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Many data teams are exploring the benefits of Apache Iceberg for managing large datasets on a data lake. Iceberg promises features like multi-engine support, easier updates and deletes on massive volumes of data, and freedom from vendor lock-in.

However, it also comes with certain limitations and operational requirements (Iceberg is the future of big data, but not if you don’t have the kind of data it needs to make a difference).

This blog post aims to give you a full picture of what Iceberg (in total) is, why organizations are interested in it, how it compares to other solutions, and what pitfalls you should be aware of before jumping in.

What Is Apache Iceberg?

Apache Iceberg is an open table format designed for large analytic datasets stored in data lakes (for example, on AWS S3). It introduces an additional metadata layer on top of files like Parquet, making it easier to:

• Track schemas and schema evolution.
• Manage snapshots (time travel).
• Perform updates and deletes without rewriting entire files.
• Work with multiple query engines (like Spark, Trino, and StarRocks).
• Many more, but this isn’t an iceberg features guide

While some services use Iceberg “under the hood,” the real advantage comes from using its multi-engine capabilities and decoupling storage from compute. This means you can switch engines or vendors as needed without being locked into a single platform.

Why Are Companies Considering Apache Iceberg?

1. Multi-Engine Support

Many organizations have different teams using different tools. One team might use Athena or Trino for interactive analytics, while another relies on Snowflake or Spark for batch processing. Iceberg acts like a common layer, allowing these teams to access the same data with their preferred engine.

2. Avoiding Vendor Lock-In

Iceberg’s open nature lets you pick the query engine or cloud service that fits your needs. If you ever decide to leave a particular vendor, your data remains in open file formats, and your Iceberg table metadata can still be read by other platforms. Cool, right?

3. Handling Large Datasets

Some companies deal with hundreds of terabytes (or more) of event data. Iceberg supports partial updates and deletes (equality deletes, positional deletes), which can be very useful if you need to comply with regulations like GDPR. Instead of rewriting entire Parquet files, you can target specific data that must be removed or altered.

Read more about iceberg deletes in our blog on Merge on Read and Copy on write Iceberg blog series

4. A “Lakehouse” Approach

Iceberg is often seen as part of a broader lakehouse architecture, bringing warehouse-like features (ACID properties, versioning, etc.) to a data lake. This can be an attractive alternative to a traditional data warehouse if you have varied workloads and want to pay only for storage and compute as you use them.

Iceberg is not something you MUST adopt leaving or simply abandoning anything or everything you had in place before you heard about Iceberg. It is a use case based technology, not to be used just about everywhere.

Potential Challenges and Limitations

1. Integration with AWS Services

Some teams find that Iceberg does not always integrate smoothly with AWS EMR, Lake Formation, or the Glue Catalog. Certain tasks may require workarounds or extra maintenance.

There are also reports of bugs or features still in beta. If your environment heavily relies on AWS services, testing Iceberg carefully in a lower environment is recommended. DE’s here discuss in length about why they are still reluctant to adopt Iceberg.

2. Maintenance Overhead

Iceberg offers powerful features, but those features come with additional operational tasks. You may need to:

• You need to optimize tables to merge small files.
• You need to vacuum or remove outdated snapshots.
• You need to Manage Schemas if you want the same schema name in multiple AWS regions (for separate testing and production setups).

For smaller datasets or transactional workloads, a simple RDBMS (like MySQL) might require far less upkeep.

3. Security Limitations

Iceberg stores data in files (commonly Parquet), meaning entire files are accessed when queried. Row-level or column-level security has to be enforced at the catalog level. If you use multiple query engines with multiple catalogs, these security rules might not consistently apply everywhere. Organizations handling sensitive PII or requiring fine-grained access control may need a different approach.

The iceberg community is working on a solution to this, but till now no concrete decision has been made. Each table format is divided into the implementation details here.

4. The Small File Problem

One well-known drawback in data lake solutions is the “small file problem.” Real-time or frequent mini-batch ingest can create thousands of tiny files, which slows queries because each file must be opened individually.

Iceberg itself won’t automatically solve this; you need to routinely run “optimize” or similar jobs to combine small files into larger ones.

5. Writing from Multiple Engines

Certain engines (for instance, Snowflake) do not support writing to external catalogs. You might end up in a situation where only one engine can write to the Iceberg table, and the other engine can only read. This constraint can limit some aspects of multi-engine interoperability.

Comparing Iceberg with Other Table Formats

1. Delta Lake

• Pros: Strong ACID transactions, deeply integrated with Databricks, broad ecosystem.
• Cons: Some advanced features may require a Databricks license. If you already use Databricks-native tables, you may not find a strong reason to switch to Iceberg.

2. Hudi

• Pros: Good for scenarios requiring high-frequency upserts and incremental data.
• Cons: Lower adoption rate and community support compared to Iceberg. Some teams report challenges with scaling or pivoting away.

3. Data Warehouse vs. Data Lakehouse

Iceberg should not be directly compared to a traditional data warehouse like Snowflake. Instead, it belongs to the lakehouse category—an evolution that merges warehouse features into a data lake. You might still prefer a data warehouse if you need simpler operations and robust, out-of-the-box security and governance features.

When to Use Spark, Trino, and StarRocks

Iceberg is often paired with multiple engines, each serving a different use case:

• Spark: Ideal for large-scale batch processing and data transformations.
• Trino: Well-suited for interactive SQL queries and fast analytics across diverse data sources.
• StarRocks: Focused on high-performance, real-time analytical workloads.

By placing Iceberg underneath, you enable all these engines to operate on the same data without duplicating it or relying on different file layouts. This is a major advantage for teams that want specialized processing but still want to keep a single source of truth.

Who Should (and Shouldn’t) Adopt Iceberg?

Strong Candidates for Iceberg:

• Large Data Volumes: Organizations with hundreds of terabytes or petabytes benefit from its advanced metadata and partial update/delete capabilities.
• Multi-Engine Environments: If you need Athena, Spark, Trino, and more to work on the same data, Iceberg is built for that.
• GDPR or Regulatory Compliance: Iceberg can simplify removing specific lines of data compared to plain Parquet on a data lake.

Cases Where Iceberg May Not Fit:

• Strict Row/Column Security Requirements: Catalog-level security may not be enough if you need granular or engine-specific enforcement.
• Small or Transactional Workloads: A simple database like MySQL may be easier to manage if your data volume is relatively small.
• AWS-Centric Stacks with Minimal Flexibility: If you rely heavily on AWS-specific tools like Glue or Lake Formation, you might face extra complexity until bugs and integration issues are resolved.

Are Vendors Worth It?

One big question is whether to manage Iceberg yourself or use a vendor. Vendors may provide:

• One-Click Maintenance: Automated file compaction and snapshot cleanup.
• Managed Catalog: Simplified schema management and less trouble with region-based duplications.
• Support and Expertise: Help with tricky integrations or performance optimizations, saving you from constant troubleshooting.

While open-source offers independence and flexibility, a vendor solution can reduce your operational overhead—especially useful if you do not have a large in-house data platform team.

But what can be better than an open source tool that dumps to Iceberg like OLake? Check us out at github.com/datazip-inc/olake

Some Parting Thoughts

Even if Iceberg helps unify data, it does not free you from housekeeping. Plan for regular optimize/vacuum tasks and track snapshots.

If you are on AWS, explore how Iceberg interacts with EMR, Glue, and Lake Formation in a sandbox environment. Test AWS Integration Early

For highly sensitive data, confirm how you will handle row or column-level restrictions, especially if multiple engines are reading the same files.

Right Tool for the Right Job:

If your current setup (for example, a single MySQL database) works just fine, adding Iceberg may introduce complexity with no real benefit. Conversely, if your data scales and you want multi-engine flexibility, Iceberg can be a major advantage.

In short, Apache Iceberg can transform your data lake into something that feels closer to a warehouse—providing ACID-like transactions, flexible updates, and broad engine compatibility.

Yet it is no magic bullet. You must be ready to handle its operational tasks, integration quirks, and potential security gaps for row-level or column-level data. For those that get it right, Iceberg’s open ecosystem and vendor-neutral approach can be a game-changer, especially in large, multi-tool environments.

Conclusion

Apache Iceberg is a powerful way to build a truly open, multi-engine data lake. It addresses many shortcomings of using raw Parquet or Hive tables, such as difficult schema evolution and complicated deletes.

However, it also introduces operational overhead like file compactions, security challenges, and potentially tricky integrations with AWS services if you go and try to implement everything on your own.

Before adopting Iceberg, ask yourself:

1. Do I have large datasets needing frequent updates or deletions?
2. Will multiple teams or engines benefit from the same data?
3. Are my security requirements covered by catalog-level rules?
4. Do I have the capacity to manage and optimize Iceberg tables?

If the answer to these questions aligns with Iceberg’s strengths, it can become a solid cornerstone of your data platform strategy. Otherwise, you might be better served by simpler solutions or a different table format—at least until your data reaches a scale and complexity where Iceberg truly shines.

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