Supported configurations

You can run Terality in a variety of environments. In general, "if it runs Python, it runs Terality".

Supported notebooks and execution environments

You can run Terality from any notebook: self-hosted Jupyter notebooks, AWS Sagemaker notebooks, Google Colab notebooks, Databricks notebooks, and any other notebook will work.

You can also run Terality directly from a Python interpreter, or include it as part of your Python scripts, whether they're running standalone or in an orchestrator such as Airflow.

Supported Python versions

Terality supports Python 3.7 and newer.

Since Terality targets pandas 1.2 and newer, Python 3.6 and older versions are not supported.

Supported operating systems

Terality works on Windows, Mac and Linux.

On Mac computers, Terality supports both Intel and Apple Silicon chips.

Supported data sources

Terality supports reading and writing to a local filesystem, AWS S3, Azure Data Lake, a Databricks file system (DBFS), and more. Details here: Importing and exporting data.

Terality also supports reading from a SQL database using the read_sql, read_sql_query or read_sql_table methods. Note that if the database is not accessible from the Internet, the data is transmitted to the machine running the Terality client, stored in memory, then uploaded to the Terality servers.


Terality is compatible with pandas 1.2 and newer.

Terality only supports the CPython interpreter (if you don't know what this means, you're likely using CPython). Other interpreters may work but are not officially supported.

Terality works on most networks with Internet access. In rare situations, you may encounter network issues when running Terality from some restricted corporate networks. Please get in touch at if you encounter such an error, and we'll sort it out.

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