IndustriesFinancial services
Real-time data you can trust.
More data engineering capacity for the pipelines your business is waiting on, with every change verified, approved by your engineers and traceable to its source.
- Lineage
- Trades
- Positions
- RiskExposure
- Simulated
- 90 days of events replayed, late records included
- Policies
- PII masked; data stays in region
- Approved
- Risk data engineering, pull request #307
- Deployed
- Blue-green, rollback ready
01 / Use cases
Where teams start.
- 01
Transaction monitoring and fraud signals
Payment and account events joined and scored in real time, with the account state as it was at payment time.
- 02
Customer 360 for advisors and agents
One current view of each customer across core banking, cards and service systems, served as an API.
- 03
Risk and regulatory reporting
Reports, dashboards and feeds computed from one verified definition, so every number agrees.
Each one is a single pipeline you can scope, build and evaluate in a Proof of Value.
02 / Why it matters
A miscounted join is a misstated exposure.
Every number has to trace back to its source, every change has to be approved by someone accountable, and the pipeline has to keep being right after the people who built it move on.
03 / Your team
Give your data team the capacity to say yes.
Clear the backlog
Your engineers describe the pipeline, an agent builds it and DataSQRL verifies it. The real-time data products risk, finance and product teams ask for ship in weeks, not quarters.
One standard way to build
Every pipeline is one SQL specification with the same tests, the same review and the same deployment, so knowledge carries across teams instead of living in one person’s head.
Fits how your team works
Git, pull requests, your CI/CD and the coding agent you already use. Nothing to re-platform, and nothing new to learn beyond SQL.
Skills that compound
Your engineers move from maintaining plumbing to designing the data products the business runs on, and they own every one they ship.
04 / Evidence
What your control functions ask for, and what DataSQRL produces.
| They ask | DataSQRL produces |
|---|---|
| Data lineage: where does this number come from? | Lineage and a full data-flow graph, produced on every compile and kept for every pipeline in production |
| Change management: who changed what, and who approved it? | Every change a reviewed pull request, kept in Git history with its test results |
| Testing: does the change do what it should? | Simulation results on replayed event histories, including late and out-of-order records, stored with each change |
| Data access and residency | Data plane in your own accounts; access rules compiled into the API (JWT-scoped) |
| Operational resilience: what happens when it breaks? | Monitoring and alerting per pipeline; blue-green deployment with one-step rollback |
DataSQRL produces evidence for your controls; your teams decide how it maps to your obligations. Security and governance →
05 / Reference
Our reference is in clinical data, where the bar is as high as yours: Medidata, a Dassault Systèmes company, runs 18,000 customer data pipelines on DataSQRL.
- 90%
- TCO reduction
- 3
- Months from Proof of Value to production
- 0
- Additional hires while workload tripled
06 / First step
Start with one use case. Decide on evidence.
A scoped Proof of Value on a use case you already have, with success criteria agreed before we start.