You can run Terality in a variety of environments. In general, "if it runs Python, it runs Terality".
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.
Terality supports Python 3.7 and newer.
Since Terality targets pandas 1.2 and newer, Python 3.6 and older versions are not supported.
Terality works on Windows, Mac and Linux.
On Mac computers, Terality supports both Intel and Apple Silicon chips.
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_tablemethods. 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 [email protected] if you encounter such an error, and we'll sort it out.