What are Apache Iceberg and Delta Lake?
Apache Iceberg and Delta Lake add table metadata and transaction rules to data-lake files so compatible engines can query and update them reliably. Lightbridge Cloud evaluates open table formats as an architecture decision within BI and data-platform selection, where catalog ownership, engine compatibility, and reporting requirements determine their practical value.
Open table formats connect data files to a consistent table state.
A directory of files does not by itself tell a query engine which version of a table to read while another process updates it. Open table formats add that coordination. The useful distinction for a BI buyer is between storing data, describing a table, and running the queries that feed a dashboard.
- Data files
- Files such as Parquet hold the rows and columns on object storage.
- Table format
- Iceberg metadata or the Delta transaction log defines which files and changes belong to a table version.
- Catalog and access controls
- A catalog makes tables discoverable to engines. Catalog, engine, and storage permissions must work together to control access.
- Query engine and BI
- An engine reads a supported table format and serves SQL results. BI tools use those results through a supported connection and semantic model.
Consider an order correction arriving while a dashboard refreshes. The table layer records a committed change; a compatible reader sees a defined table state. It does not have to guess which loose files represent the corrected order. The business still needs a rule for when that correction should affect reported revenue.
Apache Iceberg uses metadata and snapshots to describe table versions.
Iceberg tracks table state through metadata, manifests, and snapshots. A commit publishes a new state, while readers can continue using a consistent snapshot. Iceberg also supports schema and partition evolution, allowing the table definition and layout strategy to change without treating every historical file as a new table. The Apache Iceberg introduction explains those capabilities, and its reliability documentation describes commit behavior.
For a platform assessment, ask which catalog will identify the authoritative table, which engines will write to it, and who will maintain it. A table that several tools can read still needs an owner for schema changes, retention, and recovery. Open metadata does not remove those responsibilities.
Delta Lake records table changes in a transaction log.
Delta Lake combines data files with a transaction log that records committed table changes. Its capabilities include transactional updates, schema enforcement, time travel, and batch and streaming processing. The Delta Lake documentation describes these functions. An engine must understand the log and enabled table features to interpret the table correctly.
Protocol and feature compatibility matter when multiple engines share a Delta table. Enabling a feature can change what readers or writers must support. Review Delta Lake feature compatibility against the exact engine versions in the proposed architecture. Avoid equating a connector's presence with full support for every operation.
Iceberg and Delta Lake portability must be tested through the BI workflow.
Start with an actual report and trace its sources, transformations, permissions, query engine, and semantic definitions. Test fresh inserts, corrected records, schema changes, and concurrent jobs. Measure the time until the dashboard reflects a committed change and confirm that restricted data remains restricted through every supported access path.
The operating plan also needs compaction, cleanup, retention, and recovery ownership. Time travel depends on retaining the necessary metadata and files; it is not an independent backup. Catalog access and transformation code can remain platform-specific even when the underlying tables use an open format. Include those dependencies in any exit plan.
This is the table layer beneath the broader Snowflake versus Databricks decision. A table format alone cannot determine which platform suits the engineering team and report consumers. Lightbridge Cloud's BI and data-platform selection service evaluates both together.
Lightbridge Cloud runs data-platform selection and architecture advisory in-house. Vetted partners implement the lakehouse and BI environment under our project management and technical leadership. We accept no vendor kickbacks, reseller quotas, or partner-tier incentives; the recommendation follows the demonstrated workflow.
Iceberg and Delta Lake: frequently asked questions
- What are Apache Iceberg and Delta Lake?
- Apache Iceberg and Delta Lake are open table-format technologies used in data lakes and lakehouses. They manage table metadata and committed changes so compatible engines can work with files as reliable tables. They are not complete data warehouses or BI applications.
- How is a table format different from Parquet?
- Parquet defines how data is stored inside a columnar file. A table format defines how files and metadata form a table and how updates become visible. Reading raw Parquet files without the table metadata can produce an incorrect view of a table that has changed.
- What does Iceberg use snapshots for?
- Iceberg snapshots describe committed table states. Readers can work from a consistent snapshot while a writer prepares a change. Historical queries require the relevant snapshot metadata and data files to remain available under the retention policy.
- What does the Delta Lake transaction log do?
- The Delta transaction log records committed changes to the table. A compatible engine uses it with the data files to determine the table state. Delta Lake supports transactional updates, schema enforcement, and batch and streaming processing, with capability depending on the engine and enabled table features.
- Can every engine read and write both formats?
- No. Support varies by engine version, catalog integration, table protocol, and enabled features. Read support does not establish write support or safe concurrent writes. Test the precise combinations that will run in production before treating a table as portable.
- Does an open table format replace a BI platform?
- No. A BI tool still needs a query engine, permissions, and agreed business definitions. Open tables can widen the choice of data-processing tools, but they do not automatically make reports consistent or remove platform-specific governance dependencies.
Plan your data architecture with Lightbridge Cloud.
Bring the source systems, reporting requirements, and candidate platforms. We will define a practical test of table access, governance, and portability.