Local connector
Matomo
A connector in your infrastructure runs validated read-only queries. Your Matomo MySQL credentials stay there.
Matomo and Snowflake connectors
Vendor-agnostic.
On your own data.
Connect Matomo or Snowflake and turn the analytics data you already own into reports and dashboards. Drag & Drop Analytics queries the source and visualizes the results—it does not create another analytics data store.
Supported data sources
Connect without moving your data
Snowflake login
Snowflake
Connect through your Snowflake authentication. No static database password needs to be stored in Drag & Drop Analytics.
Drag & Drop Analytics
Query and visualization layer
Builds source-specific queries and renders reports and dashboards. Your analytics data remains in Matomo or Snowflake.
Data sources
Matomo + Snowflake
Analytics data
Stays at the source
DDA delivers
Reports and dashboards
The problem
Analytics vendors became data platforms.
Vendor lock-in
Duplicated datasets
Inconsistent metrics
Expensive migrations
Limited flexibility
High switching costs
The solution
Analyze the data you already own.
Drag & Drop Analytics separates storage from analysis. It queries your Matomo or Snowflake data at the source, instead of asking you to copy it into another proprietary analytics platform.
Connect instead of migrate
Use the Matomo or Snowflake data source you already operate.
Keep one source of truth
Your database remains the canonical place for analytics data.
Analyze above storage
Dashboards and reports sit above the data layer, not inside it.
How it works
From your data source to a reusable report.
Choose a connector
Connect an existing Matomo installation or Snowflake data source.
Keep access controlled
Matomo database credentials stay in your local connector; Snowflake uses its own authentication.
Query at the source
Drag & Drop Analytics sends source-specific queries instead of importing your analytics dataset.
Explore in the interface
Build reports, compare dimensions and reuse dashboards without creating another data silo.
Supported connectors
Two data sources. Clear trust boundaries.
Start with Matomo or Snowflake. Each connector is designed to access analytics data without turning Drag & Drop Analytics into its storage location.
Self-hosted connector
Matomo
The lightweight connector runs alongside Matomo in your infrastructure. It keeps the read-only MySQL credentials local and executes only validated read queries.
Database credentials stay with you
Native authentication
Snowflake
Users connect through Snowflake authentication and query configured data views. A static Snowflake database password does not need to be stored in Drag & Drop Analytics.
Access remains governed by Snowflake
Your source of truth
Your analytics data stays where it is.
Reports are generated from data in Matomo or Snowflake. Drag & Drop Analytics accesses it for analysis and does not maintain a separate copy of your analytics dataset.
Controlled access
Connect without sharing a database password.
Matomo credentials remain inside the local connector. Snowflake uses its own authentication flow, so DDA does not need a static Snowflake database password.
Principles as features
Built for ownership, not lock-in.
Vendor-agnostic
Storage and visualization stay independent.
Warehouse-native
Analyze the data you already own.
Clear trust boundaries
Database access stays with the source-specific connector or login.
No proprietary storage
Avoid another analytics-owned silo.
Reusable reporting
Build dashboards on shared definitions.
Query at the source
Use Matomo or Snowflake without copying the analytics dataset.
Origin story
Born from a real analytics migration.
The product started as engineering work during an enterprise analytics migration: comparing Adobe Analytics and Amplitude side by side on the same underlying raw event data.
Migration pressure
Multiple analytics systems needed to be compared without trusting separate storage layers.
Shared raw data
The same events became the neutral foundation for comparison.
Interface separation
The insight was structural: analytics should be independent from tracking and storage.
Commercial product
Drag & Drop Analytics evolved from that engineering pattern.
Get started
Visualize your Matomo or Snowflake data.
Start with a conversation about your existing data source, access model and reporting goals.