What are the benefits of converting CSV to Parquet?

Parquet offers significant advantages for big data workloads: better compression (reducing storage costs), faster query performance for analytical operations, schema enforcement, and better integration with data processing frameworks like Spark and Hadoop.

How does the converter handle data types?

Our converter analyzes the CSV data to infer appropriate data types for each column. You can also specify schema information to ensure precise type conversion. Parquet supports a rich set of data types including integers, floats, strings, booleans, and complex types.

Is Parquet suitable for all CSV data?

Parquet is ideal for analytical workloads and large datasets. For small files or data that needs frequent row-by-row access, CSV might still be more appropriate. Parquet shines when you need to query specific columns from large datasets.

Convert CSV to Parquet

Upload your CSV file to convert to Parquet - paste a link or drag and drop. Free for files up to 5MB, no account needed.

Need to work offline? Try Konbert Desktop for Windows, macOS or Linux.

Try it now

CSV

CSV (Comma-Separated Values) is a simple format for storing tabular data in plain text. Each row is a data record, and columns are separated by commas (or other delimiters).

Technical Details

CSV files represent data in a flat table structure. The first row typically contains column headers, and each subsequent row contains values. Fields containing commas, newlines, or quotes may be enclosed in double quotes.

Advantages

Simple format that's easy to create and parse
Universal support across spreadsheet applications
Efficient storage for tabular data
Human-readable and editable in text editors

Limitations

No support for nested data structures
No standardized way to represent data types
Special characters may require escaping
No built-in support for formulas or calculations

Parquet

Parquet is a columnar storage file format designed for efficiency with big data processing frameworks like Apache Hadoop and Spark.

Technical Details

Parquet organizes data by columns rather than rows, which enables better compression and more efficient queries for analytical workloads. It supports nested data structures and is optimized for handling complex data.