Compare 22 Apache Iceberg services partners delivering open lakehouse table format adoption, REST catalog architecture (Polaris, Lakekeeper, Apache Gravitino, Nessie), migration from Hive and Delta Lake, partition evolution and compaction strategy, integration with Snowflake, Databricks, Starburst, Trino, BigQuery, Athena, and Dremio query engines, and the governance pattern that prevents catalog and engine sprawl. Listings cover Big Four data practices running broader lakehouse modernisation, India-heritage SIs operating Iceberg adoption factories, lakehouse-native boutique consultancies specialising in storage layer engineering, and vendor-led services from the major Iceberg engine providers. Iceberg is now the de facto open lakehouse standard but adoption raises governance and operational questions that buyers consistently underestimate. No partner pays for placement on this directory.
Iceberg engagements split into four typical workstreams. Catalog and architecture decision, where the partner agrees the catalog topology (Polaris on Snowflake, Unity on Databricks, Lakekeeper, Apache Gravitino, Project Nessie, Glue), aligns it with the existing governance estate, defines the security and access pattern, and sets the multi-engine read or write strategy. Hive and Delta Lake migration, where the partner runs the in-place migration where possible, rebuilds partitioned tables to take advantage of Iceberg's hidden partitioning, and validates query parity across the target engine estate. Operational discipline, where the partner stands up the maintenance jobs that keep Iceberg healthy - compaction, manifest rewrite, snapshot expiration, orphan file cleanup - and aligns the cost model with the storage engineering team. Query engine integration, where the partner tunes Snowflake, Databricks, Starburst, Trino, Athena, BigQuery, and Dremio access patterns and resolves the inevitable compatibility friction across writer engines.
Three procurement archetypes recur. Big Four and global SIs (Accenture, Deloitte, PwC, KPMG) lead where Iceberg sits inside a broader data modernisation or data mesh programme; their advantage is operating model design rather than deep storage layer engineering. India-heritage SIs (TCS, Infosys, Wipro, HCLTech, LTIMindtree) lead on factory delivery: large-scale Hive migrations, standardised catalog rollouts, and offshore lakehouse operations. Lakehouse-native boutiques and vendor services (Tabular/Databricks, Snowflake, Tiger Analytics, Thoughtworks, Datafold, Dremio, Onehouse) lead on the harder engineering work: catalog topology trade-offs, multi-engine write strategy, and the compaction and maintenance pattern that keeps performance and cost stable at petabyte scale. Friction point: the multi-engine write story for Iceberg remains incomplete in 2026; writing from Snowflake and Databricks to the same table is technically possible but operationally fragile, and most successful programmes settle on a single writer engine per table with multi-engine read.
For complementary research see lakehouse platforms, open table formats, data catalogs, query engines, and cloud object storage. For adjacent services see data lakehouse engineering, Snowflake implementation, Databricks implementation, Starburst and Trino services, data mesh implementation, and data engineering.
Tell us what you're evaluating and we'll send a tailored shortlist of vendors that actually fit — no vendor funding, no pay-to-play.
6,000+ vendors · 893 comparisons · 48 country guides · Independent & vendor-neutral