About Dataventra
We engineer data — and build the intelligence on top of it.
Dataventra is a data engineering, analytics and DataOps company. We work with mid-market and enterprise teams whose data runs on complex SQL environments, multiple source systems and years of accumulated ETL.
Positioning
Data Engineering, Analytics & Intelligent Automation.
Our work sits where data platforms meet operations. We build the platform, make it observable, automate the manual work around it — and use that foundation to move towards machine learning and AI.
What we are
- Data engineers who build and run platforms
- Specialists in SQL Server, SSIS, Azure and Power BI
- Builders of lineage, monitoring and automation products
- A deliberate path from DataOps to applied AI
What we are not
- A generic IT outsourcing firm
- Headcount for hire without ownership
- Tool resellers or licence brokers
- AI claims without the data foundations beneath them
Principles
How we think about the work.
- 01
Engineering first
We are data engineers before we are consultants. Recommendations come from people who build and run these systems.
- 02
Visible over clever
Lineage, monitoring and documentation are part of the deliverable. If a system can't be understood, it isn't finished.
- 03
Honest about the stack you have
Many enterprises run on SQL Server and SSIS for good reasons. We improve what works and modernise what doesn't — without rewrites for their own sake.
- 04
Your team stays in control
We hand over knowledge, not dependency. Everything we build is documented and owned by you.
Capabilities
Four disciplines, one team.
Engineering, observability, automation and analytics are usually split across vendors. We keep them together, because each depends on the others.
- Engineer
Build platforms that hold up.
Pipelines, warehouses and models on SQL Server, SSIS and Azure — designed for correctness, performance and change.
- Observe
See how data moves and where it fails.
Lineage from source to report, and monitoring of every job, package, SLA and load — so nothing fails silently.
- Automate
Remove the manual steps.
From failure detection to Jira tickets and team notifications, operational work runs without someone watching.
- Analyse
Turn data into decisions.
Power BI and analytics built around real business questions — on data you can trace and trust.
How we work
Four phases. Something usable at the end of each.
Engagements are sequenced to reduce risk early and deliver value continuously — whether we're building a new platform or stabilising an existing one.
- 01
Assess
We review your environment — sources, pipelines, warehouse, reports and operations — and map where risk and cost sit today.
Output — Current-state map and prioritised findings
- 02
Design
We design the target architecture and delivery plan, sequenced so every phase delivers something usable.
Output — Architecture, roadmap and delivery plan
- 03
Build
We engineer, test and deploy in short increments, with lineage and monitoring built in from the start — not added later.
Output — Working pipelines, models and reports
- 04
Operate & improve
We monitor, automate and optimise, handing over documentation and knowledge so your team stays in control.
Output — Monitored, documented, automated platform
AI roadmap
Intelligence built on foundations, not hype.
Machine learning, generative AI and agents are where we're heading. We're building towards them deliberately — starting with the lineage, execution history and quality signals that make AI in data operations trustworthy.
- 01 · NowFoundation
Reliable, observable data
AI is only as good as the data and metadata beneath it. Lineage, execution history and quality checks are the foundation.
- End-to-end lineage metadata
- Execution and runtime history
- Data-quality and freshness signals
- 02 · NextIn development
Predictive data operations
Using execution history to anticipate problems instead of reacting to them.
- Runtime and volume anomaly detection
- SLA breach prediction
- Predictive analytics on warehouse data
- 03 · LaterExploring
Intelligent automation & AI agents
Generative AI and agents that understand your lineage and operations, and assist the engineers who run them.
- AI-assisted incident root-cause analysis
- Natural-language questions over lineage
- Agents for routine data-operations tasks
Roadmap items describe direction, not generally available features.
Let's look at your data estate together.
Tell us about your SQL Server, SSIS, Azure or Power BI environment — and what's getting in the way. You'll talk to an engineer, not a sales script.