ETL vs data pipelines: What are they and how do they work?

Data pipelines and ETL (Extract, Transform, Load) are crucial in today's dynamic business landscape. Both serve to facilitate the seamless transfer of data between systems, but they differ in their approach and focus. Data pipelines handle real-time or near real-time data processing and can integrate data from diverse sources into a single repository. In contrast, ETL focuses on batch processing and transforming data into specific formats for analysis. The choice between data pipelines and ETL depends on the organization's specific requirements. Data pipelines are suitable for organizations that require real-time or near real-time data processing or need to integrate data from diverse sources. On the other hand, ETL is well-suited for organizations dealing with large data volumes or requiring data transformation into specific formats. Various tools and technologies are available for building data pipelines and ETL processes, such as Apache NiFi, Apache Kafka, AWS Glue, Google Cloud Dataflow, Apache Airflow, Talend, and Informatica. The selection of the right tool depends on factors like data volume, complexity, velocity, features, and cost.