What are the most common data formats used for datasets (e.g., CSV, JSON, Parquet)?

The most common data formats for datasets include CSV, JSON, and Parquet. Each serves distinct purposes based on structure, use cases, and performance. CSV is widely used for tabular data, JSON for hierarchical or nested data, and Parquet for optimized analytical workloads. Understanding their strengths and limitations helps developers choose the right format for their projects.

CSV (Comma-Separated Values) is a plain-text format for tabular data, where each row represents a record and columns are separated by commas. It’s simple to read and write, making it a default choice for spreadsheets (like Excel) and basic data exchange. For example, exporting user data from a database often results in a CSV file. However, CSV lacks built-in support for data types or schemas—all values are treated as strings unless explicitly parsed. This can lead to inconsistencies when handling dates, numbers, or missing values. Additionally, CSV isn’t efficient for large datasets or nested data structures, as it requires parsing entire files even for partial queries.

JSON (JavaScript Object Notation) is a lightweight, human-readable format for representing structured data as key-value pairs. It supports nested objects and arrays, making it ideal for APIs and configuration files. For instance, a weather API might return JSON with nested fields like {"location": {"city": "London"}, "temperature": 15}. JSON’s flexibility allows it to handle complex hierarchies, but this comes at a cost: redundant keys and braces increase file size, and parsing large JSON files can be slow. While JSON is ubiquitous in web development, alternatives like Protocol Buffers or MessagePack are often better for performance-critical applications due to their binary encoding.

Parquet is a columnar storage format optimized for analytical queries. Instead of storing data row by row (like CSV), Parquet groups values by columns, enabling efficient compression and faster queries on specific fields. For example, querying a single column in a billion-row dataset requires reading only that column’s data. Parquet also supports schema evolution, allowing fields to be added or modified over time. However, Parquet files are binary and not human-readable, requiring tools like Apache Spark or Pandas to process them. This makes Parquet less suitable for small-scale applications but highly effective in big data pipelines, where reducing I/O and storage costs is critical.