When should I use Apache Hudi instead of Pathway?

Incremental data ingestion into data lakes with upsert, delete, and CDC support on object storage. Change data capture workflows writing directly to S3 or ADLS in Parquet format. ACID transactions on large data lakes enabling record-level updates without full rewrites

When should I use Pathway instead of Apache Hudi?

Python-native incremental computation on streaming data with a familiar DataFrame-like API. Unified batch and streaming pipelines using the same Python code without switching frameworks. Teams wanting stream processing without learning a JVM-based framework like Flink or Samza

What are the main weaknesses of Apache Hudi?

Complex configuration for compaction, clustering, and timeline management requires expertise. Interoperability with query engines like Trino and Athena requires careful table property setup. Steeper learning curve than writing plain Parquet files; operational overhead is significant

What are the main weaknesses of Pathway?

Younger project with a smaller community than Flink or Spark Streaming for production use cases. Limited connector ecosystem compared to established stream processing frameworks. Advanced stateful operations have less documentation and fewer community examples

Apache Hudi vs Pathway: Key Differences for Python Data Engineering

Stream Processing

Apache Hudi

Incremental Data Processing Framework

★ 4.4

Apache-2.0

pip install hudi

Pathway

Python ETL for Real-Time Data

★ 4.3

BSL-1.1

pip install pathway

Side-by-Side Comparison

Apache Hudi

Pathway

Apache Hudi

Pathway

Best For

✓Incremental data ingestion into data lakes with upsert, delete, and CDC support on object storage
✓Change data capture workflows writing directly to S3 or ADLS in Parquet format
✓ACID transactions on large data lakes enabling record-level updates without full rewrites

✓Python-native incremental computation on streaming data with a familiar DataFrame-like API
✓Unified batch and streaming pipelines using the same Python code without switching frameworks
✓Teams wanting stream processing without learning a JVM-based framework like Flink or Samza

Best For

✓Incremental data ingestion into data lakes with upsert, delete, and CDC support on object storage
✓Change data capture workflows writing directly to S3 or ADLS in Parquet format
✓ACID transactions on large data lakes enabling record-level updates without full rewrites

✓Python-native incremental computation on streaming data with a familiar DataFrame-like API
✓Unified batch and streaming pipelines using the same Python code without switching frameworks
✓Teams wanting stream processing without learning a JVM-based framework like Flink or Samza

Weaknesses

•Complex configuration for compaction, clustering, and timeline management requires expertise
•Interoperability with query engines like Trino and Athena requires careful table property setup
•Steeper learning curve than writing plain Parquet files; operational overhead is significant

•Younger project with a smaller community than Flink or Spark Streaming for production use cases
•Limited connector ecosystem compared to established stream processing frameworks
•Advanced stateful operations have less documentation and fewer community examples

Weaknesses

•Complex configuration for compaction, clustering, and timeline management requires expertise
•Interoperability with query engines like Trino and Athena requires careful table property setup
•Steeper learning curve than writing plain Parquet files; operational overhead is significant

•Younger project with a smaller community than Flink or Spark Streaming for production use cases
•Limited connector ecosystem compared to established stream processing frameworks
•Advanced stateful operations have less documentation and fewer community examples

License

Apache-2.0

BSL-1.1

License

Apache-2.0

BSL-1.1

Install

pip install hudi

pip install pathway

Install

pip install hudi

pip install pathway

Rating

★ 4.4

★ 4.3

Rating

★ 4.4

★ 4.3

Key Features

Apache Hudi

1Data lake table format enabling upserts and deletes on object storage
2Copy-on-Write and Merge-on-Read table types for read vs. write optimization
3Incremental pull queries retrieve only changed records since a timestamp
4Timeline-based metadata tracks all table operations for audit and rollback
5Native integration with Spark, Flink, Hive, and Presto

Pathway

1Python-native stream processing framework with a DataFrame-like API
2Unified batch and streaming: the same code runs on historical and live data
3Incremental computation engine automatically handles late data corrections
4Connectors for Kafka, S3, PostgreSQL, and local files
5LLM App framework for building AI pipelines on live data streams

How Python Data Engineers Use These Tools

Apache Hudi

Python data engineers use Hudi with PySpark to build CDC (Change Data Capture) pipelines on data lakes — ingesting database change events from Kafka and applying upserts to Hudi tables on S3 using `UPSERT` operation type. Hudi handles deduplication and merge semantics automatically, enabling mutable data lake tables without full partition rewrites.

Pathway

Python data engineers use Pathway to write streaming pipelines in pure Python without learning a separate API — the DataFrame-like operators (filter, join, groupby, reduce) work identically on both batch files and live Kafka streams. Pathway is particularly suited for real-time feature engineering pipelines that need to update model inputs as new events arrive.

More Stream Processing Comparisons

Stream Processing

Apache Flink vs Apache Kafka

Stream Processing

Apache Storm vs Apache Kafka

Stream Processing

Faust vs Apache Kafka

Stream Processing

Apache Kafka vs Apache Spark Streaming

Stream Processing

Apache Kafka vs Redpanda

Stream Processing

Apache Samza vs Apache Kafka

Individual Tool Pages

View Apache Hudi details →View Pathway details →

Side-by-Side Comparison

Apache Hudi

Pathway

Apache Hudi

Pathway

Best For

✓Incremental data ingestion into data lakes with upsert, delete, and CDC support on object storage
✓Change data capture workflows writing directly to S3 or ADLS in Parquet format
✓ACID transactions on large data lakes enabling record-level updates without full rewrites

✓Python-native incremental computation on streaming data with a familiar DataFrame-like API
✓Unified batch and streaming pipelines using the same Python code without switching frameworks
✓Teams wanting stream processing without learning a JVM-based framework like Flink or Samza

Best For

✓Incremental data ingestion into data lakes with upsert, delete, and CDC support on object storage
✓Change data capture workflows writing directly to S3 or ADLS in Parquet format
✓ACID transactions on large data lakes enabling record-level updates without full rewrites

✓Python-native incremental computation on streaming data with a familiar DataFrame-like API
✓Unified batch and streaming pipelines using the same Python code without switching frameworks
✓Teams wanting stream processing without learning a JVM-based framework like Flink or Samza

Weaknesses

•Complex configuration for compaction, clustering, and timeline management requires expertise
•Interoperability with query engines like Trino and Athena requires careful table property setup
•Steeper learning curve than writing plain Parquet files; operational overhead is significant

•Younger project with a smaller community than Flink or Spark Streaming for production use cases
•Limited connector ecosystem compared to established stream processing frameworks
•Advanced stateful operations have less documentation and fewer community examples

Weaknesses

•Complex configuration for compaction, clustering, and timeline management requires expertise
•Interoperability with query engines like Trino and Athena requires careful table property setup
•Steeper learning curve than writing plain Parquet files; operational overhead is significant

•Younger project with a smaller community than Flink or Spark Streaming for production use cases
•Limited connector ecosystem compared to established stream processing frameworks
•Advanced stateful operations have less documentation and fewer community examples

License

Apache-2.0

BSL-1.1

License

Apache-2.0

BSL-1.1

Install

pip install hudi

pip install pathway

Install

pip install hudi

pip install pathway

Rating

★ 4.4

★ 4.3

Rating

★ 4.4

★ 4.3

Key Features

Apache Hudi

1Data lake table format enabling upserts and deletes on object storage
2Copy-on-Write and Merge-on-Read table types for read vs. write optimization
3Incremental pull queries retrieve only changed records since a timestamp
4Timeline-based metadata tracks all table operations for audit and rollback
5Native integration with Spark, Flink, Hive, and Presto

Pathway

1Python-native stream processing framework with a DataFrame-like API
2Unified batch and streaming: the same code runs on historical and live data
3Incremental computation engine automatically handles late data corrections
4Connectors for Kafka, S3, PostgreSQL, and local files
5LLM App framework for building AI pipelines on live data streams

How Python Data Engineers Use These Tools