focus on logic/rules, instead of having to develop the means for technical implementation. This frees up a lot of time and allows your development team to focus on work that takes the business forward, rather than developing the tools for analysis.