Current Sessions | Addressing Streaming ETL Pipelines Challenges: Delving into Flink CDC

Data freshness significantly impacts the value of data insights, particularly for business data housed within databases. Establishing a more real-time synchronization pipeline is crucial for deriving actionable insights from this data. However, constructing such a pipeline encounters various challenges. Common issues include frequent table schema changes in the upstream database and the need to add or delete tables according to evolving business requirements. Moreover, flexibly scaling resources during historical reading and log reading stages proves difficult, while synchronizing multiple tables or the entire database consumes substantial resources. Additionally, constructing job workflows using the DataStream API poses its own complexities.