Your data lake just hit 10 petabytes, and your analysts are waiting for hours for queries. Your maintenance team is demanding row-level deletes for GDPR, and your data engineers are left to manually manage partition schemes that break down as the schemes evolve. Sound familiar?
This is the reality that pushed Netflix to create Apache Iceberg in 2017. What started as an internal solution to massive-scale data management problems has transformed into a table format reshaping how engineering teams tackle analytics workloads.
If you’re managing Snowflake environments and dealing with similar challenges, understanding Iceberg isn’t just helpful – it’s essential.
This guide breaks down everything you need to know, including:
- What Apache Iceberg is
- How it works
- Why you should use it
- Common use cases
- How to optimize Apache Iceberg performance
Read on to learn everything you need to know.
What is Apache Iceberg?
Apache Iceberg is an open-source table format made to handle huge analytic datasets via the reliability and simplicity of a SQL table. Think of it as a sophisticated organizational layer that sits on top of your data files, bringing database-like capabilities to data lake storage without forcing you into proprietary formats.
The core design is simple: Iceberg creates a metadata layer that tracks everything instead of treating your data lake as a collection of loosely organized files. That means keeping tabs on:
- Which files belong to which tables
- How data is structured
- What changes have occurred
- How to query it efficiently
This metadata-driven architecture allows for features that data lake formats struggle to provide, such as:
- ACID transactions
- Schema evolution
- Time travel
- Hidden partitioning
Originally developed by Netflix to handle petabyte-scale tables that were becoming unmanageable with Apache Hive, Iceberg was open-sourced in 2018 and graduated as a top-level Apache project in 2020. Today, companies like Apple, LinkedIn, and Netflix use it to manage analytics workloads that would have been impractical with traditional approaches.
What Are Iceberg Tables?
An Iceberg table is basically a collection of data files, usually stored in formats like Parquet or ORC, organized through a metadata layer that enables them to behave like a single, queryable table. This is a fundamentally different approach from traditional data lakes where files are loosely organized by directory structures.
The architecture consists of three layers:
- Data layer: The actual data files stored in formats like Parquet, and these files contain records and remain in your cloud storage (e.g., S3, Azure Blob, GCS).
- Metadata layer: This is the source of Iceberg’s power, containing manifest files that track which data files belong to the table and contain statistics (min/max values, null counts) that enable query optimization.
- Catalog layer: The catalog maintains pointers to the current metadata for each table – this can be Snowflake’s catalog, AWS Glue, or other catalog implementation. This catalog ensures all readers see a consistent table view.
This layered approach means thousands or millions of independent files can be treated as a unified table with all the guarantees you expect from a database – atomicity, consistency, isolation, and durability (ACID compliance).
How Does Apache Iceberg Work?
Iceberg’s elegance lies in how it handles read and write operations through its metadata structure.
When you go to write data to an iceberg table, the process follows a specific flow:
- New data files are written to storage first.
- Manifest files are created that describe these new files with detailed statistics.
- New metadata file is committed atomically, pointing to the updated manifest list.
This atomic commit means the table transitions cleanly from one consistent state to another, all while keeping the readers from seeing the partial wires.
Reading from Iceberg tables is where the metadata structure pays dividends. Query engineers first read the metadata file to understand the table’s current state. They scan manifest lists, which contain manifests, to identify relevant data files, using statistics to skip entire files that do not match query predicates (partition pruning and file skipping). Only the necessary data files are read and processed. This metadata-driven query planning can dramatically cut back on the amount of data scanned, especially for large tables with effective partitioning.
The snapshot-based model is especially crucial here. Every change to the table creates a new snapshot – a complete, consistent view of the table at that moment.
NOTE: Old snapshots remain accessible until explicitly expired, enabling time travel queries where you can query data as it existed at any point in history.
Why Use Apache Iceberg?
Iceberg is an easy solution for many of the problems that plague traditional data lake architectures – especially for teams running analytics at scale.
The top benefits of Apache Iceberg include:
- ACID transactions
- Schema evolution without rewrites
- Hidden partitioning
- Time travel and rollback
- Multi-engine compatibility
ACID Transactions
Apache Iceberg enables multiple writers to safely modify the same table concurrently. When you update customer records to comply with privacy regulations, those changes are atomic – they either complete entirely or not at all. You’re not stuck with partial updates corrupting your data.
Schema Evolution Without Rewrites
Traditional approaches often require copying entire petabyte-scale tables just to add a single field. Apache Iceberg means you can add columns, rename fields, change data types – all without rewriting existing data. This is invaluable when business requirements evolve.
Hidden Partitioning
Users don’t need to understand partitioning to get fast queries. Iceberg automatically handles partition values, and you can evolve partition schemes as data volumes or query patterns change. This removes a major source of user error and performance problems.
Time Travel and Rollback
This lets you query data as it existed hours, days, or weeks ago. That means if you accidentally delete data records, you can just roll back to a previous snapshot.
Multi-Engine Compatibility
The same Iceberg table can be read and written by Spark, Flink, Trino, Snowflake, and other engines, which eliminates data duplication and complex synchronization between systems. This means your ML team can use Spark while analysts query with Snowflake, all accessing the same source of truth.
NOTE: This is especially appealing for Snowflake users, since Iceberg tables enable you to store data in your own cloud storage while leveraging Snowflake’s query engine and governance feature. This enables true lakehouse architecture, where storage costs are separate from compute, and you can maintain full control over your data assets.
Common Apache Iceberg Use Cases
Iceberg is strongest in scenarios where traditional data lake formats fail. This includes:
- Data privacy compliance: Iceberg handles row-level deletes much more efficiently than traditional data lakes when it comes to GDPR or CCPA requiring you to delete specific customer records.
- Slowly changing dimensions: Iceberg is made for handling unpredictable changes for data like customer profiles, product catalogs, and other reference data points because of its ability to update individual records without republishing entire datasets.
- Multi-engine analytics workloads: Operations running diverse analytics stacks see enormous cost benefits and a reduction in errors when using Iceberg tables.
- Streaming and batch unification: Iceberg handles both streaming writes and batch loads seamlessly, so you can have real-time ingestion pipelines writing small files continuously while background compaction processes optimize table structure, no downtime or system coordination needed.
- Change data capture (CDC): Tracking changes in operational databases and replicating them to analytics environments is made much simpler with Iceberg’s support for efficient updates and deletes.
- Financial services: Banks and trading firms especially benefit from using Iceberg for transactions where ACID guarantees are non-negotiable because of its ability to handle frequent updatings while maintaining query performance.
- E-commerce analysts: Online retailers managing massive product catalogs, customer data, and order histories also benefit from Iceberg’s schema evolution and partitioning capabilities.
How to Optimize Apache Iceberg Performance
Getting maximum value from Iceberg requires more than a basic understanding of a few different optimization levers. We’ll break down each of these parts and how you can use them to their fullest extent.
Partitioning Strategy
For this first step, you’ll want to choose partition keys based on your query patterns. Date-based partitioning usually works best for time-series data, while categorical fields (region, product category) better fits dimensional queries.
Pro tip: Iceberg’s hidden partitioning means you can change strategies as patterns evolve without user-facing changes.
File Sizing
Target 512MB to 1GB per data file to get optimal query performance. This is a bit of a Goldilocks situation: too many small files increase metadata overhead, while too few large files reduce parallelism.
NOTE: Iceberg’s compactions operations rewrite small files into large ones automatically.
Table Maintenance
Regular compaction, snapshot expiration, and metadata cleanup are essential. Here’s how each of these work:
- Compaction: Consolidates small files created by streaming writes
- Snapshot expiration: Removes old table versions you no longer need for time travel
- Metadata cleanup: Prevents the metadata layer from growing indefinitely
Pro tip: For most Snowflake users, these operations can be automated, but understanding the tradeoffs is crucial for managing compute costs efficiently.
Z-Ordering
Z-ordering arranges data to optimize queries across multiple dimensions simultaneously for tables with multiple filter columns. This is particularly useful for tables with complex query patterns that filter on a lot of different columns.
Metadata Management
The metadata layer’s efficiency directly impacts query planning time. Use appropriate partition granularity – too fine-grained creates excessive mainfests, and too coarse reduces pruning effectiveness.
Pro tip: Monitor manifest file counts and consolidate when they grow too numerous.
Catalog Performance
Catalog operations can turn into bottlenecks at scale. Snowflake’s managed catalog handles this transparently, but if using external catalogs like AWS Glue, monitor catalog response times and consider implementing caching layers.
NOTE: Iceberg performance optimization isn’t fundamentally different from traditional database tuning. You’re still thinking about indexes (manifests), file sizes, and query patterns. The only difference is the scale at which these optimizations apply and the flexibility to adjust them as needs change.
Managing Iceberg Performance in Snowflake
While Iceberg brings tremendous flexibility and openness to your data architecture, the reality is that managing performance at scale is difficult. Table maintenance, compaction strategies, partition optimization, and metadata management all require ongoing attention.
For teams running Iceberg tables in Snowflake, this complexity compounds with the broader challenges of Snowflake warehouse optimization. Ask yourself:
- How do you ensure queries against Iceberg tables are using the right sized warehouses?
- When should compaction run, and on which compute resources?
- How do you track storage costs in your cloud provider separate from Snowflake compute costs?
Yuki Data specializes in solving exactly these challenges. Our platform provides automated Snowflake optimization that works seamlessly with Iceberg tables, ensuring you get the performance benefits of the open table format without the operational overhead.
Yuki takes the complexity out of managing Iceberg at scale, offering:
- Real-time monitoring
- Intelligent warehouse scaling
- Automated maintenance recommendations
Companies using Yuki have achieved an average of 37.6% savings on Snowflake costs while improving query performance and reducing the time engineering teams spend on manual optimization. If you’re adopting Iceberg in Snowflake or already running Iceberg workloads, request a demo to see how Yuki can optimize your entire data platform – from table maintenance to warehouse sizing to query optimization – no developer help required.
Apache Iceberg FAQ
What Is a Data Lake?
A data lake is a centralized repository that stores all your structured, semi-structured, and unstructured data at any scale. It differs from traditional data warehouses that require data to be structured before storage because data lakes can accept data in its raw form.
Data lake’s flexibility is what makes it so ideal for storing a diverse range of data types, including:
- Database tables
- Log files
- Images
- Sensor data
The only challenge is managing and querying this data efficiently, which is where Iceberg comes into play.
Is Apache Iceberg Better than Delta Lake?
“Better” depends on your specific ecosystems and requirements. Both Iceberg and Delta Lake provide similar core capabilities – ACID transactions, schema evolution, time travel, and data versioning.
Iceberg offers:
- Vendor-neutral capabilities
- Works seamlessly across Spark, Flink, Trino, Snowflake, and numerous other engines
- Formal specifical and multi-language implementations (that arguable make it more open)
- Metadata tree structure scales well for large tables
Delta Lake, developed by Databricks, is:
- Deeply optimized for the Spark ecosystem and offers excellent performance within that environment
- Transactional log approach can be simpler
- Requires more frequent maintenance at large scales
For Snowflake specifically, Iceberg integration is native and increasingly comprehensive, making it the natural choice. If you’re heavily invested in Databricks, Data Lake’s tight integrations can make it a better choice.
NOTE: Apache Hudi is another alternative worth mentioning, especially for optimizing streaming use cases and CDC workloads with frequent small updates. However, Iceberg’s broader engine support and simpler architecture have driven wider adoption.
Does Snowflake Use Apache Iceberg?
Yes, Snowflake provides comprehensive support for Apache Iceberg tables. As of 2024, Iceberg support in Snowflake is generally available in two primary catalog options:
- Snowflake-managed catalog: Snowflake acts as the Iceberg catalog, managing all metadata internally, providing performance comparable to native Snowflake tables with the flexibility of open formats.
- External catalog integration: Snowflake can connect to external Iceberg catalogs like AWS Glue, Snowflake Open Catalog (formerly Polaris), or other REST-compatible catalogs. This enables true multi-engine architectures where Spark, Flink, and Snowflake can all work with the same Iceberg tables.
This integration means you can create Iceberg tables directly in Snowflake using standard SQL commands, query them with full Snowflake performance and governance capabilities, and still maintain flexibility to access the same data from other rengines.
For organizations running Snowflake, this creates compelling cost optimization opportunities. By storing data in your cloud storage as Iceberg tables, you separate storage costs from computes while maintaining the full power of Snowflake’s query engine. This is especially valuable for archival data, long-term storage, or scenarios where you need multi-tool access to the same datasets.
