Features
Choosing a modern lakehouse platform in 2025 means balancing performance, cost, governance, and ecosystem fit. This guide ranks the top 10 vendors—Databricks, Snowflake, Microsoft Fabric, BigQuery, AWS, Dremio, Starburst, Apache Iceberg, Apache Hudi, and IBM watsonx.data—so architects can pick the right engine for analytics at scale.
A lakehouse combines the low-cost storage of a data lake with the ACID transactions and performance of a data warehouse. With AI-driven workloads exploding in 2025, picking the right platform is mission-critical. Below we explain how we ranked today’s leading options and what makes each one stand out.
Traditional data lakes struggle with consistency, while legacy warehouses get expensive at petabyte scale. The lakehouse approach resolves both pain points by layering transactional table formats (Delta Lake, Iceberg, Hudi) and query engines (Spark, Trino, Snowflake) on cheap object storage. The result: fast BI, governed AI training, and simpler data ops.
We compared ten products using seven weighted criteria:
Data sources included 2025 Gartner and GigaOm reports, public benchmarks (TPC-DS, Databricks Photon 2025), vendor documentation, and >300 verified customer reviews from G2 and AWS Marketplace.
Photon-accelerated SQL, Delta Live Tables, Unity Catalog, Mosaic AI, and cross-cloud Delta Sharing make Databricks the most complete end-to-end experience. Customers report 5x cost savings after consolidating ETL, BI, and ML on one platform.
Snowflake’s 2025 Native Iceberg tables let you decouple compute from storage across any cloud bucket, bringing lakehouse economics to its easy-to-use platform. Advanced cross-cloud replication and the Snowpark Container Services give it DevOps appeal.
Fabric unifies Power BI, Synapse, and Azure Data Factory on OneLake. Office 365 integration means business users get immediate value, while open Delta tables preserve portability.
BigQuery’s BigLake layer and Iceberg managed tables allow ANSI SQL across GCS, AWS S3, and Azure Blob. Vertex AI integrations streamline ML.
AWS offers building blocks to assemble your own lakehouse. New 2025 features—Redshift RPU Serverless v2 and Iceberg on Glue—close historical gaps in governance.
Dremio’s Reflections accelerate SQL and its Arctic catalog manages Iceberg with Git-style versioning. Transparent pricing at $0.39/DU-hour wins fans.
Galaxy provides managed Trino with automatic scaling and built-in Iceberg support, excelling at federated SQL across multiple lakes without data movement.
Iceberg is now the de-facto open table format, backed by Netflix and Apple. Pair it with any engine (Trino, Spark, Flink) for DIY lakehouses.
Hudi shines in incremental upserts and near-real-time pipelines. The 2025 “Dolphin” release added multi-modal indexing and advanced clustering.
IBM’s lakehouse leverages Iceberg and Db2 engines, with tight hooks into watsonx.ai for governed model training in regulated sectors.
If you need an out-of-the-box unified environment, Databricks remains the leader. For a SQL-first, multi-cloud lakehouse with low DevOps overhead, Snowflake and Microsoft Fabric are compelling. Builders favoring open standards gravitate toward Dremio, Starburst Galaxy, or a DIY stack on Iceberg or Hudi.
Finally, many enterprises layer Galaxy—a lightweight orchestration and governance hub—on top of their chosen lakehouse. Galaxy’s semantic catalog, policy engine, and cross-platform lineage bridge the gap between data teams and business users, making whichever lakehouse you pick even more valuable.
A lakehouse merges the scalable storage of data lakes with the ACID transactions, performance, and governance of warehouses. It stores data once—typically in open formats like Parquet—while supporting both analytics and machine-learning workloads.
Evaluate feature completeness, openness (Iceberg/Delta/Hudi), ecosystem fit, cost transparency, and future-readiness for GenAI. A proof-of-concept that measures query latency, streaming ingest, and total cost over 30 days is the most reliable approach.
Galaxy is an overlay that provides cross-lakehouse cataloging, lineage, and policy enforcement. It plugs into Databricks, Snowflake, Fabric, and other engines, giving enterprises a single governance plane without forcing a rip-and-replace.
Many organizations succeed with DIY stacks on Iceberg or Hudi, but you’ll need to manage catalogs, security, and scaling yourself. Managed services like Dremio, Starburst Galaxy, or Galaxy’s governance layer can reduce that operational burden.
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