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Data Ingestion
Open-Source Change Data Capture Platform
★ 4.7
Unified Logging Layer
★ 4.4
N/A — Java-based Kafka connectorN/A — Ruby daemon, install via package managerN/A — Java-based Kafka connectorN/A — Ruby daemon, install via package managerPython 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 use Fluentd to collect application logs from Python services and route them to Elasticsearch, BigQuery, or S3 for analysis. Python applications emit structured JSON logs which Fluentd's tail input plugin reads, applies filter plugins to parse and enrich, and forwards to the analytics destination — decoupling log production from storage decisions.
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