Connect instead of migrate
Use the infrastructure that already produces structured event data.
Analytics architecture, redesigned
Vendor-agnostic.
On your own data.
Drag & Drop Analytics visualizes data in customer-owned infrastructure. Connect to existing data, or send new events into your own warehouse. The analytics app does not become the data store.
Visualize what you already own
Data source
Current tracking stack
Server-side GTM, Segment, Snowplow, ETL, APIs
Customer-owned storage
Your warehouse or database
The source of truth stays in your infrastructure.
Drag & Drop Analytics
Query and visualization layer
DDA sends queries and renders dashboards. Event data is not stored in DDA.
Writes
Your infrastructure
DDA stores
No event data
Output
Reports and dashboards
Connect read-only to existing customer-owned data.
Send new data to your own warehouse
Data source
Websites, apps and services
New events can be collected through an optional managed tracking path.
Customer-owned storage
Your warehouse or database
New data lands in your storage, not in an analytics vendor silo.
Drag & Drop Analytics
Query and visualization layer
DDA visualizes the modeled data from your warehouse. No duplicate event store.
Writes
Your infrastructure
DDA stores
No event data
Output
Reports and dashboards
Use DDA-managed collection without giving up data ownership.
The problem
Analytics vendors became data platforms.
Vendor lock-in
Duplicated datasets
Inconsistent metrics
Expensive migrations
Limited flexibility
High switching costs
The solution
Connect to the database you already own.
Drag & Drop Analytics separates tracking, storage and analysis. The analytics layer queries customer-owned data directly, instead of asking teams to move data into another proprietary system.
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.
Product demo
A product walkthrough belongs here.
This section is reserved for the first real demo video. Until then, the placeholder keeps the page structure ready without inventing product footage.
Watch product demoPlay
Future product demo video
How it works
Tracking and analytics are intentionally decoupled.
Use existing tracking
Server-side GTM, JSON Tag, Segment, Snowplow, APIs, queues or ETL pipelines can produce structured events.
Write to your database
Events land in customer-owned storage, not inside the analytics application.
Model metrics in SQL
Analytics definitions stay close to the data and can evolve with your platform.
Explore in the interface
Teams build reports, compare dimensions and reuse dashboards without copying datasets.
Supported infrastructure
Designed for modern data stacks.
The connector roadmap is intentionally vendor-agnostic. The goal is not one vendor for everything, but the freedom to choose the right infrastructure.
ClickHouse
Snowflake
BigQuery
PostgreSQL
SQL Server
Azure SQL
DuckDB
MotherDuck
Matomo MySQL
Source of truth
Your database. Not us.
Every dashboard should be generated directly from customer-owned data. Drag & Drop Analytics exists above storage as the analysis layer.
Managed tracking
Use your stack, or let us manage collection.
Managed Tracking is planned as an optional collection layer. Even then, events are written into the customer's own database. Drag & Drop Analytics does not become the warehouse.
Principles as features
Built for ownership, not lock-in.
Vendor-agnostic
Storage and visualization stay independent.
Warehouse-native
Analyze the data you already own.
Open architecture
Designed around clear infrastructure boundaries.
No proprietary storage
Avoid another analytics-owned silo.
Reusable reporting
Build dashboards on shared definitions.
Future-proof connectors
A roadmap built around customer infrastructure.
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.
Future proof points
Trust sections are ready without invented proof.
Case studies
Reserved for reviewed production material.
Enterprise customers
Reserved for reviewed production material.
Security documentation
Reserved for reviewed production material.
Get started
Build analytics on infrastructure you control.
Start with a conversation about your existing data stack, tracking architecture and migration goals.