Open-Source ETL Tools have gained popularity because they are work-in-progress tools that do not provide many features in other ETL Tools but get regularly updated. The given figure highlights the stages of the ETL process: 4 Key Features of Open-Source ETL Tools Loading: Storing the transformed data into a destination, normally a Data Warehouse, to support analysis and gain valuable insights.Transformation: Converting extracted data into a standardized format so that it can be better understood by a Data Warehouse or a BI (Business Intelligence) tool.Extraction: Unifying Structured and Unstructured data from a diverse set of data sources such as Databases, SaaS applications, files, CRMs, etc.It is a three-step process that contains: Modern Data Analytics Stack leverages ETL to extract data from different sources like Social Media Platforms, Email/SMS services, Customer Service Platforms, and Surveys, transform it and load it into a Data Warehouse to gain valuable and actionable insights. Working knowledge of Open-Source and Cloud Environments.Working knowledge of SaaS applications.They are available for free with no upfront cost, come with a simple and accurate UI, and offer similar functionalities as other ETL Tools. Today, Open-Source ETL tools have gained more popularity than other tools. Ultimately, they must ensure that their ETL tool works smoothly and makes data easily available to data analysts and business teams. They have to critically evaluate available ETL tools, check for transparent pricing, and find one that best fits their requirements. 4) Smaller Budgets & Fewer Complex Requirementsįor data engineers, finding the right ETL (Extract, Transform and Load) tool becomes a challenge.4 Key Features of Open-Source ETL Tools.

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