Introduction
Hi Everyone,
In today's article, we will learn about Parquet vs Delta format in pyspark.
In the world of big data and analytics, choosing the right file format can significantly impact your data pipeline's performance, reliability, and scalability. Two popular formats that often come up in discussions are Apache Parquet and Delta Lake format. While both serve the purpose of efficient data storage, they cater to different use cases and requirements.
Parquet
Apache Parquet is an open-source, column-oriented data file format designed for efficient data storage and retrieval. It's optimized for analytics workloads and provides excellent compression and encoding schemes. Parquet files are immutable, meaning once written, they cannot be modified.
Delta Format
Delta Lake is an open-source storage framework that brings ACID transactions to Apache Spark and big data workloads. It's built on top of Parquet format but adds a transaction log that enables features like time travel, schema evolution, and reliable upserts/deletes.
Key Differences
| Feature |
Parquet |
Delta Format |
| Mutability |
Immutable files |
Supports updates/deletes |
| ACID Transactions |
No |
Yes |
| Schema Evolution |
Limited |
Full support |
| Time Travel |
No |
Yes |
| Concurrent Writes |
Not safe |
Safe with conflict resolution |
| Metadata Management |
Manual |
Automatic via transaction log |
| File Size |
Smaller |
Slightly larger (due to transaction log) |
| Query Performance |
Excellent for read-heavy |
Good, with additional features |
| Ecosystem Support |
Broad |
Growing (Spark-focused) |
When to Use Parquet?
- Write-once, read-many scenarios: When your data doesn't require frequent updates
- Maximum compression: When storage cost is a primary concern
- Broad tool compatibility: When you need to work with various analytics tools
- Simple data pipelines: When you don't need advanced features like time travel
- Archival storage: For long-term data retention with minimal access
When to Use Delta Format?
- Streaming data: When you have continuous data ingestion with potential late arrivals
- Data updates required: When you need to update or delete existing records
- Multi-user environments: When multiple teams need concurrent access to data
- Data quality requirements: When you need ACID guarantees and schema validation
- Time-based analysis: When you need to query historical versions of data
- Complex ETL pipelines: When you need reliable, fault-tolerant data operations
Summary
Parquet excels as a pure storage format for read-heavy analytics workloads where data immutability is acceptable. It offers excellent compression, broad ecosystem support, and optimal query performance for static datasets. Delta Format builds upon Parquet's strengths while adding enterprise-grade features like ACID transactions, schema evolution, and time travel. It's ideal for modern data lakes that require reliability, concurrent access, and the ability to handle changing data.