S3 Tables vs Databricks for Iceberg: Key Differences

Amazon S3 Tables and Databricks both let you work with Apache Iceberg tables, but they serve different roles. S3 Tables is a fully managed Apache Iceberg table service on AWS, while Databricks is a unified analytics platform with its own Iceberg support. This article compares the two so you can choose the right one for your data lakehouse needs.

What Are S3 Tables and Databricks Iceberg?

Amazon S3 Tables is a managed service that lets you create Iceberg tables directly in S3, without running your own table metadata infrastructure. It handles table maintenance tasks like compaction and snapshot management automatically. Databricks, on the other hand, is a cloud platform for data engineering, machine learning, and analytics. It supports Iceberg through its Delta Lake and Unity Catalog, and you can also read and write Iceberg tables stored in S3 or other clouds.

The key difference: S3 Tables is a storage-layer feature, while Databricks is a compute-and-governance platform. S3 Tables gives you a place to store Iceberg tables; Databricks gives you the tools to query, transform, and manage them, plus a broader ecosystem.

Background: The Rise of Apache Iceberg

Apache Iceberg is an open table format designed for large analytical datasets. It was created at Netflix in 2017 and later donated to the Apache Software Foundation. Iceberg solves common problems with traditional Hive tables: it supports ACID transactions, time travel, schema evolution, and hidden partitioning. These features make it a popular choice for modern data lakes.

Both AWS and Databricks have adopted Iceberg, but they integrate it differently. AWS offers S3 Tables as a native service, while Databricks supports Iceberg alongside its own Delta Lake format. Understanding this context helps you see why the two options are not direct substitutes—they are complementary pieces of a data stack.

How S3 Tables Works

S3 Tables is a new S3 bucket type, called a table bucket, that is optimized for Iceberg. When you create a table bucket, you can create Iceberg tables in it, and S3 automatically manages the table metadata, including the Iceberg catalog. This means you do not need to set up a separate catalog service like AWS Glue or run a metastore.

Key features of S3 Tables include:

• Automatic compaction of data files to keep queries fast.
• Snapshot management to control storage costs.
• Integration with AWS analytics services like Athena, Redshift, and EMR.
• Support for standard Iceberg clients, so tools that speak Iceberg can read and write these tables.

You pay for storage and for the table maintenance operations that S3 performs. This is ideal if you already use AWS and want a low-maintenance Iceberg storage layer.

How Databricks Iceberg Support Works

Databricks is a cloud platform that provides a unified workspace for data teams. It has its own table format called Delta Lake, but it also supports Apache Iceberg. You can read Iceberg tables in Databricks using the Iceberg connector, and you can also convert Iceberg tables to Delta Lake for deeper integration.

Databricks uses Unity Catalog for governance, which gives you fine-grained access control, data lineage, and auditing across all your data assets. When you work with Iceberg tables in Databricks, you can:

• Query Iceberg tables using SQL or DataFrames.
• Write to Iceberg tables using the Iceberg writer.
• Combine Iceberg data with Delta Lake tables in the same pipeline.
• Use Databricks’ optimization tools, like Photon, to speed up queries.

Databricks is a full platform, so you get notebooks, job scheduling, and ML tools. It is not just a storage layer; it is where you run your data workloads.

Comparing S3 Tables and Databricks Iceberg

When deciding between the two, consider these factors:

• Management overhead: S3 Tables is fully managed for storage; Databricks requires you to manage clusters and compute, though it simplifies much of the work.
• Ecosystem: S3 Tables integrates with AWS services; Databricks integrates with its own ecosystem and third-party tools.
• Governance: Databricks has Unity Catalog for centralized governance; S3 Tables relies on AWS IAM and S3 policies.
• Cost model: S3 Tables charges for storage and maintenance; Databricks charges for compute and sometimes storage, depending on your plan.
• Portability: Both support open Iceberg, so you can move tables between environments, but Databricks adds its own layer on top.

In practice, many teams use both: store Iceberg tables in S3 Tables and query them from Databricks. This gives you the best of both worlds—cost-effective storage and powerful compute.

Common Problems and Misconceptions

One common misconception is that S3 Tables and Databricks are competitors. In reality, they can work together. S3 Tables is a storage service, while Databricks is a compute platform. You can point Databricks at an S3 Table bucket and run queries.

Another issue is compatibility. Not all Iceberg features are supported in both systems. For example, some Iceberg table properties like certain partition transforms may not work in Databricks. Always test your workloads on both to ensure compatibility.

Performance can also vary. S3 Tables is optimized for S3, but Databricks may use its own caching and query engine, which could be faster for certain workloads. Benchmark your specific queries to see which performs better.

How to Choose Between S3 Tables and Databricks

Here is a simple decision guide:

• If you are already on AWS and want a simple, managed Iceberg storage layer, choose S3 Tables.
• If you need a full data platform with notebooks, ML, and governance, choose Databricks.
• If you want to use Iceberg tables with multiple engines (like Spark, Trino, Flink), S3 Tables is a good base.
• If you want to use Databricks’ Delta Lake features, you may prefer to convert Iceberg tables to Delta.

Many teams start with S3 Tables for storage and use Databricks for processing. This hybrid approach is common and works well.

Costs and Performance Considerations

Costs vary based on usage. S3 Tables charges for storage (per GB) and for maintenance operations like compaction (per operation). Databricks charges for compute (per DBU) and optionally for storage if you use their managed catalog. You should estimate your workload to compare costs.

Performance depends on query engine and data size. S3 Tables is designed for high-throughput analytics, but Databricks’ Photon engine can be faster for some workloads. Test with your own data to get accurate benchmarks.

Alternatives to Consider

Other options for Iceberg include:

• AWS Glue Data Catalog with Iceberg on standard S3 buckets.
• Apache Spark with Iceberg running on EMR or EC2.
• Snowflake’s Iceberg support (though it is more integrated with their platform).
• Starburst (Trino) for querying Iceberg tables.

Each has trade-offs in management, performance, and cost. Evaluate them based on your specific needs.

Frequently Asked Questions

Can I use S3 Tables with Databricks?

Yes, you can. Databricks can read and write Iceberg tables stored in S3 Tables, as long as you configure the right credentials and catalog settings. This is a common setup for teams that want managed storage with Databricks compute.

Is S3 Tables the same as S3 with Iceberg?

No. S3 Tables is a special bucket type that manages Iceberg metadata for you. Standard S3 buckets require you to set up and manage your own Iceberg catalog and maintenance. S3 Tables simplifies this by automating it.

Does Databricks support Iceberg natively?

Databricks supports Iceberg through its runtime, but it is not the default format. You can read and write Iceberg tables, but you may need to enable the Iceberg support in your cluster configuration. Delta Lake remains the primary format in Databricks.

Which is cheaper: S3 Tables or Databricks?

It depends on your usage. S3 Tables is typically cheaper for storage-only workloads, but Databricks provides more features. If you need heavy compute, Databricks may be more cost-effective per query due to its optimized engine. Always run a cost analysis for your specific scenario.

Can I migrate from Databricks Delta Lake to S3 Tables?

Yes, you can migrate by converting Delta tables to Iceberg and then loading them into S3 Tables. This is possible using tools like the Delta Lake Iceberg bridge or by rewriting data. The process requires careful planning to avoid data loss.

What are the main limitations of S3 Tables?

S3 Tables is limited to AWS regions where it is available. It also requires that you use Iceberg clients that support the S3 Tables API. Additionally, some advanced Iceberg features like row-level updates may have limitations depending on the client.

Related

• How to Remove Extra Space in Word: Step-by-Step
• How to Print Background Color in Word 2013
• Fix WordPress Could Not Remove Old Plugin Errors

in t | Finish time | min | 50 | 0
in d | Distance | km | 10 | 0 | 100000
out pace | Average pace | min/km | t / d | 2
out speed | Average speed | km/h | d / (t / 60) | 2