When your data lake is growing faster than your team can optimize it, it’s time to look into solutions like Apache Iceberg. It’s the key to saving your engineers time on managing metadata instead of building features or – avoiding tables that used to return results in seconds but now takes minutes.
This is why Databricks has made Apache Iceberg a key part of its lakehouse architecture.
If you’re reviewing modern data platforms or trying to understand if Iceberg on Databricks makes sense for your organization, this guide breaks down everything you need to know:
- What Apache Iceberg is and what makes it different from traditional table formats
- How it integrates with Databricks
- If Iceberg on Databricks is worth investing in for your team
What exactly is Apache Iceberg?
Apache Iceberg is an open table format designed specifically for huge analytic datasets. Iceberg adds a metadata layer that tracks every file, partition, and scheme change in your table, unlike traditional Hive tables or basic Parquet files.
Think of it as version control for your data lake. That means every:
- Write creates a new snapshot
- Query reads from a consistent view
- Schema change is tracked and reversible
Why Should You Care About Iceberg Tables
For engineering leaders, this solves three critical problems that frequently bother data lakes:
- Performance at scale: Iceberg uses hidden partitioning and aggressive metadata pruning to avoid full table scans so when you query a table with billions of rows, Iceberg knows exactly which files contain your data.
- Reliable concurrent writes: Multiple jobs can write to the same table simultaneously without corrupting data or requiring complex locking mechanisms, which is a big deal for traditional formats.
- Time travel without overhead: Iceberg maintains snapshots without duplicating data, making time travel queries fast and storage-efficient.
How Databricks Implements Apache Iceberg
Databricks supports Iceberg with two primary paths: Unity Catalog and Iceberg tables with external catalogs. It’s important to understand the difference between the two because it affects everything from performance to governance.
Let’s break this down.
Unity Catalog Integration
When you create an Iceberg table in Databricks with Unity Catalog, you get the full lakehouse experience. Unit Catalog acts as your metastore, tracking table metadata, managing access controls, and providing lineage tracking across your entire platform.
This integration means your Iceberg tables can benefit from:
- Centralized governance: Apply row-level security, column masking, and attribute-based access control across all your tables, regardless of format.
- Cross-cloud compatibility: Query the same Iceberg tables across AWS, Azure, and GCP without rebuilding your data pipelines.
- Automatic optimization: Databricks can run background maintenance jobs to compact small files, remove old snapshots, and optimize table layouts.
External Catalog Support
Organizations with existing Iceberg deployments using Hive Metastore, AWS Glue, or other catalogs, Databricks can connect to external Iceberg tables. This is especially important when you’re running a multi-engine architecture where Databricks is one of several compute engines reading the same Iceberg tables.
Pro tip: It’s essential to build flexibility here for teams migrating from other platforms or maintaining hybrid architectures. You’re not locked into Databricks-specific table formats, which reduces vendor lock-in and keeps your options open.
Choosing Between Databricks Iceberg vs. Delta Lake
Now we get to the question every Databricks user eventually asks: should you use Iceberg or Delta Lake?
Delta Lake is one of Databricks’ native table formats. Features include:
- Tighter integration with the Databricks system
- Includes features like Change Data Feed, Z-Ordering, and Liquid Clustering (that aren’t available in Iceberg)
- For teams fully committed to Databricks, Delta Lake often provides better performance and simpler operations
In comparison, Iceberg shines when:
- You need multi-engine compatibility
- Open format allows for ecosystem flexibility
For a deeper comparison of Databricks and Snowflake architectures, check out our deep dive into Snowflake vs Databricks[a] to learn how each platform approaches lakehouse design differently.
But practically, you’ll want to:
- Use Delta Lake when Databricks is your primary compute engine or you want maximum performance
- Use Iceberg when you need portability across engines or you’re building on existing Iceberg infrastructure
Databricks Apache Iceberg Cost and Operational Drawbacks
Iceberg tables introduce a level of architectural complexity that often makes your engineers and your cloud costs unhappy. The biggest drawbacks here are:
Storage costs
Iceberg maintains metadata files, manifest files, and multiple snapshot versions, and this metadata overhead usually adds 1-3% to total storage costs for well-maintained tables.
Poorly managed tables, though, with excessive snapshots, can see metadata grow to 10% or more of storage.
Compute costs
Iceberg’s metadata operations are voracious compute consumers. Reading metadata files, planning queries, and performing snapshot operations all require cluster time. For tables with millions of files, metadata operations can take seconds – or even minutes.
The key to managing compute costs is proper table design:
- Appropriate partition granularity
- Regular compactions
- Metadata optimization
All of these reduce the compute needed for typical queries.
Making the Decision: When Iceberg on Databricks Makes Sense
Iceberg on Databricks makes the most sense when you:
- Need true open table formats: Your architecture includes multiple compute engines (Spark, Trino, Flink) that need to share tables.
- Value portability over optimization: You want the flexibility to move workloads between cloud providers or processing engines without vendor lock-in.
- Have existing Iceberg infrastructure: You’re already running Iceberg elsewhere and want to add Databricks as a compute option.
- Require strict open-source compliance: Some organizations have policies that prefer Apache-licensed formats over vendor-specific alternatives.
On the other hand, you should consider Delta Lake when you’re:
- Fully committed to the Databricks ecosystem
- Want maximum performance and feature integration
If you’re running Snowflake as your primary data warehouse, the comparison gets a bit more complicated. Snowflake’s architecture and optimization approach differs significantly from lakehouse patterns, and the choice between platforms depends heavily on workload characteristics and existing infrastructure.
Taking Control of Your Data Platform Costs
One truth remains constant no matter if you’re running Databricks with Iceberg, Delta Lake, or evaluating alternatives like Snowflake: cloud data platforms generate costs faster than most teams can optimize them.
If you’re managing a Snowflake environment and facing challenges around query optimization, warehouse sizing, and cost management, Yuki can take those problems off your hands. Our platform continuously optimizes your Snowflake workloads. Users have seen up to 30% in cost savings without requiring any code changes or engineering time.
Though we’ve covered Databricks and Iceberg in this guide, many teams find that Snowflake’s architecture better fits their needs when paired with proper optimization. Yuki ensures you get maximum value from that investment – automatically managing the optimizations that would otherwise consume your team’s time.
Ready to see what 30% savings looks like in your Snowflake environment? Talk to our team about how Yuki delivers plug-and-play optimization for data leaders who want results, not more engineering projects.
[a]link to this article once live
