Apache Parquet vs DuckDB: What are the differences?

Introduction: Apache Parquet and DuckDB are two widely used columnar storage file formats that are utilized in big data and data warehousing environments. While they both serve the purpose of efficiently storing and querying large datasets, there are key differences between the two technologies.

1. Data Storage Format: Apache Parquet is a file format that stores data in a columnar format, which improves query performance by allowing the system to read only the necessary columns. On the other hand, DuckDB is a columnar database system that stores data in columnar format within the database itself, enabling faster query processing compared to traditional row-based databases.

2. Query Processing: Apache Parquet is primarily meant for storing and efficiently reading large datasets, while DuckDB is designed for both storing and querying data, providing support for complex SQL queries and aggregations. DuckDB offers optimized query processing techniques such as vectorization and SIMD instructions, which can significantly improve query performance.

3. File System Dependency: Apache Parquet relies on Hadoop Distributed File System (HDFS) or other file systems as its underlying storage layer, making it suitable for integration with Hadoop ecosystem tools. In contrast, DuckDB is a self-contained database system that does not have any external dependencies on file systems, enabling easy deployment and operation in various environments.

4. Supported Data Types: Apache Parquet supports a wide range of data types including simple types like integers and strings, as well as complex types like nested structures and arrays. On the other hand, DuckDB provides support for various data types that are commonly used in relational databases, allowing users to work with diverse datasets seamlessly.

5. Compression Techniques: Apache Parquet employs advanced compression techniques such as dictionary encoding, run-length encoding, and bit-packing to reduce storage space and improve query performance. In comparison, DuckDB utilizes a combination of lightweight compression algorithms to achieve high compression ratios while maintaining fast query processing speeds.

6. Workload Optimization: Apache Parquet is optimized for analytical workloads that involve scanning large datasets and running complex queries, making it ideal for business intelligence and data analytics applications. DuckDB, on the other hand, is designed for transactional workloads that involve frequent data updates and real-time query processing, making it suitable for interactive data analysis and online transaction processing (OLTP) scenarios.

In Summary, Apache Parquet and DuckDB differ in terms of data storage format, query processing capabilities, file system dependency, supported data types, compression techniques, and workload optimization, making them suitable for distinct use cases in the realm of big data and data warehousing.