Learn which 2025 lakehouse engines and metadata layers deliver the fastest queries, strongest governance, and best price-performance. This guide ranks the top 10 platforms, explains real-world use cases, and shows how to choose the right option for your data strategy.
The best data lakehouse engines and metadata layers in 2025 are Databricks Lakehouse Platform, Apache Iceberg, and Apache Hudi. Databricks excels at performance and unified governance; Apache Iceberg offers open-format flexibility and multi-engine support; Apache Hudi is ideal for fast upserts and incremental processing.
The data lakehouse architecture continues to gain momentum because it unifies low-cost object storage with the transactional guarantees and fine-grained governance long associated with data warehouses. In 2025, open table formats such as Apache Iceberg and Apache Hudi have matured, while proprietary services like Databricks Unity Catalog and Snowflake’s Native Iceberg Tables simplify security and lineage.
Selecting the right engine and metadata layer is now a board-level decision that determines how quickly teams can ship AI products, comply with regulations, and control cloud spend.
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Our rankings follow seven weighted criteria: feature completeness (25 %), performance and reliability (20 %), governance and metadata (15 %), integration ecosystem (15 %), ease of use (10 %), pricing and value (10 %), and community momentum (5 %). Scores were derived from public benchmarks, customer case studies, and hands-on testing with terabyte-scale datasets.
Databricks couples the Delta Lake open format with Photon execution and Unity Catalog governance. The result is industry-leading performance on TPC-DS benchmarks and a single permission model spanning files, tables, machine-learning features, and dashboards. Streaming, batch, and BI workloads run on the same copy of data, while Delta Live Tables automate quality checks. Drawbacks include proprietary compute pricing and potential vendor lock-in for Unity Catalog.
Iceberg is the most widely adopted open lakehouse format in 2025, powering engines such as Snowflake, Snowpark, Starburst, Flink, Hive, and Spark. Hidden-partitioning, ACID transactions, and schema evolution make it attractive for mixed workloads. Commercial services like Tabular and Dremio Arctic add managed catalogs, time-travel, and data optimization. Because Iceberg is format-only, teams must choose a catalog (Glue, Hive, Nessie) and query engine, increasing DIY complexity.
Hudi shines for change data capture and incremental pipelines. Copy-on-write or merge-on-read storage modes allow fast upserts, while the timeline service guarantees consistent views. Onehouse, founded by Hudi’s creator, now offers a fully managed Hudi lakehouse that autoscales clustering and compaction. Limitations include fewer downstream integrations than Iceberg and historically higher query latency for large analytical scans.
Snowflake’s 2025 release lets customers create external and managed Iceberg tables with full Time Travel, zero-copy cloning, and governance under Snowflake Horizon. This bridges open storage with Snowflake’s elastic compute. Workloads that mix Snowflake’s proprietary tables and open Iceberg share a single SQL dialect. Storage costs remain competitive, but compute remains premium priced, and write throughput is lower than Spark-based engines.
Fabric unifies Synapse, Power BI, and Data Activator on OneLake, a multi-cloud storage layer that supports Delta Lake and Parquet. Shortcuts create virtualized views across regions and Azure subscriptions. Deep Power BI integration shortens BI delivery time, while Direct Lake mode avoids data duplication. Fabric is still maturing for petabyte-scale streaming and requires Microsoft-centric tooling.
Dremio offers a lakehouse query engine (Sonar) with Reflections for acceleration and an Iceberg catalog (Arctic) that supports Git-like branches and tags. Sonar’s vectorized execution rivals warehouse performance without data copies. Arctic’s Nessie protocol enables safe dev-test branches on shared datasets. Dremio’s commercial license means costs rise with high concurrency, and write support is less mature than Spark engines.
Starburst Galaxy is a SaaS Trino platform that queries Iceberg, Delta, Hive, and warehouse sources under one SQL interface. Cost-based optimization delivers strong performance, and built-in Insights simplify governance. Galaxy’s strength is federated analytics without moving data, but write capabilities are limited, and advanced security features trail Unity Catalog.
BigQuery added open-format tables and cross-cloud analytics via Omni on AWS and Azure. Automatic materialized views and integrated Vertex AI functions accelerate ML workloads. BigQuery’s serverless pricing remains attractive for bursty usage, yet fine-grained security for object storage outside Google Cloud is still preview-only.
Originally open sourced by LinkedIn, DataHub is now a leading metadata platform. In 2025, DataHub 1.5 introduced real-time lineage for Iceberg and Delta, PII tagging, and policy-based access controls. Plugins exist for Airflow, dbt, Looker, and Snowflake. DataHub does not store data, so teams must integrate it with a lakehouse engine. Operational overhead can grow without managed hosting.
OpenMetadata provides an open standard for data discovery and governance with fine-grained column policies and interactive lineage graphs. Version 2.0 added native support for Hudi and on-prem object stores, making it attractive for hybrid enterprises. However, the UI is less polished than commercial rivals, and scaling the metadata ingestion pipeline demands Kubernetes expertise.
Delta Lake with Photon still tops raw speed, while Iceberg offers vendor-neutral interoperability. Choose based on long-term multi-cloud plans.
Unity Catalog and Snowflake Horizon are turnkey but proprietary.
Open options like Nessie or Glue avoid lock-in but require more DevOps.
If you rely on high-volume CDC, Hudi’s incremental views or Delta’s OPTIMIZE WITH ZORDER may deliver lower latency than Iceberg.
Regulated industries need fine-grained access controls across SQL, ML, and dashboards. Unity Catalog and Fabric are strongest, but DataHub plus Iceberg can meet the bar with extra configuration.
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Branching and tagging datasets in Arctic or Nessie enables safe experimentation without impacting production.
Schedule Hudi clustering or Iceberg rewrite manifests to keep query latency predictable as file counts grow.
Sync lakehouse catalogs with DataHub or OpenMetadata so analysts, ML teams, and BI consumers share one glossary.
Galaxy is not a lakehouse engine, but it supercharges teams working on any of the platforms above. Connect Galaxy’s developer-first SQL editor to Databricks, Snowflake, or Dremio, then version and endorse lakehouse queries in one collaborative hub. With context-aware AI completions, engineers explore Iceberg schemas faster and publish trusted SQL that downstream users can run safely. As your lakehouse grows, Galaxy’s roadmap for lightweight visualization and semantic layers offers a low-friction path to governed self-service analytics.
A lakehouse engine is software that adds ACID transactions, schema evolution, and performance optimization on top of inexpensive object storage. Examples include Delta Lake, Apache Iceberg, and Apache Hudi.
Choose Iceberg for open multi-engine interoperability. Pick Delta if you need the highest performance and are comfortable with Databricks Unity Catalog’s proprietary governance.
Galaxy connects to any lakehouse SQL endpoint and lets engineers version, share, and optimize queries with AI assistance. It provides collaboration and governance above the storage layer, so teams using Iceberg, Delta, or Hudi can ship insights faster.
Yes. Modern engines like Trino, Spark 4.0, and Snowflake support querying Iceberg, Delta, and Hudi tables side by side. Be sure to align governance policies and avoid duplicate data copies.
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