Apache Spark
SSIS
Apache Airflow
Grafana
PrometheusReal-time Analytics Dashboard
Monitor business KPIs with live data streaming and visualization
Data Engineering & Analytics
Modern Data Platforms for Intelligent Analytics
Transform your data into actionable insights with modern data engineering, advanced analytics, and business intelligence solutions.
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Projects delivered
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Active clients
Five ways this goes wrong
Why choosing the right partner matters
Every one of these is something we have been called in to fix after someone else built it. Which is why we plan for all five before writing code.
01/05
What usually happens
Pilots that stall
Most initiatives never leave proof of concept, because nobody planned for production. The demo works, the business case never lands, and the budget quietly moves on.
Seen on projects we inherited, not hypotheticals.
How we handle it
Discovery ends in a production plan, not a demo. If a pilot cannot reach production, we say so before it is built.
Which of these is yours?Discovery names it in the first two weeks — and the roadmap is yours either way.
Inside the practice
What Our Data Engineering & Analytics Services Cover
5 capabilities, and engagements usually combine several. Open any one to see how we deliver it.
Measured, not promised
What You Get Out of It
Four outcomes we agree at the start of an engagement and hold ourselves to at the end. If we miss one, that shows up in the review, not in the small print.
Unified Data Platform
Centralize all your data sources in one modern platform
Real-time Insights
Get instant access to business-critical analytics
Scalable Architecture
Handle petabytes of data with cloud-native solutions
Data Governance
Ensure data quality and compliance across the organization
Shipped more than once
Where It Gets Used
Patterns we have built before, and the shape of problem each one suits. If your situation is close to one of these, we have the reference architecture already.
Customer 360 Platform
Unified view of customer data across all touchpoints
Supply Chain Optimization
Optimize inventory and logistics with predictive analytics
How We Deliver It
A sequence you can plan around, with a decision point at the end of each phase rather than one big reveal at the end.
01
Data Assessment
Evaluate current data landscape and identify opportunities
02
Architecture Design
Design scalable data platform architecture
03
Data Integration
Connect and integrate all data sources
04
Analytics Implementation
Build dashboards and analytical models
05
Governance & Monitoring
Implement data governance and monitoring
Modern Data Platforms
Microsoft Fabric, Azure Synapse, Databricks, Delta Lake, Apache Spark
Data Integration & ETL
Azure Data Factory, Logic Apps, SSIS, Apache Airflow, Talend
Data Governance
Microsoft Purview, Data Catalog, Data Lineage, Data Quality, Compliance
Microsoft Fabric- Azure Synapse
Databricks
Power BI
Azure Data Factory- Microsoft Azure
Amazon AWS
Data
The Tech Stack We Work In
The tooling we actually build data engineering & analytics on.
Google Cloud
Azure OpenAI
Snowflake
Kubernetes
Docker
Terraform
PyTorch
Business Intelligence
Power BI, Tableau, Looker, QlikView, D3.js
DataOps & Observability
Azure Monitor, DataDog, Grafana, Prometheus, ELK Stack
Frequently Asked Questions
What we are asked most often before a data engineering & analytics engagement starts.
You do not have to know. Discovery exists to answer exactly that, and it ends in a written recommendation — including the recommendation not to proceed, if the numbers do not support it.
Discovery runs one to three weeks. Build phases are typically six to sixteen weeks depending on scope, delivered in increments you can actually run rather than one release at the end.
That is the normal case rather than the exception. Integration and phased migration are scoped in the first phase, because something that cannot reach your existing workflows and data is not finished.
You do. Source, infrastructure definitions, trained models and documentation land in your own repositories and cloud tenancy as we go — not in a handover at the end.
Monitoring, alerting and agreed response targets are part of delivery, not an upsell. Where we run the platform under managed services, we are the ones who respond.
Get a free data engineering & analytics assessment
Tell us what you are trying to change and we will map what it actually takes — architecture, sequence and cost — before anyone commits to a build.
Book a Free Architecture ReviewWhat you get from the audit
- A complete architecture map of what you have today
- A prioritised change list, worst-first
- A sequenced roadmap broken into phases
- A fixed-price estimate for phase one
Yours to keep whether or not you engage us.
