Apache Hudi

Apache Hudi is an open-source data lakehouse platform built on a high-performance open table format, designed to bring database functionality to data lakes. It targets data engineers and architects who need to support update-heavy workloads, such as change data capture (CDC) from databases like PostgreSQL and MySQL, high-scale streaming data from Apache Kafka or Apache Pulsar, and real-time analytics with low latency. Hudi reimagines slow old-school batch data processing with a powerful incremental processing framework, enabling minute-level analytics on fresh data. It is particularly suited for organizations that require mutability—quick updates and deletes—on large datasets, making it ideal for use cases like GDPR compliance, fraud detection, and real-time dashboards where traditional append-only table formats fall short. Hudi integrates with a wide ecosystem, including Apache Spark, Apache Flink, Databricks, AWS EMR, Azure HDInsight, and cloud storage like Amazon S3, Google Cloud Storage, and Azure Blob Storage, along with data catalogs such as AWS Glue and Apache Hive Metastore.

Hudi works by combining a table format with a sophisticated ingestion engine, offering two table types: Copy-On-Write (COW) and Merge-On-Read (MOR). COW rewrites entire Parquet files on each commit, providing optimized read performance, while MOR uses a combination of columnar base files and row-based delta logs to amortize updates without rewriting whole files, reducing write amplification for high-frequency changes. Hudi provides ACID transactional guarantees with atomic writes, snapshot isolation, and non-blocking concurrency controls tailored for long-running lake transactions. Its fast, pluggable indexing—including Bloom filters, HBase index, and record-level index—enables quick updates and deletes on datasets with billions of records. Hudi supports time travel queries, allowing users to query historical data by table version, roll back to a specific state, and audit data changes via commit history. The incremental processing framework enables 10x efficiency gains by processing only new or changed data, replacing full-batch reprocessing for pipelines.

In the open table format landscape, Hudi competes directly with Apache Iceberg and Delta Lake. Unlike Iceberg, which is primarily a file format, Hudi is both a file format and a full ingestion engine, making it more complex but more capable for update-heavy workloads. As of v1.0.2, Hudi offers full-fledged Merge-On-Read support, while Iceberg (v1.10.0) and Delta Lake (v4.0.0) have more limited MOR capabilities. Hudi excels in scenarios requiring out-of-order record handling, bursty traffic, and data deduplication—areas where Iceberg and Delta Lake are less mature. However, Iceberg is simpler for traditional append-only workloads and has broader adoption in query engines like Trino and Presto. Delta Lake, backed by Databricks, has strong integration with the Databricks ecosystem and ML workflows. Hudi's market position is strongest in streaming and CDC-heavy environments, particularly in organizations using Apache Spark or Flink for real-time data ingestion.

The honest trade-offs with Hudi include its higher complexity compared to Iceberg, as it requires understanding both the table format and the ingestion engine. For append-only workloads, Iceberg or Delta Lake may be simpler to deploy and maintain. Hudi's focus on mutability and incremental processing means less emphasis on traditional batch optimization, which can lead to overhead for pure batch pipelines. Operational considerations include managing compaction for MOR tables to prevent read performance degradation, and the need for careful tuning of indexing strategies for large-scale datasets. Additionally, Hudi's ecosystem, while broad, has fewer native integrations with some query engines compared to Iceberg, which has become the default format for many cloud warehouses like Snowflake and BigQuery. Despite these trade-offs, Hudi's strengths in real-time, update-heavy scenarios make it a compelling choice for modern data lakehouse architectures.

Open source under Apache License 2.0, with no licensing costs; managed services like Onehouse offer commercial support.