Apache Iceberg vs. Delta Lake: A Complete Comparison

What Are Iceberg and Delta Lake?

Delta vs Iceberg – Does It Even Matter Anymore?

Delta vs Iceberg: Does the storage format you choose still matter?

https://www.advancinganalytics.co.uk/blog/delta-vs-iceberg-does-it-even-matter-anymore?utm_campaign=6953562-Data%20Engineering%202025&utm_content=343886889&utm_medium=social&utm_source=linkedin&hss_channel=lcp-12986209

• Apache Iceberg
• Open-source table format created at Netflix.
• Manages large analytic datasets with ACID guarantees.
• Backed by the Apache Software Foundation.
• Works with multiple engines: Spark, Flink, Trino, Presto, Snowflake.

• Delta Lake
• Open-source project led by Databricks.
• Brings reliability to data lakes with ACID transactions and schema enforcement.
• Deeply integrated with Apache Spark and the Databricks ecosystem.
• Recently open-sourced under the Linux Foundation.

Architecture Differences

Iceberg

• Snapshot-based metadata with manifest and metadata files.

• Hidden partitioning (no need to hardcode partitions in queries).

• Delete files (equality deletes and position deletes) enable efficient row-level operations.

• Designed for multi-engine interoperability (Spark, Flink, Trino, Presto).

Delta Lake

• Relies on a transaction log (_delta_log) stored in JSON and Parquet.

• Uses Parquet data files as the storage layer.

• Strongly tied to Spark (although connectors exist for Presto, Trino, and Flink).

• Metadata management is simpler, but less scalable for ultra-large datasets compared to Iceberg.

🔹 Iceberg’s Metadata Tree

Table Metadata File
 ├── Schema, partition spec, properties
 ├── Snapshot list
 │     ├── Snapshot 1 → Manifest List → Manifest → Data Files
 │     ├── Snapshot 2 → ...
 │     └── Snapshot N

• Snapshots track table state.

• Manifests store metadata about groups of data files.

• Delete files handle row-level deletes efficiently.

🔹 Delta Lake’s Transaction Log

_delta_log/
 ├── 00000001.json
 ├── 00000002.json
 ├── ...
 └── 00123456.checkpoint.parquet

• JSON log files record every transaction.

• Periodic Parquet checkpoints speed up recovery.

• Parquet files store actual table data.

Update & Merge Operations

• Iceberg
• Supports row-level operations via equality deletes and position deletes.
• Can operate in Copy-on-Write (rewrite files) or Merge-on-Read (apply deletes at read time).
• Efficient for streaming + batch workloads.

• Delta Lake
• Uses file rewrites for most updates/deletes.
• Optimized for Spark; fast in Databricks environments.
• Row-level deletes are supported but less granular than Iceberg’s.

Schema Evolution

• Iceberg:
• Schema evolution is robust — columns can be added, dropped, renamed, or reordered safely.
• Uses column IDs rather than just names for backward compatibility.

• Delta Lake:
• Schema evolution supported (add columns, merge schema), but renames and reorders are limited.
• Strong enforcement to prevent accidental corruption.

Ecosystem & Compatibility

• Iceberg
• Engine-neutral: Spark, Flink, Trino, Presto, Dremio, Snowflake.
• Gaining traction in multi-cloud and open-source communities.
• Better for organizations that want to avoid vendor lock-in.

• Delta Lake
• Best experience on Databricks with Spark.
• Expanding connectors, but less engine-agnostic than Iceberg.
• Strong choice if you’re already committed to Databricks.

Feature Comparison Table

Which Should You Choose?

• Iceberg: Works across Spark, Flink, Trino, Presto, Dremio, Snowflake.
• Favored in multi-cloud, multi-engine setups.

• Delta Lake: Best in Spark + Databricks.
• Other connectors exist, but Spark is first-class.

• Pick Iceberg if:
• You need multi-engine support (Spark, Flink, Trino, Snowflake, etc.).
• You want to avoid vendor lock-in.
• You have very large datasets (billions of files).

• Pick Delta Lake if:
• You’re already on Databricks or heavily invested in Spark.
• You want fast, reliable performance within a managed environment.
• Your team values simplicity and a Spark-first ecosystem.