Why Enterprises Need Dedicated Data Synchronization Engines
In the past, when enterprises built data platforms, their focus was usually concentrated on two areas: how to store more data and how to leverage computing capabilities to analyze that data. As a result, data warehouses, data lakes, and various computing engines became the core components driving the evolution of data architectures.
However, with the rise of real-time business requirements and AI applications, enterprises are facing a new challenge that is no longer simply “how to analyze data,” but rather “how to keep data flowing reliably and continuously.”
Today, a typical enterprise data environment includes multiple systems such as operational databases, messaging systems, real-time computing platforms, data lakes, and analytical databases. Data generated by business operations needs to continuously move from one system to another to support real-time analytics, business decisions, and intelligent applications.
Data synchronization has gradually evolved from a simple ETL extraction task into a fundamental capability that...
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