Mode Analytics
What it is: Collaborative analytics platform combining SQL, Python, and visual reporting. Notebook-style interface for data exploration.
What It Does Best
Flexible workflow. Start with SQL exploration, add Python analysis, create visual reports. All in one notebook-style interface.
Version control built-in. Git-like versioning for reports. See who changed what and when. Rollback to previous versions.
Collaboration features. Comment on charts, share findings, schedule reports. Data catalog shows all queries across organization.
Key Features
SQL editor: Fast, auto-complete SQL editor with formatting
Python/R notebooks: Run Python and R for advanced analytics
Report builder: Drag-and-drop visual reporting on SQL results
Version history: Git-like tracking of all changes
Data catalog: Search and discover queries across organization
Pricing
Studio: Free (individuals, public reports)
Business: Starting at $200/editor/month
Enterprise: Custom pricing
When to Use It
✅ Data team needs exploratory analytics
✅ Want SQL + Python in one place
✅ Need report version control
✅ Balancing ad-hoc and production reporting
✅ Data discovery and knowledge sharing important
When NOT to Use It
❌ Non-technical users building dashboards
❌ Need fully-featured BI platform
❌ Want drag-and-drop interface only
❌ Small team without SQL skills
❌ Need complex data transformations
Common Use Cases
Data exploration: Ad-hoc analysis and hypothesis testing
Analyst workflows: SQL + Python for comprehensive analysis
Executive reporting: Production dashboards from SQL queries
Data discovery: Search and reuse queries across teams
Collaborative analysis: Comment and discuss findings with stakeholders
Mode vs Alternatives
vs Looker: Mode better for ad-hoc analysis; Looker better for governed metrics
vs Tableau: Mode better for SQL-first teams; Tableau better visualization
vs Jupyter: Mode better for sharing and collaboration; Jupyter more flexible
Unique Strengths
Notebook meets BI: Best blend of exploration and production reporting
Version control: Only BI tool with proper version history
Data catalog: Excellent query discovery and reuse
SQL + Python: Seamless workflow between languages
Bottom line: Perfect middle ground between notebooks and BI tools. Best for data teams doing both exploration and reporting. Strong collaboration and version control features.