Apache Iceberg is now the default data lakehouse table format for analytics and AI. It’s not hard to understand why. Iceberg brings robust table management, time travel, schema evolution, and scalable performance to object storage. But even the best technology requires upkeep. But as Iceberg adoption increases, Iceberg data maintenance becomes more critical.
What kind of maintenance does Iceberg need? To understand the problem, it’s essential to know how Iceberg works.
Why Iceberg requires maintenance
The secret to Iceberg’s success is metadata. As Iceberg tables grow and evolve, they accumulate metadata, snapshots, and small files. If left unmanaged, it can impact performance and inflate storage costs. Iceberg’s architecture is designed for fast writes and flexible use, but that flexibility comes with ongoing maintenance needs.
To keep your lakehouse optimized, a set of regular data maintenance tasks is essential. When completed consistently, these tasks ensure Iceberg delivers fast queries, storage cost efficiency, and dependable data integrity.
In this article, we’ll break down the core maintenance practices recommended for Iceberg and show how Starburst Galaxy makes them easy to schedule, automate, and scale so your lakehouse runs smoothly without constant manual effort.
What is Iceberg data maintenance?
To keep Iceberg tables performing efficiently and cost-effectively, regular maintenance is required. The Iceberg documentation outlines three essential tasks that help manage metadata, storage, and query performance.
Here’s a quick look at what each of these maintenance steps involves, plus one more that we think is just as essential as Iceberg’s own list.
Step 1: Data compaction
The first task is data compaction. While Iceberg’s write-optimized architecture excels at capturing streaming or frequent data updates, it inevitably leads to file fragmentation resulting from thousands of small files spread across partitions. This fragmentation increases query planning and execution time, especially for large analytical workloads.
The best way to fix this problem is by performing regular data compaction. Compaction addresses this by rewriting and consolidating these small files into larger, more query-friendly segments. This not only reduces metadata overhead but also enhances scan efficiency and improves parallelism for engines like Starburst.
Step 2: Iceberg snapshot expiration
The next essential task is snapshot expiration. Each time a dataset changes in Iceberg, a new snapshot is created. These snapshots power features like time travel and rollback by preserving a record of the table’s state at each point in time.
While this functionality is useful, especially for auditing and debugging, it can lead to rapid growth in metadata and storage usage.
To deal with this problem, you need to perform regular snapshot expiration. Expiring old snapshots reduces overhead and ensures the system stays efficient. For organizations subject to data regulations like GDPR, regularly removing outdated snapshots is also an important compliance step.
Step 3: Delete orphan files
Orphan file removal is the next step. When using Iceberg, data files are written before the associated metadata is committed. If a write fails partway through the process, or is never completed, leftover data files can be created.
These files are known as orphan files, and if left unchecked, they can accumulate, adding to storage costs. To deal with this problem, routine cleanup is essential to control and maintain storage costs.
Step 4: Profiling and Statistics
The final step involves the management of profiling and statistics. The profiling and statistics maintenance operation automates the metric refresh process by analyzing the lake table and returning relevant metrics to the query optimizer on a scheduled basis. This approach improves query performance.
As data evolves, these statistics can accumulate and become outdated or redundant. If left unchecked, they may consume unnecessary storage and potentially degrade performance.
How Starburst solves the data maintenance problem
Managing Iceberg data maintenance manually can quickly become complex, especially as your data ecosystem grows.
That’s where Starburst comes in. Starburst automates and simplifies these routine tasks, helping you maintain performance, reduce storage overhead, and ensure your Iceberg tables remain query-ready at all times.
Let’s explore how this happens.
Starburst data maintenance scheduling
Starburst simplifies Iceberg maintenance by offering powerful scheduling tools that fit your data architecture. You can schedule maintenance tasks for a single table, a group of tables, or entire schemas and catalogs. This ensures consistent data hygiene and performance across your environment.
Configuring data maintenance
Check out the image below to see how Starburst Galaxy handles data maintenance scheduling.
Data maintenance error handling
When you add new tables to a scheduled catalog, Starburst automatically includes them in the maintenance schedule. Built-in notifications and detailed error handling help you stay ahead of any issues that arise.
Check out the image below to see how Starburst Galaxy alerts users to any errors that might arise.
By applying scheduling at both the schema and table levels, you can maintain broader coverage while making exceptions where needed. Starburst includes full query history, making it easy to track and audit all maintenance activity.
Starburst jobs feature
In addition to Starburst’s built-in data maintenance scheduling, Starburst Galaxy includes a separate Jobs feature that allows you to automate individual SQL tasks. Using this feature, you can define a single SQL statement, such as compacting files, refreshing materialized views, or cleaning up statistics, and schedule it to run on a recurring basis.
This feature offers greater flexibility for automating operations that extend beyond the four core maintenance activities listed above. When used alongside scheduled maintenance, Jobs provide a comprehensive set of tools to help keep your Iceberg data reliable, efficient, and ready for analytics and AI workloads.
Materialized View Refreshes
Refreshing materialized views is essential for maintaining accurate and performant analytics, especially as underlying data changes. Starburst simplifies this process by enabling you to automate refreshes with the Jobs feature.
To enable this feature, write a SQL statement that performs the refresh and schedule it as a recurring Job. This approach ensures that your materialized views remain current without manual upkeep, supporting more efficient and trustworthy query results.
Want to see Starburst Data Maintenance in action? Check out the interactive demo below.
Starburst Galaxy makes Iceberg table maintenance easy
Apache Iceberg offers powerful capabilities for managing data at scale, from schema evolution to time travel. But even the most advanced table format requires ongoing maintenance to perform at its best. Starburst Galaxy manages that maintenance, both simple and scalable.
With built-in tools for scheduling tasks across schemas or tables, automating SQL jobs, and refreshing materialized views, Starburst removes the operational burden from your data teams. It provides a unified platform purpose-built for Iceberg optimization to help you manage your data down to the table level.
Ready to streamline your Iceberg operations and unlock faster, more reliable insights? Start your Starburst Galaxy trial today.
