When should I use Apache Hudi instead of Apache Spark Streaming?

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 Apache Spark Streaming instead of Apache Hudi?

Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API. Teams already using PySpark who want to add streaming with minimal new tooling or concepts. Combining historical batch data with real-time streams in a single unified Spark job

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 Apache Spark Streaming?

Micro-batch architecture introduces latency compared to true streaming engines like Flink. Legacy DStream API is deprecated; migration to Structured Streaming is required for new work. JVM overhead and cluster management complexity remain significant operational challenges

Apache Hudi vs Apache Spark Streaming: Key Differences for Python Data Engineering

Stream Processing

Apache Hudi

Incremental Data Processing Framework

★ 4.4

Apache-2.0

pip install hudi

Apache Spark Streaming

Scalable Stream Processing

★ 4.6

Apache-2.0

pip install pyspark

Side-by-Side Comparison

Apache Hudi

Apache Spark Streaming

Apache Hudi

Apache Spark Streaming

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

License

Apache-2.0

License

Apache-2.0

Install

pip install hudi

pip install pyspark

Install

pip install hudi

pip install pyspark

Rating

★ 4.4

★ 4.6

Rating

★ 4.4

★ 4.6

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

Apache Spark Streaming

1Micro-batch and continuous streaming modes on the Spark engine
2Exactly-once semantics with checkpointing and write-ahead logs
3Unified API with Spark batch — same DataFrame operations on streams
4Native Kafka, Kinesis, S3, and Delta Lake source/sink support
5Watermarking for handling late-arriving data in event-time windows

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.

Apache Spark Streaming

Python data engineers use Spark Structured Streaming via PySpark to process high-volume Kafka streams at scale. A streaming job reads a Kafka topic as a DataFrame, applies transformations (filtering, aggregations, joins with static data), and writes results continuously to Delta Lake or a database — using the same PySpark syntax as batch jobs.

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 Apache Spark Streaming details →

Side-by-Side Comparison

Apache Hudi

Apache Spark Streaming

Apache Hudi

Apache Spark Streaming

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

✓Micro-batch stream processing integrated with the Spark ecosystem using Structured Streaming API
✓Teams already using PySpark who want to add streaming with minimal new tooling or concepts
✓Combining historical batch data with real-time streams in a single unified Spark job

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

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

•Micro-batch architecture introduces latency compared to true streaming engines like Flink
•Legacy DStream API is deprecated; migration to Structured Streaming is required for new work
•JVM overhead and cluster management complexity remain significant operational challenges

License

Apache-2.0

License

Apache-2.0

Install

pip install hudi

pip install pyspark

Install

pip install hudi

pip install pyspark

Rating

★ 4.4

★ 4.6

Rating

★ 4.4

★ 4.6

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

Apache Spark Streaming

1Micro-batch and continuous streaming modes on the Spark engine
2Exactly-once semantics with checkpointing and write-ahead logs
3Unified API with Spark batch — same DataFrame operations on streams
4Native Kafka, Kinesis, S3, and Delta Lake source/sink support
5Watermarking for handling late-arriving data in event-time windows

How Python Data Engineers Use These Tools