Data Intelligence Platform Architecture
A reference architecture for building modern analytics platforms. From data ingestion to dashboards.
The Problem
Most operational systems are excellent at recording data but terrible at analyzing it. Your CRM tracks every customer interaction, your ERP logs every transaction, but getting answers to simple questions like "what's our customer retention by segment?" requires exporting to spreadsheets and hours of manual work.
A data intelligence platform solves this by extracting data from operational systems, storing it in a format optimized for analytics, and presenting insights through dashboards that anyone can use. This article provides a reference architecture you can adapt to your needs.
The Data Journey
Reference Architecture
Click any box to learn moreKey Architecture Decisions
Operational databases (OLTP) are optimized for fast reads/writes of individual records. Analytical queries that scan millions of rows would slow them down and impact production systems. A separate warehouse (OLAP) is optimized for these analytical workloads.
Without it, every dashboard might calculate "revenue" differently. The semantic layer defines metrics once — everyone uses the same formula. It also pre-computes common queries, so dashboards load in milliseconds instead of seconds.
ETL (Extract-Transform-Load) transforms data before loading. ELT(Extract-Load-Transform) loads raw data first, then transforms in the warehouse. Modern stacks favor ELT — it's more flexible, and warehouses are powerful enough to handle transformations.
For connectors and orchestration, buy (Fivetran, Airflow). For the semantic layer and dashboards, it depends on your needs — embedded analytics tools work for standard use cases, custom development for highly specific requirements.
Technology Options by Layer
The Bottom Line
A data intelligence platform isn't one tool — it's a stack of specialized components working together. The key is separation of concerns: operational systems handle transactions, warehouses handle analytics, semantic layers handle business logic, and frontends handle user experience. Get this architecture right, and you can answer any data question in seconds instead of days.