🗂️Apache Hudi

NEW

Also known as: Hudi

A transactional data lake framework enabling record-level upserts, deletes, and incremental processing on object storage.

DESCRIPTION

Apache Hudi brings database-like capabilities to data lakes with ACID semantics and efficient upserts. It supports two storage table types—Copy-on-Write and Merge-on-Read—optimizing for read or write performance respectively. Hudi integrates tightly with Spark, Flink, and Presto/Trino for ingestion and querying. It enables incremental pull of changed records and maintains indexes for fast writes. Hudi suits CDC pipelines, GDPR deletions, and near-real-time analytics on lakes. It competes with Iceberg and Delta Lake, each offering transactional lake tables. Operational considerations include clustering, compaction, and metadata tuning.

ALTERNATIVE DEFINITIONS

Apache Hudi

Apache Hudi is a data lake platform that supports upserts, deletes, and incremental processing on data lakes.

Focuses on upserts, deletes, and incremental pipelines.

Source

Databricks Documentation

Apache Hudi is an open source transactional data lake framework that supports record-level inserts, updates, and deletes.

Highlights transactional semantics at record level.

Source

AWS Big Data Blog

Apache Hudi is a data lake framework that brings ACID transactions and efficient upserts to large datasets stored on object storage.

Emphasizes ACID transactions on object storage.

Source

RELATED TERMS

Data Lake Data Lakehouse Upsert Change Data Capture Apache Spark Schema Evolution ACID Transactions Partitioning

COMPETING TERMS

Apache Iceberg Delta Lake

KNOWLEDGE AREAS

Data Engineering Data Storage & Operations Real-time & Streaming Data Data Architecture

TARGET AUDIENCE

Data Engineer Data Architect Analytics Engineer MLOps Engineer Cloud Architect

EXTERNAL SOURCES

• Wikipedia - Apache Hudi
• Apache Hudi - Hudi overview and concepts
• Apache Spark - Spark integration basis

TOP QUESTIONS

When should you use Apache Hudi over plain Parquet tables?

Use Hudi when you need record-level updates, deletes, incremental pulls, and ACID guarantees on a data lake.

Category: Use cases

What is the difference between Hudi’s Copy-on-Write and Merge-on-Read?

Copy-on-Write optimizes reads by rewriting files; Merge-on-Read optimizes writes using log files merged at read time.

Category: Comparison

How does Apache Hudi enable CDC workflows?

Hudi indexes changed records and supports incremental queries to consume updates efficiently.

Category: Capabilities

What are common mistakes with Apache Hudi?

Misconfigured compaction and clustering or ignoring partitioning can cause performance degradation and query latency.

Category: Mistakes

How do you query Apache Hudi tables?

Use engines like Spark, Presto/Trino, or Hive with Hudi connectors to read and write tables.

Category: Integration

What risks exist with Apache Hudi adoption?

Operational complexity around compactions, metadata, and indexing requires tuning; poor configuration can increase costs.

Category: Risks

What is a hot topic around Apache Hudi?

Balancing COW vs MOR for lakehouse workloads and optimizing for streaming ingestion are key topics.

Category: Topic

How do you secure Apache Hudi datasets?

Apply IAM on object storage, encrypt data, enforce schema contracts, and audit compactions and deletions.

Category: Best practices

Where should you start learning Apache Hudi?

Begin with the Hudi overview, try a Spark quickstart, and experiment with upserts and incremental queries.

Category: Getting started