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An AWS lakehouse is a set of services with separate jobs, not a single product: Amazon S3 stores data, Apache Iceberg organizes files into tables, AWS Glue Data Catalog holds table metadata, Athena runs SQL queries, and Lake Formation governs access. Build the design around the engines and operations you need, then verify their permissions and Iceberg support together; the combination is not automatically secure or equally suitable for every workload.
What each part of an AWS lakehouse does
The architecture works when storage, table format, catalog, query engine, and governance are treated as distinct layers. Their responsibilities overlap at integration points, but none replaces all the others.
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| Layer | Service or format | Role |
|---|---|---|
| Storage | Amazon S3 | Stores the data files that make up the lake. |
| Table format | Apache Iceberg | Tracks data files as tables and supplies table-level operations and metadata. |
| Catalog | AWS Glue Data Catalog | Holds discoverable table metadata used by compatible AWS services. |
| Query | Amazon Athena | Provides SQL access to data in S3 through catalog metadata. |
| Governance | AWS Lake Formation | Applies permissions to catalog resources and registered data locations, alongside IAM. |
Why Iceberg matters
A directory of files does not, by itself, provide table semantics. Iceberg manages collections of files as tables and supports capabilities such as schema evolution, versioning, and time travel. AWS documentation also describes record-level insert, update, and delete operations. Whether a particular operation is available depends on the engine and its version, so validate the exact operations your applications need rather than assuming every Iceberg-capable service supports them all.
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- Store data in S3. Organize ownership, retention, and access around the data and the engines that will use it.
- Represent datasets as Iceberg tables. Iceberg metadata describes the table and its data files; the files remain in S3.
- Expose table metadata through Glue Data Catalog. Compatible services use the catalog to discover table definitions.
- Query with Athena. Athena integrates with Glue and can query Iceberg tables using supported operations.
- Grant access through the intended governance path. Lake Formation permissions apply to catalog resources and registered S3 locations when the relevant integration and permissions are configured.
This is a conceptual flow, not a complete deployment recipe. IAM policies, S3 location registration, catalog configuration, and service-specific permissions all affect whether a principal can actually read or write a table.
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How to plan the implementation
Start with requirements, not a service checklist. Record which teams own ingestion, table maintenance, data quality, permissions, and the integrations that expose the data. Then map each workload to its required operations and engines.
1. List the workload operations
For each consumer, specify whether it must read, write, run DDL, perform time travel, or maintain tables. Check support for each required operation against the Athena engine version and any other engine that will access the table. A general statement that an engine supports Iceberg is not proof that it supports every operation your workflow requires.
2. Decide how tables and metadata will be managed
Choose the S3 layout and Iceberg table conventions that fit your data ownership and lifecycle requirements, and establish how table metadata will be published in Glue. Confirm that the services and any other engines that need access can use that catalog arrangement.
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Identify the principals that need access and the level of control required. Lake Formation can provide fine-grained controls, including table-, column-, row-, and cell-level controls in supported integrations. AWS also documents hybrid access mode, which allows administrators to onboard Lake Formation permissions selectively while retaining IAM-based permissions for Glue and S3 actions. These models require deliberate, consistent configuration; enabling Lake Formation alone does not secure every route to the data.
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4. Register locations and configure permissions
Lake Formation integrations require registering the relevant S3 location and assigning appropriate permissions to the IAM principal for the table, database, and location. In supported service integrations, Lake Formation can provide access to S3 data through temporary credentials. Check the requirements for each service and principal rather than treating a catalog grant as the only permission needed.
5. Test the complete access path
Validate with the actual principals and engines that will use the tables. Test intended reads and writes, confirm that users see only the data their grants allow, and verify that the table operations used by ingestion and maintenance succeed. Include IAM and S3 access paths in the review, not just catalog visibility.
Lake Formation and Iceberg write-operation limits
A key boundary in AWS’s documentation is that Lake Formation does not currently manage permissions on certain Iceberg write operations for open table formats, including VACUUM, MERGE, UPDATE, and OPTIMIZE. Do not assume that fine-grained Lake Formation grants govern these operations in the same way as supported reads or catalog access. Determine how the selected engine authorizes each write or maintenance operation, and verify the current AWS guidance before deploying because support can change.
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When to consider Amazon S3 Tables
Amazon S3 Tables is an alternative design path to evaluate, not a default upgrade for every existing lake. AWS describes it as S3 storage optimized for analytics with built-in Apache Iceberg support. Its documented integrations include Glue Data Catalog and Lake Formation; access is available through engines including Athena and EMR. AWS describes governance choices that can use IAM controls or IAM together with Lake Formation grants.
Best Value
Compare S3 Tables with an existing S3-and-Iceberg arrangement against the specific engines, operations, catalog exposure, governance model, and operational responsibilities your workload requires. The available documentation establishes the integration options, but does not establish that S3 Tables is universally faster, cheaper, or simpler, or that migration is appropriate for every workload.
How to choose an architecture that fits
- Workload and engine compatibility: Confirm read, write, DDL, time-travel, and maintenance support for every engine and version involved.
- Governance needs: Decide whether controls need to operate at table, column, row, or cell level, and account for the documented write-operation boundary.
- Catalog and integration model: Check how metadata is exposed through Glue and which AWS or third-party engines must discover and use the tables.
- Operational ownership: Assign responsibility for ingestion, table maintenance, data quality, permissions, and service integration.
- Deployment context: Verify service availability and requirements for the relevant AWS Region and configuration. AWS capabilities and integrations can vary by version and Region.
There is no single bundle that fits every organization. A design should be selected for its workload and operating model, with support and permissions checked at the level of the actual operations involved.
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