When should I use Apache Hudi instead of Zilla?

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 Zilla instead of Apache Hudi?

API gateway for Kafka — exposing Kafka topics over REST, SSE, gRPC, or MQTT protocols. Bridging web clients and IoT devices to Kafka without building a custom intermediary service. Event-driven architectures needing protocol translation into and out of Kafka topics

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 Zilla?

Very niche use case; most teams use Confluent REST Proxy or custom services instead. Small community and limited documentation outside the vendor's own materials. Adds another infrastructure component to manage alongside an already complex Kafka deployment

Apache Hudi vs Zilla: Key Differences for Python Data Engineering

Stream Processing

Apache Hudi

Incremental Data Processing Framework

★ 4.4

Apache-2.0

pip install hudi

Zilla

Event-Driven API Gateway

★ 3.7

Aklivity Community License

N/A — Java-based gateway

Side-by-Side Comparison

Apache Hudi

Zilla

Apache Hudi

Zilla

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

✓API gateway for Kafka — exposing Kafka topics over REST, SSE, gRPC, or MQTT protocols
✓Bridging web clients and IoT devices to Kafka without building a custom intermediary service
✓Event-driven architectures needing protocol translation into and out of Kafka topics

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

✓API gateway for Kafka — exposing Kafka topics over REST, SSE, gRPC, or MQTT protocols
✓Bridging web clients and IoT devices to Kafka without building a custom intermediary service
✓Event-driven architectures needing protocol translation into and out of Kafka topics

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

•Very niche use case; most teams use Confluent REST Proxy or custom services instead
•Small community and limited documentation outside the vendor's own materials
•Adds another infrastructure component to manage alongside an already complex Kafka deployment

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

•Very niche use case; most teams use Confluent REST Proxy or custom services instead
•Small community and limited documentation outside the vendor's own materials
•Adds another infrastructure component to manage alongside an already complex Kafka deployment

License

Apache-2.0

Aklivity Community License

License

Apache-2.0

Aklivity Community License

Install

pip install hudi

N/A — Java-based gateway

Install

pip install hudi

N/A — Java-based gateway

Rating

★ 4.4

★ 3.7

Rating

★ 4.4

★ 3.7

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

Zilla

1Event-native API gateway built for Kafka-based architectures
2Exposes Kafka topics as REST, SSE, gRPC, and MQTT endpoints
3Protocol translation between HTTP and Kafka without custom code
4Fan-out and fan-in patterns for multi-consumer topic access
5Schema registry integration for Avro and Protobuf message validation

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.

Zilla

Python data engineers use Zilla to give non-Kafka consumers HTTP-based access to Kafka data — a Python REST client can read from or write to a Kafka topic without the Kafka client library. This simplifies event-driven microservice architectures where lightweight Python services need to participate in a Kafka-based pipeline without managing consumer group offsets.

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Apache Kafka vs Redpanda

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Apache Samza vs Apache Kafka

Individual Tool Pages

View Apache Hudi details →View Zilla details →

Side-by-Side Comparison

Apache Hudi

Zilla

Apache Hudi

Zilla

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

✓API gateway for Kafka — exposing Kafka topics over REST, SSE, gRPC, or MQTT protocols
✓Bridging web clients and IoT devices to Kafka without building a custom intermediary service
✓Event-driven architectures needing protocol translation into and out of Kafka topics

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

✓API gateway for Kafka — exposing Kafka topics over REST, SSE, gRPC, or MQTT protocols
✓Bridging web clients and IoT devices to Kafka without building a custom intermediary service
✓Event-driven architectures needing protocol translation into and out of Kafka topics

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

•Very niche use case; most teams use Confluent REST Proxy or custom services instead
•Small community and limited documentation outside the vendor's own materials
•Adds another infrastructure component to manage alongside an already complex Kafka deployment

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

•Very niche use case; most teams use Confluent REST Proxy or custom services instead
•Small community and limited documentation outside the vendor's own materials
•Adds another infrastructure component to manage alongside an already complex Kafka deployment

License

Apache-2.0

Aklivity Community License

License

Apache-2.0

Aklivity Community License

Install

pip install hudi

N/A — Java-based gateway

Install

pip install hudi

N/A — Java-based gateway

Rating

★ 4.4

★ 3.7

Rating

★ 4.4

★ 3.7

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

Zilla

1Event-native API gateway built for Kafka-based architectures
2Exposes Kafka topics as REST, SSE, gRPC, and MQTT endpoints
3Protocol translation between HTTP and Kafka without custom code
4Fan-out and fan-in patterns for multi-consumer topic access
5Schema registry integration for Avro and Protobuf message validation

How Python Data Engineers Use These Tools