Apache Iceberg vs Delta Lake

Yes, but the cost of switching grows with the number of tables, the amount of history you keep and the number of jobs that write to them. Both formats commonly store data in Parquet files, so the data files may be reusable, while the metadata layer, the catalog and the writers usually need to be converted or rebuilt. Treat the choice as a multi-year commitment for the catalog and the write path. Before you commit, ask your engineers to list every engine, job and BI tool that touches the tables, and confirm which of those each format supports in the versions you run. Iceberg's metadata and Delta's transaction log are different structures, so any conversion needs a tested plan and a rollback path.

No supplied figure gives a duration, so treat any number you hear as unverified until you measure it in your own environment. The main drivers are total data volume, the number of tables, how much snapshot or version history must survive the move, and how many writers must be paused or cut over. Conversion is only part of the work. Catalog changes, query tests on each engine, access policies and BI connections all add effort. Ask any implementation partner for a written migration plan that lists the tables in scope, the cutover order, the validation checks and the rollback steps, and ask which assumptions about downtime and data volume the plan uses. A quote should follow from that written scope.

Yes. Trino lists both an Iceberg connector and a Delta Lake connector, and it queries data in place through connectors without loading that data into its own storage. The Iceberg connector works with Hive Metastore, AWS Glue, JDBC, REST, Nessie and Snowflake catalogs, selected with iceberg.catalog.type, and it supports Iceberg format versions 1 and 2, with version 3 marked experimental. The Delta Lake README lists a Trino connector that reads from and writes to Delta Lake. Confirm the specific Delta features your tables use against the connector documentation for the Trino release you deploy, since Trino 484 was published on October 15, 2026, and your version may differ. Plan separate object storage and a table catalog for each format, and test joins across both before you depend on them.

No. Delta.io states that UniForm, Delta Universal Format, lets Iceberg and Hudi clients read Delta tables, which can reduce how many copies of the same data you keep for different readers. It does not remove the choice. Writers still produce one primary format, the metadata must stay in sync, and you still need to confirm which readers, catalogs and features each engine supports in your version. Check how each engine handles the operations your tables depend on, such as deletes, schema changes and time travel, and test those paths in a staging copy. Ask for the exact read behavior of each client in writing, and name the team that owns the sync process and its monitoring.

Your team needs working knowledge of the compute engines you choose, usually Spark or Trino, plus object storage, a table catalog and the day-to-day operations of the format itself. Iceberg adds a catalog service that you must run, secure, upgrade and back up, and the catalog you pick determines much of that work. Delta Lake work often centers on Spark and the transaction log, while Trino-first teams should test Delta connector features against their own workloads. Skills to check include table maintenance such as compaction and old-version cleanup, access control, and upgrade testing. Trino 484 requires a 64-bit Java 25 runtime, minimum 25.0.1, so confirm that your platform team can maintain that runtime alongside the rest of the stack.

DeployOpen plans, implements, customizes, integrates, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside the client's own infrastructure, including cloud accounts, data centers and air-gapped networks. A lakehouse build in that environment falls within this scope. Most engagements are fixed-scope, so the scope, deliverables and timelines are agreed in writing before work starts, and ongoing support, upgrades and continuous testing are available as retainers. DeployOpen does not publish a price list, and a quote follows from the written scope. Ask that the scope name the engines, catalog, storage layer, access controls, backup approach and compliance evidence you need. DeployOpen prepares systems and evidence for compliance assessments but does not issue audit reports or certifications.

The cost has four main parts: internal staff time, compute and storage, the operation of the catalog and engines, and any outside services. Both Apache Iceberg and Delta Lake are open-source projects under the Apache License 2.0, so the format code carries no license fee, but you still pay for infrastructure, people and any implementation partner. One-time costs include design, migration, testing and training. Recurring costs include storage, compute, catalog hosting, upgrades and any retainer. Costs rise with the number of engines, data volume, write frequency, the amount of deletes and upserts, and the compliance evidence you must produce. When you request a quote, ask for one-time and recurring costs listed separately, with the assumptions, engines and versions named, and the staff effort each party is expected to contribute.

The catalog matters most for Iceberg, because engines use it to find and track table metadata. Trino's Iceberg connector supports Hive Metastore, AWS Glue, JDBC, REST, Nessie and Snowflake catalogs, and the right choice depends on which engines you run and where your metadata must live. Delta tables keep their transaction log in storage, but engines still need a way to discover those tables, so you should confirm how your chosen engines register and find Delta tables. For both formats, ask who operates the catalog, how it is backed up and upgraded, and whether it sits inside your compliance boundary. Check the Trino Iceberg connector documentation for the catalog options supported by your Trino release before you finalize the design.