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Data Ingestion
Open-Source Change Data Capture Platform
★ 4.7
Universal Data Ingestion Framework
★ 3.9
N/A — Java-based Kafka connectorN/A — Java-basedN/A — Java-based Kafka connectorN/A — Java-basedPython data engineers typically run Debezium as the CDC producer and write Python consumers of the change streams it generates. After deploying Debezium connectors via Docker Compose or Kubernetes, Python services consume CDC events from Kafka topics using confluent-kafka or kafka-python — receiving full before/after row images for every database change, which are then written as Parquet to S3 or applied as upserts to a data warehouse. For teams without Kafka, Debezium Server sinks directly to AWS Kinesis or Redis Streams, both of which have first-class Python client libraries (boto3, redis-py), keeping the Python integration straightforward.
Python data engineers interact with Gobblin by defining configuration files that specify source, extractor, converter, and writer plugins — executed as a Hadoop or standalone Java job. Python orchestration scripts manage Gobblin execution via REST API, monitor job completion, and process ingested output files with PySpark for downstream transformation and loading.
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