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To resolve dependency conflicts, ensure your Maven configuration is correct. For instance, if the IDE does not reflect database changes, setting paralelism to 1 might help. Step 5: Transmit Data in JSON Format to Kafka Debezium-json format captures and converts database change events into JSON messages suitable to message queues like Kafka. tEn ...

Apache Flink and Apache Hive are commonly integrated in modern data pipelines to enable efficient batch and stream processing with persistent, partitioned storage. One powerful capability is writing data from Flink SQL into Hive tables using dynamic partitioning—where partition values are derived from the data itself rather than being hardcoded ...

Two-Phase Commit in Flink Sink Operators Flink achieves exactly-once guarantees for external systems through an implementation of the Two-Phase Commit (2PC) protocol. The core mechanism aligns transaction boundaries with checkpoint barriers. When a sink operator receives the first record of a checkpoint interval or encounters a checkpoint barri ...

Apache Flink 1.14.0 Highlights Core Features Checkpointing for Bounded Streams. Mixed DataStream and Table/SQL Applications in Batch Execution Mode. Introduction of the Hybrid Source for seamless reading across multiple sources. Buffer Debloating to minimize checkpoint latency. Fine-Grained Resource Management for dynamic Slot sizing. New Puls ...

Prerequisites Exactly Once Processing For exactly once semantics to work properly: Source systems: Must support data retransmission (e.g., message queues like Kafka, distributed file systems like HDFS) Sink systems: Must support idempotent operations (e.g., Doris supports deduplication) At Least Once Processing For at least once semantics: S ...