Modern data architecture has shifted toward the "Data Lakehouse," where open table formats like Iceberg, Hudi, Paimon, and Delta Lake provide database-like features (ACID transactions, time travel, and schema evolution) on top of cheap object storage like S3 or ADLS.
While they share the same goal of bringing order to the "wild west" of data lake files, they differ significantly in their origins and optimization strategies.
🤝 Core Similarities
All four formats share a foundational "DNA" that separates them from traditional file formats like plain Parquet or CSV:
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ACID Transactions: They use a metadata layer to ensure that writes either succeed completely or fail without corrupting the table.
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Time Travel: By maintaining historical snapshots, they allow you to query the data as it existed at a specific point in time.
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Schema Evolution: You can add, rename, or drop columns without rewriting the entire dataset.
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File Format Underneath: They all primarily use Apache Parquet for actual data storage, using a metadata layer (JSON or Avro) to track which files belong to the table.
⚔️ Key Differences at a Glance
| Feature | Apache Iceberg | Delta Lake | Apache Hudi | Apache Paimon |
| Origin | Netflix | Databricks | Uber | Alibaba / Flink |
| Primary Focus | Scalable Analytics | Spark Ecosystem | Incremental Upserts | Real-time Streaming |
| Architecture | Hierarchical Snapshots | Linear Transaction Log | Timeline-based | LSM-Tree (Log-Structured) |
| Partitioning | Hidden Partitioning (Automatic) | Manual / Liquid Partitioning | Manual | Bucket-based |
| Best For | Large, multi-engine lakes | Databricks/Spark users | Complex CDC & Updates | High-velocity Flink streams |
🧊 Apache Iceberg: The "Universal" Standard
Iceberg was designed at Netflix to solve the performance issues of Hive.6 It is highly engine-agnostic, meaning it works equally well with Spark, Trino, Flink, and Presto.
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Unique Strength: Hidden Partitioning. You don't have to manually maintain partition columns (like
year=2024/month=10). Iceberg handles the relationship between the data and the partition logic automatically, which prevents user errors and speeds up queries. -
Best Use Case: Large-scale enterprise data lakes where multiple different query engines need to access the same data reliably.
📐 Delta Lake: The Performance Powerhouse
Created by Databricks, Delta Lake is the most mature and widely adopted format. It is deeply integrated with the Apache Spark ecosystem.
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Unique Strength: Simplicity and Speed. In a Spark/Databricks environment, it offers features like Z-Ordering and "Liquid Partitioning" that automatically cluster data for maximum performance without complex tuning.
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Best Use Case: Organizations already heavily invested in the Databricks ecosystem or Spark-heavy ETL pipelines.
🏎️ Apache Hudi: The "Upsert" Specialist
Hudi (Hadoop Upsert Delta and Incremental) was built at Uber specifically to handle massive streams of updates and deletes (CDC - Change Data Capture) from databases.
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Unique Strength: Merge-On-Read (MOR) and Copy-On-Write (COW) modes. It allows you to balance the trade-off between write speed and read performance. It also features built-in "table services" like automatic compaction and cleaning.
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Best Use Case: Real-time database replication and scenarios requiring frequent, record-level updates.
🌀 Apache Paimon: The Streaming Native
Paimon (formerly Flink Table Store) is the newest of the four. It is built from the ground up to integrate with Apache Flink for high-speed streaming.
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Unique Strength: LSM-Tree Structure. Similar to NoSQL databases like Cassandra, Paimon uses an LSM-tree architecture to handle massive write throughput and provide low-latency queries on streaming data.
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Best Use Case: High-velocity real-time streaming pipelines and "streaming lakehouses" where data freshness is measured in seconds.
Summary of Design Philosophies
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Iceberg prioritizes flexibility and correct metadata handling at massive scale.
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Delta Lake prioritizes performance and ease of use within the Spark ecosystem.
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Hudi prioritizes incremental processing and efficient record-level updates.
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Paimon prioritizes streaming-first unification of batch and real-time data.