From your live database to the chart on the screen.
We build the data warehouse, the pipelines that fill it, and the in-product dashboards on top. One team owns the whole path. Your reporting stops fighting your app for resources.
Your product collects a lot of data. Your customers keep asking one question.
Every click, every event, every record. But your customers keep asking: is any of it working?
You do not have a clean answer. The numbers live in your app database. Pulling reports from there slows the app down for real users. So you bolt on a few dashboards. Then a few more. Soon you have a pile of one-off reports and no team that owns them.
Sound familiar? Here is the deeper problem. Reporting queries and live traffic want the same database. They should not share one. The moment a sales leader runs a heavy report, your app gets slower for everyone else. And every customer wants the data sliced their own way.
You are not short on data. You are short on a system that turns it into trustworthy answers, fast, without taking your product down. That is analytics engineering. It is what we do.
You are not short on data. You are short on a system that turns it into trustworthy answers.
We own the full path: raw data in, trustworthy charts out.
Pipelines that move your data, cleanly
We build ETL and ELT pipelines that copy data from your live databases into a separate warehouse. We use log-based change data capture so the pipeline reads your database change log, not your tables. Your live app barely feels it. We pull from more than one source into one warehouse.
A warehouse built for reporting, not for your app
Your live database runs the product. The warehouse answers questions. We keep them apart on purpose. Heavy reporting never touches the database your users depend on.
Dimensional models in dbt
We reshape raw rows into models built for reporting: per-customer tables, rollups, and aggregates. New source tables get added by script, not by a week of hand-typing.
High-velocity ingestion
Some data does not arrive in neat rows. We ingest fast-moving and unstructured data, the kind that breaks simple loaders. Our largest team does this every day in production.
In-product analytics, dashboards and the backend behind them
This is the part most teams underestimate. We build the dashboards your customers see inside your product, and the backend framework that powers them. The dashboards are configuration, not hardcoded reports. A new dashboard ships as a setting change, not a front-end release. That is how you serve hundreds of customers without hundreds of versions of the code.
Data quality and source-to-warehouse validation
Bad numbers kill trust faster than slow ones. We run automated data-quality checks on business rules. We also prove the warehouse matches the source, table by table, on row detail and on totals. If a number drifts, we catch it before your customer does.
Pipeline performance tuning
Dashboards are only as good as the data is fresh. We tune warehouse pipelines so they run in minutes, not hours. We rebuild only what changed in each window, instead of churning every table every cycle.
OLTP to warehouse to dashboard
Change data capture moves data off the live databases into a separate warehouse, where dbt models and validation gates feed the dashboards your customers see.
We run a system like this in production right now.
We are not describing theory. We run a system like this in production right now.
Since 2017, our largest team has owned the analytics platform behind Allego, a revenue enablement leader used by a quarter of the Dow Jones Industrial Average. We built it from the live database to the chart on the screen. A multi-tenant data warehouse. A configurable dashboard framework that serves hundreds of enterprise customers from one codebase. The pipeline that keeps it fresh. We cut that pipeline from running in tens of minutes to running in minutes. Read the full story in our Allego embedded analytics case study.
The same team built the metrics Allego uses to read its own product. Which features get used. Which accounts are growing, and which are going quiet.
This is not new ground for us. Years before that, we built Inside License Analytics with Metrix Consulting, a software asset management product. Their SQL Server reporting had grown too slow to use. We moved the heavy analysis off it, pulling simulation data through Spark into Elasticsearch. The result let users visualize hundreds of cloud entities at once and see the cost impact of a change right away.
Different decade, same problem. Your live system cannot also be your analytics engine. We have solved that more than once.
We pick tools that survive production, not tools that look good on a slide.
Your stack may differ. We have worked across MySQL, SQL Server, and Elasticsearch sources too. The pattern holds.
Start small. Prove it. Scale into a team that owns it.
We grow into the work. A fixed-fee assessment maps your data and the path to the dashboards your customers want. A fixed-scope pilot puts one pipeline and one set of dashboards into production, validated against the source. Then a senior team owns the analytics platform end to end, the way we have run it inside Allego since 2017.
- 01 Assess
Architecture & Readiness Assessment
A fixed-scope engagement. You get an architecture blueprint, risk analysis, roadmap, and ROI estimate.
- 02 Prove
Pilot / Proof-of-Value Build
One real use case, shipped to production, with monitoring in place.
- 03 Scale
Embedded Engineering Partnership
A senior team owns modules end to end, for years. The Allego and benelog model.