As data lakes grow in size and complexity, tools like Amazon Athena combined with table formats like Apache Iceberg become essential for scalability, data governance, and performance. In this post, we’ll explore:

• When it makes sense to use Iceberg.
• How to partition your data effectively.
• Best practices to avoid common pitfalls in production.

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Athena + S3: How far does the classic approach go?

The typical pattern when querying data in S3 using Athena is:

• Store data in columnar formats like Parquet or ORC.
• Manually partition data by fields such as date or region.
• Load partitions using MSCK REPAIR TABLE or ALTER TABLE ADD PARTITION.

This approach works well for append-only datasets, where data is never modified. But it comes with limitations:

• No support for updates or deletes.
• No data versioning or time travel.
• Manual partition management becomes error-prone and fragile.
• Partition metadata can grow large and hurt performance.

This is where Iceberg comes in.

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What is Apache Iceberg and why is it useful with Athena?

Iceberg is an open table format built for data lakes. It’s now natively supported in Athena and solves many of the challenges of the traditional Parquet + partitions approach. Key benefits:

• Update, delete, and merge operations (MERGE INTO, DELETE, UPDATE).
• Schema evolution without recreating tables.
• Flexible partitioning you can change later without rewriting data.
• Time travel and snapshot queries.
• No need for MSCK REPAIR or manual partition registration.
• Optimized metadata handling and small file management.

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Should you always use Iceberg?

Not necessarily. Iceberg introduces operational complexity, so it’s most valuable in specific scenarios:

Scenario	Use Iceberg?
Append-only data	Not needed
Data that needs updates or deletes	Yes
Need time travel or versioning	Yes
Frequently evolving schemas	Yes
Ad-hoc queries over long historical ranges	Yes
Small datasets or low volume	Overkill

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How should you partition an Iceberg table?

One of Iceberg’s biggest advantages is its separation of logical partitioning from physical layout. You can partition by:

• Direct fields (region, category)
• Derived fields (years(date), truncate(product_id, 100))
• Bucketing (bucket(32, user_id))

CREATE TABLE sales (
  sale_id        string,
  sale_date      date,
  customer_id    string,
  total_amount   double
)
PARTITIONED BY (
  years(sale_date),
  bucket(16, customer_id)
)
STORED AS ICEBERG

You can later modify the partition strategy without rewriting the entire dataset.

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Best Practices

1. Partition by fields used in filters.
   Avoid partitioning by columns that are rarely queried.
2. Don’t over-partition.
   Partitioning by day with low daily volume can create thousands of folders, hurting performance.
3. Consider bucketing.
   Great for high-cardinality fields like user_id or product_id.
4. Stick with Parquet.
   Iceberg works best with columnar formats like Parquet to minimize scanned data.
5. Compact files regularly.
   Use Iceberg’s compaction features to reduce the number of small files.

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Final Thoughts

Apache Iceberg is a powerful addition to the modern data stack when working with Athena. It’s not mandatory for all use cases, but it shines when:

• Your data changes frequently.
• You need to evolve your schema safely.
• You’re working with large historical datasets.
• You want to avoid the pain of manual partition management.

Before jumping into Iceberg, take a look at your workload, data patterns, and whether you truly need versioning, updates, or schema evolution. But if you’re building a long-term data platform, Iceberg is a strong foundation.