The parquet file format is an increasingly popular way to store and manage large datasets, but many popular analytics tools do not open parquet files directly. If you want to open and analyze parquet data without writing code, Row Zero is your best choice. Row Zero is a powerful online spreadsheet that opens parquet files as a spreadsheet with a simple file import and has 200+ built-in functions, pivot tables, and charts to analyze parquet data.
If you are technical and comfortable writing SQL or python, you have additional options to query parquet files and analyze and transform data stored as parquet. In this guide, we'll explore the best parquet software tools to view parquet files and analyze parquet data.
Best parquet software by category
- Spreadsheets and no code analytics tools
- Python parquet tools
- SQL parquet tools
- Other popular parquet tools
Spreadsheets and no code analytics tools
Row Zero is an enterprise-grade spreadsheet built for big data that makes it easy to open parquet files with a simple one-click import. You can open parquet files from your computer, a URL, or Amazon S3. You can also query data warehouses directly from the spreadsheet.
Row Zero works like Excel and Google Sheets but is 1000x more powerful. Easily view parquet files and analyze parquet data with pivot tables, charts, and 200+ spreadsheet formula functions. You can also open parquet.gz files directly in Row Zero.
Beyond Row Zero, there are few no code tools to open parquet files. Excel and Google Sheets cannot open parquet files directly and many BI tools like Tableau cannot open parquet files natively as a file import.
Python parquet tools
If you know python and don't need to leverage spreadsheet features for analysis, you can use various python libraries to read parquet and analyze large datasets. Here are a few popular ones:
- Pandas + PyArrow: Pandas and PyArrow are great for reading and analyzing small to medium sized parquet files (less than a few GB). Use pd.read_parquet() to read parquet files with Pandas.
import pandas as pd df = pd.read_parquet("file.parquet") - Polars: Polars offers faster parquet reading and processing and supports larger parquet files than Pandas. Polars has a small memory footprint and supports lazy pipelines for optimization and real-time dashboarding.
import polars as pl df = pl.read_parquet("file.parquet") # eager lazy_df = pl.scan_parquet("file.parquet") # lazy - Dask: Dask is used for analyzing very large parquet and CSV files (TB-sized datasets). Dask is for parallel computing and especially useful for handling large datasets that don’t fit into memory. You can do groupbys, joins, and aggregations on data that's too large for Pandas. Dask reads parquet via dask.dataframe.read_parquet().
import dask.dataframe as dd df = dd.read_parquet("large_files/") result = df.groupby("col").mean().compute() - Jupyter Notebooks - Jupyter Notebooks can be used for analyzing and visualizing parquet data via Pandas.
SQL parquet query tools
If you know SQL and don't need to leverage built-in spreadsheet features for analysis, you can use various tools to query parquet with SQL. Here are a few popular ones:
- Amazon Athena: Athena is a serverless, interactive query service that lets you analyze data directly in Amazon S3 with SQL. You can run analytical queries on large datasets stored as Parquet, ORC, CSV, JSON, etc. Athena integrates with BI tools like Tableau, PowerBI, Looker, and QuickSight and makes it possible to work with parquet files in these tools to build business intelligence dashboards on parquet data. If your parquet files are stored in S3, Athena can be a great choice for working with parquet.
- DuckDB: Duck DB is an open-source, in-process SQL OLAP database management system designed to run SQL analytics on large datasets without needing to load them into a database. You can query parquet files in-memory on your laptop and DuckDB can handle large 100M+ row parquet files. DuckDB also has easy Python and R integration.
Other popular parquet software tools
In addition to the tools and libraries above, there are a few other categories of tools that are commonly used with parquet.
- Data connector / integration tools like Fivetran and Airbyte let you convert, move, or read parquet and make it possible to import parquet data into a variety of tools.
- Cloud data warehouses like Databricks, Snowflake, and BigQuery support importing and exporting parquet files and are increasingly adding dashboarding and analytics features that let you query, analyze, and visualize parquet data.
- Data processing frameworks like Apache Spark, Hive, and Flink are used to read and write parquet at massive scale.
Conclusion
The best parquet software tool for you will depend on your use case, technical ability, and data size. While the parquet file format is increasing in popularity, there are few no or low code analytics tools that make it easy to work with parquet files. Row Zero is a powerful spreadsheet built for big data that makes it easy for both technical and non-technical folks to open large parquet files and analyze parquet with pivot tables, charts, and spreadsheet functions. It's a powerful parquet GUI for all skill levels. If you know python or SQL, you can use a variety of tools to read, write, and analyze parquet with code. However, in many situations it will be much faster to analyze parquet with Row Zero since you can leverage built-in spreadsheet features instead of writing code and can easily share analysis and collaborate in real-time. Row Zero connects directly to your database or data warehouse, so you can preview parquet files before importing to your database or query data directly from your spreadsheet. You can try Row Zero for free and see why Row Zero is the best analytics tool for parquet files.