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Data Ingestion
AWS Data Utility Belt for Python
★ 4.3
Open-Source Change Data Capture Platform
★ 4.7
pip install awswranglerN/A — Java-based Kafka connectorpip install awswranglerN/A — Java-based Kafka connectorAWS Data Wrangler (now called `awswrangler`) is the standard tool for AWS-native Python data pipelines. Engineers replace `boto3` + `pandas` boilerplate with single calls: `wr.s3.read_parquet('s3://bucket/prefix/')` reads all files into a DataFrame, and `wr.s3.to_parquet(df, 's3://bucket/output/', dataset=True)` writes back with Glue catalog registration and partitioning.
Python 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.
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