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Using Apache Hudi with Apache Iceberg: Interoperability via Apache XTable Tags: Yes — Apache Iceberg engines and catalogs can read Apache Hudi tables

from the top result below

Using Apache Hudi with Apache Iceberg: Interoperability via Apache

https://hudi.apache.org/blog/2026/07/28/using-hudi-with-apache-iceberg-via-xtable

Using Apache Hudi with Apache Iceberg: Interoperability via Apache XTable Tags: Yes — Apache Iceberg engines and catalogs can read Apache Hudi tables

Migrating from Delta Lake to Apache Hudi

https://hudi.apache.org/blog/2026/08/04/migrating-from-delta-lake-to-hudi

Migrating from Delta Lake to Apache Hudi Tags: You can migrate a Delta Lake table to Apache Hudi either by translating its metadata with Apache XTable — no data rewrite required, since both formats store data as Apache Parquet — or by rewriting the table once with a Spark bulk_insert; many teams start by running

Apache Hudi vs Apache Iceberg for Streaming Ingestion

https://hudi.apache.org/blog/2026/08/11/hudi-vs-iceberg-for-streaming-ingestion

Apache Hudi vs Apache Iceberg for Streaming Ingestion Tags: Both Apache Hudi and Apache Iceberg can ingest streaming data, but they are built around different assumptions about how often a table is written to

Apache Hudi vs Delta Lake for Write-Heavy Workloads

https://hudi.apache.org/blog/2026/08/12/hudi-vs-delta-lake-for-write-heavy-workloads

Apache Hudi vs Delta Lake for Write-Heavy Workloads Tags: For update- and delete-heavy tables, the core architectural difference is this: Apache Hudi locates the records being changed through pluggable indexes — including a record-level index that maps each key to its file group — and can absorb changes into Merge

What is an Open Table Format?

https://hudi.apache.org/blog/2026/07/14/what-is-an-open-table-format

Apache Hudi, Apache Iceberg, Delta Lake, and Apache Paimon are the major open table formats; in all of them the data itself stays in open file formats like Apache Parquet or Apache ORC

Apache Hudi - The Data Lake Platform

https://hudi.apache.org/blog/2021/07/21/streaming-data-lake-platform

Apache Hudi - The Data Lake Platform Tags: Apache Hudi is a streaming data lake platform built around a database kernel—not merely a table format or a transactional layer, but a complete software stack with built-in table services, data services, and incremental processing capabilities

This document is a specification for the Hudi's Storage Format which

https://hudi.apache.org/learn/tech-specs-0-point-x

Apache Hudi will manage metadata, and provide common abstractions and pluggable interfaces to most/all common compute/query engines

Syntax

https://hudi.apache.org/learn/tech-specs

Timeline Hudi stores all actions performed on a table into a Log Structured Merge (LSM) tree structure called the Timeline

a GitHub account

https://hudi.apache.org/contribute/developer-setup

Once cloned, build Hudi by following Building Apache Hudi from source in the README, which also covers building against a specific Spark version or Flink version

Migrating from Apache Hive Tables to Apache Hudi

https://hudi.apache.org/blog/2026/07/30/migrating-from-hive-to-hudi

Migrating from Apache Hive Tables to Apache Hudi Tags: Migrating an Apache Hive table to Apache Hudi means converting its underlying data files into a Hudi table — either in place with Hudi's bootstrap mechanism or by rewriting the data with Spark — and re-registering it in your metastore through Hudi's catalog

Concurrency Control

https://hudi.apache.org/docs/concurrency_control

Concurrency Control Concurrency control defines how different writers, readers, and table services coordinate access to a Hudi table

Apache Hudi vs Apache Iceberg for CDC Workloads

https://hudi.apache.org/blog/2026/08/06/hudi-vs-iceberg-for-cdc-workloads

Apache Hudi vs Apache Iceberg for CDC Workloads Tags: Both Apache Hudi and Apache Iceberg can ingest change data capture (CDC) streams, but they were designed around different problems

Migrating from Apache Iceberg to Apache Hudi

https://hudi.apache.org/blog/2026/08/05/migrating-from-apache-iceberg-to-hudi

Migrating from Apache Iceberg to Apache Hudi Tags: You can adopt Apache Hudi on an existing Apache Iceberg table either by translating its metadata with Apache XTable — no data rewrite, since both formats store data as Parquet files — or via a one-time rewrite; and because XTable also works in the reverse direction

Security Model

https://hudi.apache.org/contribute/security

If you have apprehensions regarding Hudi's security, or you discover vulnerability or potential threat, don’t hesitate to get in touch with the Apache Security Team by dropping a mail at security@apache

Incremental Processing on the Data Lake

https://hudi.apache.org/blog/2020/08/18/hudi-incremental-processing-on-data-lakes

Apache Hudi is a data lake framework which provides exactly this ability to ingest, manage and query large analytical data sets on a distributed file system/cloud stores

What is Upsert on a Data Lake?

https://hudi.apache.org/blog/2026/07/15/what-is-upsert-on-a-data-lake

How Open Table Formats Make Upserts Possible Open table formats — Apache Hudi, Apache Iceberg, and Delta Lake — introduced a transactional metadata layer on top of immutable files, turning a directory of Parquet files into a proper table

Spark Quick Start

https://hudi.apache.org/docs/quick-start-guide

13 spark-shell --master "local[2]" \ --packages org

Overview

https://hudi.apache.org/docs/overview

Try out these Quick Start resources to get up and running in minutes: Unstructured Data Quick Start Guide – store embeddings (VECTOR) and raw bytes (BLOB) and run similarity search with hudi_vector_search If you want to experience Apache Hudi integrated into an end to end demo with Kafka, Spark, Hive, Presto, etc

Data Lakehouse vs Data Warehouse vs Data Lake: What's the Difference?

https://hudi.apache.org/blog/2026/07/23/lakehouse-vs-data-warehouse-vs-data-lake

Projects like Apache Hudi, Apache Iceberg, and Delta Lake define tables (not just files) over Parquet and ORC, and bring the database capabilities the lake was missing: ACID transactions with concurrency control, so writers commit atomically and readers never see partial results