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A data lake is raw files on cheap object storage with no database guarantees, while a lakehouse adds an open table format layer, such as Apache Hudi, Apache Iceberg, or Delta Lake, on top of those files
from the top result below
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
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
https://hudi.apache.org/docs/quick-start-guide
13
spark-shell --master "local[2]" \
--packages org
https://hudi.apache.org/powered-by
Adopters
Apache Hudi is a fast growing diverse community of people and organizations from all around the globe
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
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
https://hudi.apache.org/learn/tech-specs
Hudi brings database capabilities (tables, transactions, mutability, indexes, storage layouts) along with an incremental stream processing model (incremental merges, change streams, out-of-order data) over very large collections of files/objects, turning immutable cloud/file storage systems into transactional data