Apache Hudi review
A strong self-hosted choice for streaming, CDC, and mutable lakehouse tables.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Apache Hudi is an open-source, self-hosted data lakehouse platform for teams building streaming, CDC, incremental, and batch data pipelines. It adds transactional tables with ACID guarantees, updates, deletes, change capture, indexing, and table services to external storage such as Amazon S3, Google Cloud Storage, Azure Blob Storage, and HDFS. Its audience includes data engineering teams that need mutable lakehouse tables, low-latency analytics, and historical queries without adopting a hosted commercial product.
Its strongest area is the depth of its incremental and change-oriented processing model. Hudi supports upserts, deletes, time travel, CDC ingestion through Hudi Streamer, and incremental processing. Teams can choose Copy-on-Write or Merge-on-Read tables, while automatic clustering, compaction, cleaning, and indexing services address ongoing table maintenance. SQL table creation and querying work through Spark SQL and Flink SQL, and Hudi connects with Apache Kafka, Apache Pulsar, PostgreSQL, MySQL, Debezium, Apache Flink CDC, Apache Spark, and Apache Flink.
Platform fit is another notable strength. Hudi integrates with Hive, Presto, Trino, Apache Impala, Amazon S3, Google Cloud Storage, and Azure Blob Storage, with catalog synchronization for Hive Metastore, AWS Glue, BigQuery, and DataHub. It also provides pluggable support for Apache Iceberg and Delta Lake formats. The tradeoff is operational scope: deployment is self-hosted, storage remains external, and teams must choose table types and manage the surrounding processing and catalog environment. Hudi is a good fit for organizations that want open-source control over streaming and CDC lakehouses; teams seeking a hosted service or a simpler, narrow-purpose analytics tool should consider alternatives.
Apache Hudi pros and cons
- Where it wins
- ACID tables with upserts, deletes, CDC, and time travel
- Supports Spark SQL, Flink SQL, streaming, and batch pipelines
- Integrates with Kafka, databases, cloud storage, and catalogs
- Where it doesn't
- Requires self-hosted deployment and operational ownership
- External storage architecture adds infrastructure decisions
- Copy-on-Write and Merge-on-Read require table design choices
Apache Hudi fact sheet, pricing and score →
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