Differences between Avro, Parquet, and Iceberg in a structured, comparison-table format. It covers technology aspects, properties, and real-world use cases to help architects, engineers, and decision-makers choose the right technology for their data platform.
High-Level Classification
Technology
Category
What It Solves
Avro
Serialization / Row-based file format
Efficient data exchange & streaming with schema enforcement
Parquet
Columnar storage file format
Fast analytical queries & efficient storage
Iceberg
Table format (metadata layer)
Reliable, scalable data lake tables with ACID guarantees
Core Properties Comparison
Property
Avro
Parquet
Iceberg
Data Orientation
Row-based
Column-based
File-format independent
Typical File Extension
.avro
.parquet
Uses Parquet / Avro / ORC
Schema Storage
Embedded in file
Stored in file footer
Centralized table metadata
Schema Evolution
Excellent
Limited
Excellent (safe evolution)
Compression Support
Yes (Snappy, Deflate)
Yes (Snappy, GZIP, ZSTD)
Depends on underlying file format
Metadata Management
Minimal
Per-file metadata
Versioned snapshots & manifests
ACID Transactions
No
No
Yes
Time Travel
No
No
Yes
Updates & Deletes
Not supported
Not supported
Supported (row-level)
Concurrency
Single writer
Single writer
Multi-writer safe
Technology & Architecture Comparison
Aspect
Avro
Parquet
Iceberg
Role in Data Stack
Ingestion / Messaging
Storage layer
Table management layer
Read Optimization
Sequential reads
Column pruning & predicate pushdown
Metadata + file pruning
Write Pattern
Append-heavy
Batch writes
Append, overwrite, merge
Partition Handling
Manual
Static partitions
Hidden & evolving partitions
Small File Handling
Poor
Poor
Built-in compaction
Cloud Object Store Friendly
Limited
Yes
Designed for cloud storage
5. Performance Characteristics
Area
Avro
Parquet
Iceberg
Streaming Performance
Excellent
Poor
Not designed for streaming
Analytical Query Performance
Poor
Excellent
Excellent
Large Dataset Handling
Limited
Good
Excellent (PB-scale)
Metadata Overhead
Low
Medium
High (but optimized)
Use Case Comparison
Use Case
Avro
Parquet
Iceberg
Event Streaming (Kafka)
Best choice
Not suitable
Not suitable
Data Lake Storage
Not ideal
Good
Best choice
BI & Analytics
Poor
Excellent
Excellent
Incremental Loads
No
No
Yes
CDC / Merge Operations
No
No
Yes
Auditing & Time Travel
No
No
Yes
Multi-Engine Access
Limited
Good
Excellent
Tooling & Ecosystem Support
Technology
Supported Engines
Avro
Kafka, Spark, Flink
Parquet
Spark, Hive, Presto, Trino
Iceberg
Spark, Flink, Trino, Athena, Snowflake
Typical Data Architecture Mapping
Data Platform Layer
Recommended Technology
Event Ingestion
Avro
Raw / Bronze Layer
Parquet
Curated / Silver Layer
Iceberg + Parquet
Analytics / BI
Iceberg
Machine Learning
Iceberg
Decision Guidance
Scenario
Recommendation
Real-time streaming pipelines
Avro
Read-heavy analytical workloads
Parquet
Enterprise data lake with updates
Iceberg
Schema evolution at scale
Iceberg
Simple batch storage
Parquet
Data Storage Structure
Below is the data order_id | user_id | product | amount | event_time
