Apache Parquet is an open-source file format designed for efficient storage and fast querying of large datasets, especially in big data and analytics systems.
Parquet is a column-oriented storage format. Instead of storing data row by row like a spreadsheet:
Row1: name, age, city
Row2: name, age, city
It stores data column by column:
Names → [Alice, Bob, Charlie]
Ages → [30, 25, 40]
Cities→ [NY, LA, SF]
Why we use Apache Parquet?
• Faster analytics queries
• Efficient compression
• Reduced disk usage
• Optimized for big data processing
It’s heavily used with tools like:
• Apache Spark
• Apache Hadoop
• Apache Hive
Key features of Parquet
1. Columnar storage
• Reads only needed columns
• Improves query performance
2. High compression
• Similar data stored together → compresses better
3. Schema support
• Data structure is stored with the file
4. Predicate pushdown
• Filters data early (faster queries)
5. Efficient I/O
• Reads less data from disk
When to use Parquet?
Use it when:
• You’re doing analytics (OLAP queries)
• Working with large datasets (GB–PB)
• Need fast read performance
• Using Spark/Hadoop ecosystem
• Running queries like:
- “Get average salary”
- “Filter users by country”
When NOT to use Parquet?
• Real-time streaming data (better: Avro)
• Frequent row-level updates
• Small datasets
• Write-heavy workloads
How Parquet works?
• Data is split into columns
• Each column is stored separately
• Data is compressed per column
• Queries read only required columns
Advantages
• Very fast for analytics
• Excellent compression
• Reduces I/O significantly
• Supports complex nested data
• Widely adopted in big data ecosystem
Disadvantages
• Slower writes compared to row formats
• Not ideal for transactional systems
• Harder to debug (binary format)
• Not suitable for frequent updates
Alternatives
1. Apache Avro
• Row-based
• Better for streaming and writes
2. ORC
• Similar to Parquet (used in Hive)
• Highly optimized for Hive workloads
3. JSON
• Human-readable, but inefficient
4. CSV
• Simple but no schema, poor performance
