Data you can actually use. Without the infrastructure bill.
We build the platforms, pipelines and integrations that bring structured and unstructured data together, then the analytics and dashboards that make it useful. From storage and processing through governance, we cover the full stack, not just the dashboard on top, and work with the tools your data already lives in.
You're in the right place if…
- Organisations with data spread across databases, SaaS tools, devices and external APIs
- Teams that rely on manual extracts and spreadsheets for reporting
- Product companies that need real-time or large-scale data processing
- Businesses preparing their data foundations for machine learning and AI
What gets in the way.
Siloed data
Each system holds part of the picture, and joining it up is a manual effort.
Unreliable reporting
Inconsistent definitions and data quality issues undermine trust in the numbers.
Scale and latency
Batch processes can't keep up with data volumes or the need for timely insight.
Governance gaps
Unclear lineage, access and privacy controls create compliance risk.
Data Engineering, end to end.
Data platforms
Scalable storage and management for structured, semi-structured and unstructured data.
Data pipelines and processing
Batch and streaming pipelines, including real-time processing with Apache Kafka and Spark Streaming.
Data integration
Bringing together data from databases, warehouses, SaaS platforms, IoT devices and external APIs.
Analytics and visualisation
Descriptive, diagnostic and predictive analytics with interactive dashboards and reporting.
Data governance and security
Lineage, quality management, privacy compliance such as GDPR, encryption and access controls.
ML-ready data foundations
Data preparation and features for predictive analytics, anomaly detection and recommendations.
A delivery approach you can plan around.
Map the data
Identify sources, consumers, quality issues and the decisions the data should support.
Design the platform
Choose storage, processing and governance patterns that fit volume, latency and budget.
Build pipelines
Implement ingestion, transformation and integration with monitoring and quality checks.
Deliver insight
Publish trusted datasets and dashboards, and extend them as needs grow.
Ways to work with us.
Data assessment
A review of sources, quality and architecture with a prioritised plan.
Platform or pipeline build
A scoped project to stand up a data platform, pipeline or analytics layer.
Ongoing data team
Data engineers who extend and operate the platform over time.
Technologies we work with
- PostgreSQL
- MySQL
- MariaDB
- MongoDB
- Azure Cosmos DB
- Apache Kafka
- Apache Spark
- AWS Glue
- Azure Data Factory
- Google Cloud Dataflow
- Power BI
- Tableau
- Amazon QuickSight
- Apache Superset
Data Engineering: common questions.
Do we need to replace our existing databases or BI tools?
Usually not. We design pipelines and platforms around where your data already lives, and recommend changes only where they solve a real problem.
Can you handle real-time data?
Yes. We build streaming pipelines with tools such as Apache Kafka and Spark Streaming where the use case needs timely data.
How does data engineering relate to AI?
AI depends on clean, governed, accessible data. Data engineering builds that foundation, and our AI services build on top of it.
Let's talk about your data engineering needs.
An engineering lead will review the scope and reply within one business day to set up a discovery call.