Apache Iceberg vs. Delta Lake: A Comprehensive Comparison
Apache Iceberg and Delta Lake are two of the most widely used open-source table formats for data lakes. While both enable ACID transactions, schema evolution, and time travel, they have key differences in architecture, ecosystem support, and governance.
In June 2024, Databricks acquired Tabular, the company founded by the creators of Apache Iceberg, which has sparked discussions about their interoperability and future convergence. However, they remain distinct technologies with different strengths.
This article provides a side-by-side comparison of Apache Iceberg and Delta Lake.
1. Origin & Governance
| Feature | Apache Iceberg | Delta Lake |
|---|---|---|
| Developed By | Netflix (2017) | Databricks (2017) |
| Open-Sourced | 2018 | 2019 |
| Governing Body | Apache Software Foundation | Linux Foundation (Databricks-led) |
| Vendor-Neutral | ✅ Yes | ⚠️ No (Databricks influence) |
- Apache Iceberg is vendor-neutral and is adopted by AWS, Snowflake, Google Cloud, and others.
- Delta Lake is closely tied to Databricks, though it now supports other engines.
2. Architecture & Design
| Feature | Apache Iceberg | Delta Lake |
|---|---|---|
| ACID Transactions | ✅ Yes | ✅ Yes |
| Schema Evolution | ✅ Yes (flexible, supports delete column) | ✅ Yes (does not support delete column) |
| Time Travel | ✅ Yes | ✅ Yes |
| Metadata Management | ✅ Scalable snapshots | ⚠️ Single _delta_log file (scaling issue) |
| Concurrency Control | ✅ Optimistic (lock-free) | ✅ Optimistic |
| Partitioning | ✅ Hidden partitioning (automatic pruning) | ⚠️ Manual partitioning |
- Iceberg’s metadata scales better because it maintains snapshots instead of using a transaction log.
- Delta Lake relies on a single
_delta_log, which can be a bottleneck for very large datasets.
3. Engine Compatibility
| Processing Engine | Apache Iceberg | Delta Lake |
|---|---|---|
| Apache Spark | ✅ Full support | ✅ Full support |
| Trino/Presto | ✅ Full support | ⚠️ Partial support |
| Flink | ✅ Full support | ⚠️ Limited support |
| Hive | ✅ Full support | ⚠️ Limited support |
| AWS Athena | ✅ Full support | ⚠️ Limited support |
| Databricks | ✅ Now supported (since Tabular acquisition) | ✅ Native support |
| Snowflake | ✅ Full support | ⚠️ Limited support |
- Iceberg is more open and integrates well with multiple engines beyond Spark.
- Delta Lake was initially designed for Databricks, but support for other engines has improved.
4. Performance & Optimization
| Feature | Apache Iceberg | Delta Lake |
|---|---|---|
| Metadata Scaling | ✅ Efficient, scalable | ⚠️ _delta_log can be a bottleneck |
| Partition Pruning | ✅ Hidden partitioning (automatic) | ⚠️ Manual partitioning required |
| Merge-on-Read | ✅ Supported | ✅ Supported |
| Read Performance | ✅ Faster (scalable metadata) | ⚠️ Slower when _delta_log grows |
| Write Performance | ⚠️ Slightly slower (snapshot-based updates) | ✅ Faster for batch writes |
- Iceberg outperforms Delta Lake in large-scale metadata management.
- Delta Lake writes are generally faster, but reading large
_delta_logfiles can be slow.
5. Cloud & Storage Support
| Storage Backend | Apache Iceberg | Delta Lake |
|---|---|---|
| AWS S3 | ✅ Supported | ✅ Supported |
| Azure ADLS | ✅ Supported | ✅ Supported |
| Google Cloud Storage (GCS) | ✅ Supported | ✅ Supported |
| HDFS | ✅ Supported | ⚠️ Limited support |
Both support major cloud providers, but Iceberg is more hybrid-friendly.
6. Industry Adoption & Use Cases
| Use Case | Apache Iceberg | Delta Lake |
|---|---|---|
| Large-Scale Analytics | ✅ Netflix, AWS Athena, Snowflake | ✅ Databricks |
| Machine Learning | ✅ Snowflake ML, Trino | ✅ Databricks ML |
| Data Lakehouse | ✅ AWS Lake Formation, Google BigLake | ✅ Databricks Lakehouse |
- Delta Lake is dominant in Databricks Lakehouse implementations.
- Apache Iceberg is preferred for multi-cloud and hybrid systems.
7. Final Thoughts: Which One Should You Use?
Use Apache Iceberg if:
✅ You need multi-engine support (Flink, Trino, Athena, etc.).
✅ You work with Snowflake, AWS Athena, or Hive.
✅ You want better scalability and metadata management.
Use Delta Lake if:
✅ You are using Databricks.
✅ You need tight Spark integration and transactional guarantees.
✅ You prefer simpler lakehouse implementation.
With Databricks acquiring Tabular, the lines between Iceberg and Delta Lake may blur, but both remain distinct for now. 🤗
📌 Key Takeaways:
- Apache Iceberg is more open, scalable, and supports multiple engines.
- Delta Lake is tightly integrated with Databricks and is better for Spark-heavy workloads.
- The future will likely see increased interoperability between the two due to Databricks' acquisition of Tabular.
Quiz?
Check your knowledge from the above topic by answering the questions below.
Click on the answer you believe to be correct for each question to see if you are right or wrong!
Q.1. Governance
What organisation governs Apache Iceberg?
Select one answer from the below
Databricks
Linux Foundation
Apache Software Foundation
Netflix
Q.2. Origin of Delta Lake
Who originally developed Delta Lake?
Select one answer from the below
Netflix
Snowflake
Databricks
Q.3. Metadata Management
Which of the following best describes Apache Iceberg’s metadata management?
Select one answer from the below
Uses a single `_delta_log` file to track all transactions
Stores scalable metadata snapshots for fast table listing
Requires manual partitioning for query optimization
Does not support time travel
Q.4. Schema Evolution
Which table format allows deleting columns as part of schema evolution?
Select one answer from the below
Apache Iceberg
Delta Lake
Both A and B
Neither A nor B
Q.5. Performance Bottlenecks
Which of the following can become a performance bottleneck in Delta Lake?
Select one answer from the below
Too many small files
Large `_delta_log` transaction logs
Lack of schema enforcement
Limited metadata storage
Q.6. Time Travel
Which feature allows both Apache Iceberg and Delta Lake to query historical versions of data?
Select one answer from the below
Data Skipping
Snapshot Isolation
Time Travel
Change Data Capture (CDC)
Q.7. Streaming Support
Which table format has better support for streaming workloads?
Select one answer from the below
Apache Iceberg
Delta Lake
Apache Hudi
Parquet
Q.8. Interoperability
Which table format is designed to work with multiple query engines, including Trino, Flink, and Snowflake?
Select one answer from the below
Delta Lake
Apache Iceberg
Apache Hudi
ORC
Q.9. Databricks Acquisition
What was the significance of Databricks acquiring Tabular in 2024?
Select one answer from the below
Databricks discontinued Apache Iceberg
Databricks now supports both Iceberg and Delta Lake
Delta Lake was merged into Apache Iceberg
Apache Iceberg is no longer open-source
Q.10. Metadata Scaling
Which table format scales better for large metadata operations due to its snapshot-based approach?
Select one answer from the below
Delta Lake
Apache Iceberg
Apache Hudi
CSV files
Q.11. Cloud Support
Which cloud provider has native support for Apache Iceberg in its data lake services?
Select one answer from the below
AWS
Azure
Google Cloud
All of the above
Q.12. Query Optimization
Which table format automatically optimizes partitions without requiring manual partitioning?
Select one answer from the below
Delta Lake
Apache Iceberg
Apache Hudi
ORC
Q.13. Schema Evolution Flexibility
Which table format offers more flexible schema evolution, including support for deleting columns?
Select one answer from the below
Apache Iceberg
Delta Lake
Both A and B
Neither A nor B
Q.14. Concurrency Control
Which concurrency control method does both Apache Iceberg and Delta Lake use?
Select one answer from the below
Pessimistic locking
Two-phase commit
Optimistic concurrency control
Lock-based transactions
Q.15. Adoption in Snowflake
Which open table format is fully supported by Snowflake for external tables?
Select one answer from the below
Delta Lake
Apache Iceberg
Apache Hudi
None of the above
Q.16. Adoption by Cloud Providers
Which cloud provider has native support for Apache Iceberg in its data lake services?
Select one answer from the below
AWS
Azure
Google Cloud
All of the above
Q.17. Query Performance
Which table format scales better for large metadata operations due to its snapshot-based approach?
Select one answer from the below
Delta Lake
Apache Iceberg
Apache Hudi
CSV files
Q.18. Query Optimization
Which table format automatically optimizes partitions without requiring manual partitioning?
Select one answer from the below
Delta Lake
Apache Iceberg
Apache Hudi
ORC
Q.19. Time Travel Feature
Which feature allows both Apache Iceberg and Delta Lake to query historical versions of data?
Select one answer from the below
Data Skipping
Snapshot Isolation
Time Travel
Change Data Capture (CDC)
Q.20. Performance Bottlenecks in Delta Lake
Which of the following can become a performance bottleneck in Delta Lake?
Select one answer from the below
Too many small files
Large `_delta_log` transaction logs
Lack of schema enforcement
Limited metadata storage
Q.21. Lakehouse Architecture
Which table format is most closely associated with the Lakehouse architecture?
Select one answer from the below
Apache Iceberg
Apache Hudi
Delta Lake
Parquet
Q.22. Adoption in Data Warehouses
Which table format is more widely supported in cloud-based data warehouses like Snowflake and BigQuery?
Select one answer from the below
Delta Lake
Apache Iceberg
Apache Hudi
None of the above
🔗 References
Post Tags: