Unveiling the Battle: Apache Parquet vs CSV — Exploring the Pros and Cons of Data Formats
Storage Efficiency: Parquet is a columnar storage file format, meaning it stores data column by column instead of row by row. This columnar storage layout makes Parquet highly efficient for analytical queries and data processing tasks. It offers compression techniques and encoding schemes that can significantly reduce storage space compared to CSV files. On the other hand, CSV files store data in a row-based format, which can be less efficient in terms of storage space.
Performance: Parquet’s columnar storage layout also contributes to improved query performance. When executing analytical queries that only require specific columns, Parquet can skip reading irrelevant data, resulting in faster query execution times. In contrast, CSV files need to read entire rows even if only a subset of columns is needed, which can impact performance for certain use cases.
Data Types and Schema Evolution: Parquet supports complex data types and nested structures, making it suitable for handling structured and semi-structured data. It also provides support for schema evolution, allowing new columns to be added to existing Parquet files without requiring rewriting the entire dataset. CSV, on the other hand, represents data in a flat, tabular format and does not provide built-in support for complex data types or schema evolution.
Ease of Use and Interoperability: CSV files are widely supported and can be easily opened, viewed, and edited using standard text editors or spreadsheet software. They have a simple, human-readable format and are commonly used for data exchange between different systems. Parquet files, although not directly readable by humans, can be processed by various data processing frameworks and tools that support the Parquet format, such as Apache Spark, Apache Hive, and Apache Arrow.
Serialization and Data Compression: CSV files store data as plain text, which means they have low serialization and deserialization overhead. However, CSV files do not provide built-in compression, resulting in larger file sizes. Parquet files, on the other hand, use column-level compression techniques such as Snappy, Gzip, or LZO, which can significantly reduce file sizes and improve I/O performance. However, the compression and serialization overhead may be higher for Parquet compared to CSV.
Schema Flexibility: CSV files do not enforce a strict schema, allowing for flexibility in terms of the number of columns and their types. This can be beneficial when working with heterogeneous or evolving datasets. Parquet, on the other hand, benefits from a defined schema, ensuring data consistency and enabling more efficient compression and query optimization.
Parquet has helped its users reduce storage requirements by at least one-fourth on large datasets.
Parquet query time is less because of its columnar format, needs to scan less data so query cost on any cloud is less.
In summary, Parquet is generally preferred when dealing with large datasets, analytical workloads, and complex data types, as it offers improved storage efficiency and query performance. CSV files are commonly used for simpler tabular data, data exchange, and scenarios where human readability and ease of use are important. The choice between Parquet and CSV depends on the specific requirements, use cases, and the tools or frameworks being used for data processing and analysis.
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Dinesh Chopra