New

Opt-in Feature

Users can now download filtered data directly from individual visualizations within your White Label Embeds (WLEs). This allows for more granular data exploration without needing to export the entire dashboard.

Key Details

• Granular Control: Users can export the specific data powering a single chart or table, respecting all active filters.

• Workspace-Level Toggle: This is an Opt-in feature. To ensure consistent data governance, the ability to download can only be enabled or disabled at the Workspace level.

• WLE Impact: Once enabled for a workspace, the download option will be available across all embedded dashboards within that workspace.

Early Release - Invite Only

Data Mashup is a high-performance interface for performing SQL transformations across disparate data sources within a single report. It removes the "single connection" restriction, enabling joins between different warehouses and flat files without requiring an ETL process to centralize data first.

Key Features

• Cross-Source Joins: Perform standard SQL joins across independent datasets.

• Fast operations: Uses a cached layer to treat external datasets as local tables for high-performance search.

• Sources: Supports SQL and Flat File Datasets; Notebook outputs and CSV/Excel support are coming soon.

Learn more here

Improvement

New and Improved SQL Editor

We have overhauled the SQL Editor to make writing queries smoother, faster, and more customisable - from "mind-reading" autocomplete to a high-performance editor interface.

1. Enhanced Autocomplete

• Advanced Parsing: Full support for CTEs, quoted/case-sensitive table names, and non-aliased JOINS.
• Flexible Syntax: Recognises tables separated by commas and multiple queries separated by semicolons.

2. Better Customisation & UX

• Visuals: New High-Contrast theme, font sizes (10–24px), and Minimap toggle.
• Organization: Use -- #region / -- #endregion for code folding.
• Editing Tools: Added Mouse-wheel zoom, and dedicated Undo/Redo buttons.

3. Performance Boost

• Instant Suggestions: We now fetch table and column metadata in parallel to eliminate wait times.
• Lag-Free Typing: Optimised rendering ensures the UI stays snappy even when handling massive queries.

New

Customize your report with filters and instantly generate a shareable URL. Others will land on the same filtered view — no setup needed. Shared views never expire — bookmark or share them anytime

Learn more here

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

Improvement

Opt-in Feature

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