In-Database Analytics
Window functions, ROLLUP, CUBE, and statistical functions run complex calculations where the data lives, avoiding extra copies, extra pipelines, and the delays they bring.
Database Solutions
Database Analytics Services
Get More Analytics From the Databases You Already Own
Before adding another platform, it is worth asking what your current databases can already do. We design analytical models, tune queries, and connect BI tools directly to PostgreSQL, SQL Server, Oracle, and MySQL HeatWave where that approach fits.
What We Deliver
Where We Make the Difference
Modern relational engines include window functions, columnstore indexes, materialized views, and analytical extensions that many teams never use. For a lot of reporting needs, these features answer questions quickly without copying data elsewhere. We help you decide which analytics belong in the database and which need a separate platform.
Faster Analytical Queries
Execution plan analysis, index design, query rewrites, and materialized views turn slow report queries into fast ones without starving the operational workload of resources.
Analytics-Ready Models
We design dimensional models, summary tables, and reporting schemas inside your existing database so BI tools can query clean, well-named structures directly.
Governed BI Connections
Power BI, Tableau, and Qlik connect to certified datasets and a shared semantic layer, so self-service reports agree with each other and with finance.
Our Approach
How We Deliver Database Analytics Services
Step 1: Sort the Workloads
We review your reporting needs and database capabilities, then separate what the current database can serve well from what truly needs a warehouse.
Step 2: Design for Both Workloads
We design analytical schemas, indexes, and materialized views that serve report queries while protecting the transactional work the database exists to do.
Step 3: Build and Measure
We implement the analytical layer, tune the heaviest queries, and measure their impact on operational performance before anything reaches your users.
Step 4: Connect BI and Train
We connect the BI tools, document the analytical model, and train analysts and report builders to work with it confidently on their own.
Will running analytics slow down our transactional database?
It can if nothing is designed for it. We use read replicas, columnstore indexes, materialized views, and scheduled refreshes to keep heavy queries away from everyday transactions, and we measure the impact before go-live.
When do we actually need a separate data warehouse?
When you combine many source systems, need long history at large scale, or when analytical load would overwhelm the operational database. We tell you plainly if you have reached that point.
Which BI tools do you work with?
We connect Power BI, Tableau, and Qlik to relational databases. If you have not settled on a tool, we recommend one based on the licenses and skills you already have.
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