Making the Right Choice: When to Use Parquet Over Delta File Format

Nnaemezue Obi-Eyisi
2 min readOct 26, 2023

In the fast-evolving landscape of big data processing, choosing the right file format can significantly impact your data pipeline’s efficiency and effectiveness. Parquet and Delta are two popular formats, each with its own strengths and ideal use cases. In this article, we’ll explore when it makes sense to opt for the Parquet file format over Delta.

1. Query-Optimized Analytics

Scenario: You have a data processing pipeline primarily designed for running analytical queries on extensive datasets, and you don’t need features like ACID transactions or data versioning.

Solution: In this case, Parquet shines as the preferred choice. Its design prioritizes high query performance and is widely supported by various data processing tools. You can efficiently analyze large datasets without the added complexity of Delta’s features.

2. Simplicity

Scenario: Your data pipeline and processing requirements are straightforward, and you don’t require advanced capabilities like schema evolution, strong data consistency, or transaction support.

Solution: Parquet offers a simple and efficient solution in such cases. Its straightforward structure is easy to work with and can streamline your data pipeline without introducing unnecessary complexities.

3. Compatibility

Scenario: Your existing ecosystem is built around the Parquet file format, and you want to maintain compatibility with your current toolset.

Solution: If Parquet is already a standard in your environment, there may be no need to introduce the complexity of Delta. Parquet enjoys broad compatibility with various data processing frameworks and tools, ensuring seamless integration with your existing infrastructure.

4. Performance-Intensive Workloads

Scenario: You have read-heavy workloads where query speed is of paramount importance, and you need an optimized file format.

Solution: Parquet’s columnar storage design and efficient compression options make it a suitable choice for performance-intensive workloads. It excels at delivering rapid query results and is an ideal fit for such scenarios.

5. Data Warehousing and Reporting

Scenario: Your focus is on data warehousing and generating reports efficiently.

--

--

Nnaemezue Obi-Eyisi

I am passionate about empowering, educating, and encouraging individuals pursuing a career in data engineering. Currently a Senior Data Engineer at Capgemini