Mode release notes mode.com

New

Accelerate your analysis by importing any existing Jupyter Notebook. With the new Import Notebook option, simply upload your .ipynb file and dive straight into your work—no setup required.

Learn more here

Early Release - Invite Only

Workspace Admins can now easily set an automated Data Retention Policy for Report result data in Mode. This new setting simplifies controls that were previously only available via API, automatically purging data older than your set period (default is 1 year). This includes result data, PDFs, and Datasets.

This is an invite-only feature; contact Support or your CSM to enable.

Learn more here

Improvement

You can now effortlessly create reusable datasets directly from any successful SQL query within a report. This feature captures a snapshot of your query, allowing you to instantly save and share the results as a new dataset, eliminating the need to recreate queries from scratch.

Learn more here

New

Admin Managed Schedules allow organization admins to set maximum schedule frequencies for report and dataset refreshes, ensuring that scheduling aligns with system performance, cost-efficiency, and data governance objectives. This feature gives admins greater oversight and control over how often users can schedule updates.

Learn more here

New

The Python CleanSlate environment is a new execution option in Mode, available alongside Python 3 and Python Edge. It starts without any pre-installed third-party packages, giving users full control over their environment setup. This is ideal for analysts who want a minimal, dependency-free starting point for custom workflows.

Learn more here

Improvement

Both libraries unlock powerful data engineering and analytics workflows directly within your notebooks.

Snowpark empowers you to build scalable data pipelines and run advanced data transformations using Snowflake’s engine. PySpark provides distributed data processing and machine learning capabilities via Apache Spark, enabling efficient analysis of large datasets.

learn about our full set of supported libraries here

Improvement

Commits made through Mode’s GitHub integration will now appear as Verified in GitHub. These commits are shown as coming from the GitHub App bot (instead of the individual user), with the original Mode user's name included in the commit message for clarity (e.g., Committed by Jane Smith).

There’s no change to your existing workflow—just improved transparency and commit integrity.

Learn more about our GitHub integration here.

Improvement

We’ve updated the API for purging all report results to support a minimum date threshold of 1 day, reduced from the previous 15-day limit. This change allows users to implement stricter retention policies, if they choose.

Learn more about how the Purge API here.

Improvement

We're upgrading our Python environment from version 3.9 to 3.11. This update brings:

• Performance Boost: Faster execution thanks to core optimizations.
• Better Error Messages: Clearer tracebacks to speed up debugging.
• New Language Features: Includes match statements (structural pattern matching), precise error locations, and improved typing.
• Deprecations: Some older libraries or syntax may no longer be supported—please review your code for compatibility.

For most users, no changes are required. If you maintain custom Python code, we recommend testing it in the Python 3.11 environment (Python Edge).

Learn more about support library version here

New

We’ve introduced Audit Logs to help you track user actions, monitor system changes, and enhance security. This feature offers comprehensive logs to enhance compliance, ensure accountability, and provide clear visibility into system activity.

Discover the full list of events currently tracked in our audit logs here