“The battle between open table formats isn’t just technical – it’s about who controls the future of data architecture. Delta Lake, Iceberg, and Hudi each represent different visions of how data should be organized, accessed, and governed. The winner will shape how we build data platforms for decades to come.” – Matei Zaharia
Delta Lake vs. Apache Iceberg vs. Apache Hudi: A Practical Analysis for Leadership and Engineers
Data lakes have become the foundation of modern data platforms, but traditional data lakes have long suffered from governance, consistency, and performance challenges. To address these issues, a new generation of transactional data lake table formats has emerged: Delta Lake, Apache Iceberg, and Apache Hudi.
For CEOs, CTOs, and VPs of Engineering, the choice of a table format directly impacts total cost of ownership (TCO), performance, vendor lock-in, and long-term sustainability of the data platform. For data engineers, the primary concerns are query performance, ease of implementation, and feature richness.
This blog provides a detailed comparative analysis of Delta Lake, Iceberg, and Hudi, breaking down each format’s capabilities, best-fit use cases, community strength, cost considerations, and long-term viability.
How We Conducted the Analysis
The analysis was performed by:
- Reviewing technical documentation and benchmarking reports.
- Consulting industry practitioners using each format at scale.
- Examining real-world case studies across different industries.
- Comparing costs of implementation, infrastructure, and long-term maintenance.
- Analyzing the community strength and vendor involvement in each ecosystem.
Constraints:
- The analysis assumes cloud-native environments (AWS, Azure, GCP).
- Performance metrics depend on query engines (Spark, Trino, Presto, Snowflake, etc.).
- Future roadmap projections are based on publicly available information.
What Are Delta Lake, Iceberg, and Hudi?
Delta Lake
- Developed by: Databricks (Open-source under the Linux Foundation)
- Optimized for: ACID transactions, schema enforcement, and time travel on Apache Spark
- Storage format: Parquet-based, with a transaction log (delta log)
- Best for: Large-scale batch and streaming workloads with deep integration into Spark-based architectures
Apache Iceberg
- Developed by: Netflix, later open-sourced (Now under Apache Software Foundation)
- Optimized for: Analytical queries and multi-engine support (Spark, Trino, Flink, Hive, etc.)
- Storage format: Parquet, ORC, Avro with a metadata catalog
- Best for: Separation of compute and storage, large-scale analytical workloads, and data lakehouse architectures
Apache Hudi
- Developed by: Uber, later open-sourced (Now under Apache Software Foundation)
- Optimized for: Real-time data ingestion and incremental processing
- Storage format: Parquet-based, with indexing and metadata tracking
- Best for: Low-latency upserts, CDC (Change Data Capture), and real-time data pipelines
Key Comparison Factors
1. Performance & Query Efficiency
| Feature | Delta Lake | Iceberg | Hudi |
| Optimized for | Spark | Multi-engine | Real-time ingestion |
| Query Performance | Fast for Spark, but slower outside Spark | Best for analytics, optimized reads | Optimized for updates, but complex query planning |
| Indexing | Basic | Advanced hidden partitioning | Advanced bloom filters, indexing |
| Streaming Support | Near real-time | Eventual consistency | Best-in-class CDC support |
- Iceberg performs best for large-scale OLAP workloads due to optimized metadata management.
- Hudi is the go-to choice for near real-time ingestion pipelines and CDC use cases.
- Delta Lake is ideal for Spark-heavy environments, but it locks into Spark-based execution.
2. Total Cost of Ownership (TCO)
| Cost Factor | Delta Lake | Iceberg | Hudi |
| Infrastructure Cost | Higher for large-scale Spark workloads | More efficient query execution saves compute | Lower storage costs, but indexing can add complexity |
| Operational Overhead | Low in Databricks, higher outside | Medium (requires catalog setup) | High for tuning ingestion & compaction |
| Vendor Lock-in | Databricks ecosystem preference | Open and cloud-agnostic | Open-source, but tuning is complex |
- Iceberg has the lowest TCO for open and cloud-agnostic architectures.
- Delta Lake locks in users to Databricks unless deployed in OSS mode, increasing long-term costs.
- Hudi requires active tuning but has strong cost efficiency in real-time pipelines.
3. Community Strength & Ecosystem
| Factor | Delta Lake | Iceberg | Hudi |
| Adoption | High in Databricks, growing outside | Broad across AWS, Snowflake, Trino | Niche, real-time data processing |
| Multi-Engine Support | Limited outside Spark | Best-in-class (Spark, Trino, Flink, Snowflake) | Strong for Spark, Flink |
| Community Contributions | Led by Databricks, mixed open-source engagement | Broad vendor adoption (Netflix, AWS, Snowflake, etc.) | Uber-led, growing but smaller |
- Iceberg has the strongest open-source community and multi-vendor backing (AWS, Snowflake, Cloudera).
- Delta Lake is deeply tied to Databricks but has increasing OSS adoption.
- Hudi remains niche but is the best choice for streaming-heavy architectures.
Best Applications for Each Format
| Use Case | Best Choice |
| Lakehouse with Spark workloads | Delta Lake |
| Multi-engine analytics (Spark, Trino, Flink) | Iceberg |
| Low-latency ingestion & real-time analytics | Hudi |
| Large-scale OLAP workloads with decoupled compute & storage | Iceberg |
| Enterprise Databricks deployment | Delta Lake |
| Streaming ingestion and change data capture (CDC) | Hudi |
Final Verdict: Choosing the Right Table Format
- For Enterprises Deep in Databricks → Delta Lake is the obvious choice.
- For Open, Cloud-Agnostic Architectures → Iceberg wins for long-term sustainability.
- For Streaming and Real-Time Use Cases → Hudi provides the best CDC and ingestion efficiency.
Leadership Takeaways
- CTOs & VPEs: Iceberg offers the best multi-engine flexibility and lowest long-term cost.
- CEOs: Beware of Databricks lock-in with Delta Lake if cost reduction is a goal.
- Data Engineers: If CDC and real-time processing are priorities, Hudi is the right tool.
Wrapping up…
The decision between Delta Lake, Iceberg, and Hudi is not just a technical one—it’s a business decision that impacts long-term TCO, vendor lock-in, operational complexity, and data strategy.
- Iceberg is the best long-term bet for multi-engine, cloud-native architectures.
- Delta Lake is great if you’re already in the Databricks ecosystem.
- Hudi is the best option for real-time ingestion, CDC, and streaming-heavy workloads.
Each organization’s needs will differ, and the choice should be data-driven, cost-aware, and aligned with future growth strategies.
