Data · Analytics
A real-time analytics suite teams trust
A pipeline and dashboard layer that turns raw events into metrics decision-makers believe, fast enough to act on.
In short
Data build, in short
- A single semantic layer defining core metrics
- A combined streaming and batch pipeline, reconciled
- Pre-aggregated read models for fast dashboards
- Automated freshness and correctness checks on the data
What made this hard.
Analytics earns trust slowly and loses it fast. When two dashboards disagree or a number lands hours late, people stop using the data and go back to gut feel.
The brief was a pipeline that defines metrics once, computes them consistently and surfaces them quickly enough to change a decision.
Stack
- TypeScript
- Python
- ClickHouse
- Kafka
- dbt
- Next.js
The build, step by step.
Define metrics once
Core metrics live in one semantic layer, so every chart that shows revenue means the same thing by revenue.
Stream and batch together
Fresh events stream in for immediacy while batch jobs keep history correct, reconciled against each other.
Fast where it matters
Dashboards read from pre-aggregated models, so the screens people open daily load without waiting.
Tested data, not hoped-for data
Pipelines have checks for freshness and correctness, so a broken upstream feed is caught before anyone sees a wrong number.
What you walk away with.
- A single semantic layer defining core metrics
- A combined streaming and batch pipeline, reconciled
- Pre-aggregated read models for fast dashboards
- Automated freshness and correctness checks on the data
Services involved
A real-time analytics suite teams trust questions, answered.
Still unsure if A real-time analytics suite teams trust is right for your project? A senior engineer will tell you straight on a free call.
They disagree when each defines a metric in its own query. We define core metrics once in a semantic layer, so every chart computes them the same way.
Yes. Streaming gives immediacy and batch keeps history correct, and the two are reconciled. You get a fast number now and a correct number that agrees with it.
Pipelines run freshness and correctness checks, so a broken upstream source is caught and flagged before it reaches a dashboard.
Building something similar?
Book a free 30-minute consultation. We'll pressure-test your challenge and map a path forward, whether or not we end up working together.