The race to design a fully autonomous system is fostering innovations in the development of modern data systems. Developers are striving to create data ecosystems that are self-correcting and have minimal downtime so as to manage data movement effectively within their organizations.
Due to such a drive for automation, the use of self-healing data pipelines has increased rapidly. A conventional data pipeline consists of data processing elements connected in a relevant manner to move data between two different data systems. For example, extracting data from IoT devices, such as temperature sensors, and loading it into an analytical database for monitoring forms a simple ELT data pipeline. Such traditional pipelines are prone to limitations, including downtime, crashes, low scalability, and excessive monitoring overhead.