In the early stages, many startups rely on traditional batch-processing systems that perform adequately when data volumes are low. However, as organizations grow, these batch pipelines often become inefficient - resulting in slow processing times and complex troubleshooting when issues arise. In many cases, organizations face debates over selecting the optimal batch processing tool, which can consume valuable resources without resolving the underlying performance challenges. Moreover, when failures occur, the process of tracking down issues within a complex network of scheduled jobs can be arduous, sometimes necessitating the reprocessing of entire data batches and causing operational delays.
Recent advancements in the field of Extract, Transform, Load (ETL) have led to the emergence of Zero-ETL strategies. With these developments, streaming data processing is increasingly recognized as the preferred approach from the outset, rather than as a lat