Over the last decade, companies adopted specialized tools for ETL, reverse ETL, observability, orchestration, governance, and activation. While these tools solved individual problems, they also introduced fragmentation across the data ecosystem, making it increasingly difficult to maintain reliability, visibility, and operational control.

The result is a modern data stack that often works in silos:
 Pipelines move data. Observability tools monitor portions of the stack. Catalogs document assets. But teams still struggle to understand the full downstream impact of data issues across analytics, applications, and AI systems.

As AI workloads continue to grow, many organizations are beginning to rethink whether disconnected tooling can support the next generation of data driven systems.