Apache Iceberg is here to stay. But how best to use it? How should someone implement Iceberg?
The Starburst Icehouse architecture offers an answer.
Predicated on Iceberg and Trino, it offers an emerging idealized reference architecture for the modern data lakehouses, and with good reason.
Why Iceberg + Trino is the perfect implementation
Iceberg’s powerful approach to transactional data, combined with Trino’s ability to scale quickly using distributed SQL, delivers warehouse-like performance.
But there’s a catch.
Iceberg tables require continuous planning, monitoring, and maintenance. Fall down in this area, and you’ll suffer degraded query performance, bloated storage costs, and operational headaches.
What to consider when considering Iceberg
You may be one of many companies that want to implement Iceberg, but are concerned about management costs. If so, Managed Iceberg may be a solution for you.
Here’s a rundown on why Apache Iceberg keeps making headlines, why it can be a pain to manage, and how Managed Iceberg delivers the promise without the overhead.
The power of Apache Iceberg
Apache Iceberg is an open table format designed to address the limitations of its predecessor, Apache Hive. Unlike traditional storage systems, Iceberg acts as a metadata layer on top of your existing data lake. It works across cloud platforms, whether that’s Amazon S3, Azure Data Lake Storage, or Google Cloud Storage.
What makes the Iceberg table format special?
Its true difference is its inherent vendor-neutral architecture. You own your data. You’re free to query it with any compatible query engine. This openness eliminates vendor lock-in and gives organizations true control over their data lakehouse strategy.
In addition, Apache Iceberg supports a number of key features that make it ideal for today’s high-performance data workloads. Rich metadata and transaction support, branching support, schema evolution, time travel, hidden partitioning, and others make it easier than Hive to change and optimize your data as your business grows and evolves.
Most importantly of all, Iceberg supports ACID transactions, ensuring data consistency even with concurrent write operations. This makes Apache Iceberg tables ideal for use cases involving streaming ingestion, where data integrity is critical.
Iceberg v3
Iceberg v3 builds on this rich bed of functionality. Support for default values, deletion vectors, row-level lineage, and table encryption, among other performance and flexibility optimizations, makes it even easier to support analytics and AI workloads with large datasets at petabyte scale.
The Iceberg ecosystem continues to grow, with support from query engines like Trino, Apache Spark, Apache Flink, Presto, and data platforms including Snowflake and Databricks. This broad compatibility across providers ensures interoperability and prevents vendor lock-in.
The hidden costs of unmanaged Iceberg
Apache Iceberg is inherently powerful and solves a lot of problems resulting from Hive architecture. But managing it isn’t without problems if you don’t have the right tools.
Without proper maintenance, query performance degrades over time. Storage costs climb. Data engineering teams are stretched thin managing routine tasks.
Unfortunately, many organizations don’t realize this immediately. Instead, they only recognize it as they see the performance of critical queries fall steadily over weeks or months. This puts data engineers in reactive mode, leaving them scrambling to keep everything running optimally.
Data ingestion challenges
To do it right, getting data into Iceberg tables requires careful orchestration. Many teams use Kafka or Apache Flink for streaming ingestion, but implementing this correctly is tricky. Poor implementation results in duplicate data, missed records, or brittle data pipelines that break under load.
Setting up multiple ingestion workflows for different sources compounds the complexity. Each pipeline needs its own configuration, monitoring, and error handling.
File management and compaction
There are also multiple issues with maintaining Apache Iceberg tables over time as your data changes. While Iceberg is designed to perform well over time, this benefit doesn’t come for free.
Every time an Iceberg table changes, new snapshot metadata files are created. For tables with frequent updates or streaming ingestion, this quickly leads to thousands of small files scattered across partitions.
Small files hurt performance. Each file has a fixed cost to open and read. When a query requires reading thousands of tiny Parquet files instead of dozens of larger ones, I/O operations become a bottleneck—even with excellent metadata.
Compaction consolidates these small data files into larger, more efficient ones. But running compaction manually is time-consuming and easy to neglect. Without regular compaction, query performance degrades steadily.
Snapshot management
Iceberg maintains snapshots of your tables for time travel and rollback capabilities. Valuable? Yes. But these snapshots accumulate quickly and consume cloud storage. Old snapshots also increase query planning time as the metadata grows.
Snapshot expiration removes outdated versions beyond your retention requirements. This cleans up both metadata files and the underlying Parquet files that expired snapshots reference.
Another essential maintenance task is orphan file cleanup. This reclaims object storage from data files no longer referenced by any snapshot. These orphaned files accumulate after snapshot expiration and can represent significant wasted storage costs.
Partitioning and sorting maintenance
Proper partitioning dramatically improves query performance through partition pruning—but only if configured correctly. Over-partitioning creates too many small partitions. Under-partitioning misses optimization opportunities.
Sorted tables enable aggressive file skipping during queries. Data written in sorted order by frequently-queried columns can reduce data scanned by 50% or more. But maintaining sort order requires regular optimization as new data arrives.
Hidden partitioning is one of Apache Iceberg’s key features, but you still need to optimize partition strategies as your workloads evolve. This includes handling schema changes and ensuring partitioning remains aligned with query patterns.
Monitoring and measuring
You can’t improve what you don’t measure.
Effective data management for Iceberg requires tracking file statistics, query performance metrics, and table health indicators over time. Without consistent monitoring, problems only become visible after they impact users. By then, remediation is more complex and disruptive.
Metadata management is critical here. The Iceberg catalog tracks all metadata about your tables, including versioning information, schema evolution history, and the lifecycle of data files. Keeping this metadata optimized is essential for maintaining performance.
The migration challenge
Many organizations also face a difficult question. How much data should live in Iceberg tables in the first place?
Years of focus on data centralization have conditioned teams to view over-centralization as the default approach. In practice, mass centralization projects often run over time and budget. Research shows that over 80% of large-scale data migration projects fail to end on time or within budget.
Forced centralization often creates more data silos than it solves. In the meantime, business teams, frustrated by slow migration timelines, often build their own shadow solutions rather than wait.
Data governance and sovereignty laws add another consideration. In many jurisdictions, certain types of data can’t cross national borders at all. If it can, strict regulations and compliance must be observed. That makes full centralization impossible regardless of technical capability.
Making Managed Iceberg happen: Alleviating the overhead
So what’s the solution? The best approach is to go into Iceberg usage with your eyes wide open, understanding the operational overhead involved.
In this context, a manual approach isn’t optimal. As your data processing workloads increase, your data team will struggle to keep up.
The better tactic is a Managed Iceberg approach. With Managed Iceberg, you implement automation that monitors and resolves the most common Iceberg issues—compacting tables, simplifying data pipelines, optimizing partitioning, and streamlining metadata management.
Building this out yourself, of course, is still possible. But doing so would still require a sustained engineering effort to create and maintain.
That’s why Starburst Galaxy implements Managed Iceberg as part of our overall Iceberg support. This allows you to focus on your data and business use cases while leaving tedious maintenance to us.
Starburst Galaxy implements Managed Iceberg with four key features:
Easy data ingest
Implementing data pipelines via Kafka or Apache Flink correctly is complex. Mistakes lead to duplicate records, missed events, or workflows that require constant babysitting.
Managed Iceberg Pipelines simplify this dramatically. Galaxy provides zero-ops data ingestion from both Kafka and Amazon S3. Production-ready workflows come out of the box, delivering 10x faster queries and a 66% reduction in data costs compared to manual implementations.
The process collapses to three easy steps:
- Connect to your Kafka-compliant stream or Amazon S3 bucket
- Select a destination Iceberg table
- Map your source data to a relational format
Galaxy also provides automated data preparation via SQL, ensuring data integrity from the moment it lands in your data lakehouse.
Automated maintenance scheduling
Manual maintenance is error-prone and time-consuming. Galaxy’s automated maintenance scheduling eliminates this burden entirely. You can configure maintenance at the table, schema, or catalog level using the Iceberg catalog. The platform automatically includes new tables in maintenance schedules as your data ecosystem grows.
Automate retention in line with compliance requirements
Flexible retention periods vary depending on your compliance and auditing requirements. Automatic deletion based on retention policies keeps storage costs under control without manual intervention.
Orphan file removal
Galaxy also performs orphan file cleanup, reclaiming wasted object storage automatically. All of this is powered by ongoing statistics and profiling maintenance to ensure that query engines always have the most current metadata for efficient execution plans and optimal query performance.
Data compaction
Data compaction runs automatically in the background, consolidating small Parquet files before they impact performance. This automated approach to file management ensures your Apache Iceberg tables remain optimized as data volumes grow.
Jobs automation
Beyond core maintenance operations, Galaxy’s Jobs feature lets you schedule any SQL task on a recurring basis. This gives you the flexibility to implement custom workflows that extend beyond standard maintenance.
Use Jobs for materialized view refreshes, custom data quality checks, or specialized ETL transformation pipelines. Any SQL function or workflow can be automated and scheduled, providing scalability for your data engineering team.
Adopting an iterative approach to migration
Managed Iceberg also solves the migration challenge by enabling an iterative, selective approach.
Galaxy doesn’t force all data into Iceberg at once. Its Icehouse architecture lets you use distributed queries to access data wherever it lives. Trino connects to your existing data sources, whether databases, data warehouses, data lakes, or other data lakehouses.
This means you can identify high-value datasets to migrate first, using a data-driven approach. These are typically large datasets that are frequently accessed by multiple teams or that will most benefit from Apache Iceberg’s advanced functionality, such as time travel, schema evolution, versioning, or ACID transactions.
With this flexibility, you can start small, migrating two to three datasets at a time. You can then monitor performance improvements and operational impacts before migrating additional datasets selectively as needed.
This approach avoids long, endless migration projects that consume engineering resources for months. It also prevents wasting time migrating infrequently accessed data, since distributed access via federated queries is sufficient.
The fastest way to scale your data lakehouse
Managed Iceberg delivers a host of benefits over non-managed approaches to Apache Iceberg, including:
- Reduced operational burden: Frees your teams from writing automation scripts so they can focus on high-value work.
- Consistent performance: Moves your maintenance efforts from reactive to proactive, meaning your Iceberg tables are always query-ready.
- Cost efficiency: Keeps storage and compute costs at optimal levels through automated optimization.
- Scalability: Removes manual maintenance as a barrier to adding more tables, schemas, and data.
- Reliability and governance: Ensures compliance with retention policies via full query history for tracking and auditing.
Together, these features bring a new level of scalability and simplicity to the Icehouse architecture. They deliver the power of the Iceberg table format combined with the operational ease of a fully managed data platform.
Apache Iceberg ensures a smooth transition from your existing data warehouse to a modern, open source, standards-based solution suited to your most demanding analytics and machine learning workloads. With Managed Iceberg, you can make the leap at a fraction of the time and cost.
Get started with Starburst today
Ready to experience Managed Iceberg for yourself? Contact us for a demo or read more about how Starburst Galaxy can transform your data lakehouse implementation with scalable, high-performance Apache Iceberg tables.
