Why Enterprises Need Dedicated Data Synchronization Engines

In the early days of enterprise data platforms, the main focus was storing more data and leveraging computing power to analyze it. This led to the development of data warehouses, data lakes, and various computing engines as core components of data architecture. However, with the emergence of real-time business demands and AI applications, enterprises now face a new challenge: reliably and continuously flowing data.

A modern enterprise data environment typically includes multiple systems like operational databases, messaging systems, real-time computing platforms, data lakes, and analytical databases. Data generated by business operations must continuously move between these systems to support real-time analytics, decision-making, and intelligent applications. Data synchronization has transformed from a simple extraction task into a crucial capability connecting the entire data ecosystem.

Traditional synchronization methods initially relied on scripts, but as data sources and synchronization tasks grew, these scripts became hard to maintain, evolving into complex data pipelines. Enterprises later attempted to use computing engines like Flink and Spark for synchronization, but these frameworks were primarily designed for data processing, not long-running, reliable data movement.

Dedicated execution engines are now sought after to address this issue. Scripts initially worked for small data volumes, reading data from operational databases, performing transformations, and writing results into data warehouses. However, as data sources and target systems increased, script-based approaches faced challenges. Different data sources had varying connection requirements, and different business scenarios needed distinct data processing methods.

Enterprises moved beyond a few synchronization jobs to a multitude of data programs lacking unified management.

Scripts struggled in production with failures, needing to track processed data, resend records, and prevent duplicate writes. Enterprises began adopting distributed computing frameworks for synchronization reliability, but computing engines and synchronization engines serve different purposes. Computing engines focus on complex data processing, while synchronization systems ensure reliable and efficient data movement from source to sink.

Running simple data transfer pipelines on a full computing framework adds unnecessary resource consumption and operational complexity for large-scale synchronization workloads.

The ST Zeta Engine is designed specifically for data synchronization scenarios, adopting a decoupled architecture separating connectors from runtime. Connectors handle data connectivity with diverse systems, while the Zeta Engine manages the execution process, including scheduling, execution, state management, and failure recovery.

This design allows new data sources to be integrated without redesigning the entire synchronization workflow. The Zeta Engine's distributed execution model breaks large synchronization jobs into parallel tasks executed by multiple workers, optimizing resource utilization and scaling horizontally as data volumes grow.