Data engineering
Design and build batch and streaming pipelines that move, clean and model data at scale.
- ETL / ELT pipelines
- Streaming with Kafka and Spark
INTELLYLABS / DATA & ANALYTICS
Data science & engineering.Find the signal.
Connect scattered data, build pipelines you can trust and turn information into decisions. From modern data platforms to dashboards and predictive models, we make data useful.
From raw data to decisionsPipelines, models and dashboards in one team.
SERVICES
Data capabilities, end to end.
Turn data assets into a real competitive advantage with state-of-the-art techniques, tools and practices.
Data platforms & warehousing
Modern lakehouse and warehouse platforms that serve analytics and AI.
- Snowflake, Databricks, BigQuery
- Hadoop and HBase modernisation
Business intelligence
Interactive dashboards and reports that answer the questions leaders ask every week.
- Power BI and Tableau
- KPI frameworks
Data science & ML
Predictive models for forecasting, segmentation, churn and anomaly detection.
- Python, scikit-learn, Spark ML
- Model deployment and monitoring
Data governance & quality
Ownership, lineage, quality rules and access controls that make data trustworthy.
- Data catalogues
- Privacy and compliance
Decision support
Embed analytics in operational workflows and test the impact of digital initiatives.
- Experimentation and A/B testing
- Operational analytics
IN THE REAL WORLD
From fragmented sources to one reliable view.
In our healthcare engagement, clinical, laboratory and payer information arrived in flat files, HL7, CCDs and multiple databases. Reporting relied on manual reconciliation.
A cloud data platform with automated inbound and outbound feeds replaced that effort with repeatable, governed reporting.
- Automated ingestion from many formats
- Single source of truth for reporting
- Self-service access with SSO
- Scalable cloud data platform
OUR APPROACH
Building an analytics capability that lasts.
Understand
Clarify decisions, KPIs and data sources.
Engineer
Build pipelines, models and quality checks.
Visualise
Deliver dashboards and self-service tools.
Scale
Add advanced analytics and enable your team.
IN PRACTICE
Data science in action.
Projects and perspectives connected to this capability.
Healthcare
Simplifying healthcare data
A connected clinical data and reporting platform for a US health system.
Read the case study→
Workforce
Future workforce insights
Putting human skills at the heart of competitiveness in the age of intelligent technology.
Read the case study→
Automation
How process automation changed the game
Reducing labour-intensive supply chain and finance tasks for a US consumer products company.
Read the case study→FAQ
Data questions, answered.
You don’t need to be a data expert to get started.
Why do we need data engineering services?
Data engineering replaces slow, manual data handling with automated, reliable pipelines — so reports are timely, consistent and trusted, and infrastructure costs stay predictable.
We have limited knowledge of data engineering. How will you help?
Our specialists will understand your goals and current systems, then recommend a technology stack that fits your budget, skills and IT landscape. No deep technical knowledge is needed on your side.
What is the difference between data engineering and data science?
Data engineering builds the systems that collect, store and move data at scale. Data science uses statistics and machine learning on that data to generate insights and predictions. Both work best together.
What benefits will my business see?
- Faster, data-driven decisions
- Better product quality and user experience
- Lower costs through simpler data infrastructure
- Quicker time to insight for new questions
NEXT STEP
Make your data work harder.
Tell us about your data sources and the decisions you want to improve. We will suggest a practical first project.
