AWS Solution
Modern Data Platform (Lakehouse) Foundation on AWS
Unify your data lake and data warehouse into a single governed, analytics-ready environment
We design and implement an AWS-native lakehouse using S3, Glue, Lake Formation, Redshift, and Athena — so your teams get clean, governed, analytics-ready data for BI today and AI tomorrow, without rebuilding your infrastructure.
DELIVERED AT SCALE
30M+
Events processed daily on AWS
4TB+
Data managed per month on AWS
10+ yrs
Data engineering expertise across AWS, GCP & Snowflake
AWS-certified from architecture to delivery
The Problem
Your data stack is blocking the business, not enabling it
Most organisations operate disconnected data stacks — a warehouse for reporting, a lake for raw storage, and a tangle of pipelines holding everything together. The result is slow decisions, rising costs, and a data platform that can't support AI or ML without major re-engineering.
Inconsistent Data Across Systems
The same metric is calculated differently in the warehouse vs. the lake. Every team maintains their own copy of the data with no single source of truth and no shared data contracts.
No Governance or Access Control
No central control over who can access what data and when. Sensitive data is exposed across teams, making compliance, audits, and regulatory reporting a manual, high-risk process.
Analytics Ceiling — Can't Scale to AI
BI works, but the data model can't support AI or ML workloads without significant re-engineering. Every new use case requires rebuilding the foundation instead of extending it.
Why Us
Not just a vendor. A data engineering partner.
Most consultancies hand you a report. We stay until it's in production and afterwards maintenance support
Production-first mindset
We architect for day-30, not day-1. Every lakehouse we build handles real-world scale — terabytes of data, concurrent analytics workloads, and production-grade SLAs from week one.
Full-stack AWS expertise
From S3 and Glue to Lake Formation, Redshift, Athena, and Kinesis — we cover the entire AWS data stack. No handoffs between specialists, no knowledge gaps mid-project.
Embedded, not outsourced
We work inside your team's tools — Jira, GitHub, Slack. We hire, train, and mentor engineers. We leave your team stronger than we found it.
Measured by outcomes
60–70% ETL performance gains at 10XCRM. 6–8 hours → 30 minutes at Tracium. We define success metrics upfront and are accountable to them.
How We Work
From raw data to analytics-ready in three phases
A structured engagement model that builds your lakehouse correctly — governed, scalable, and AI-ready from day one.
Well-Architected
Built on the AWS Well-Architected Framework
Every lakehouse we deliver is assessed across all five pillars — so you get architecture that's secure, governed, and built to scale from day one.
Security
Column-level access controls and row-level security via Lake Formation — zero-trust from day one.
Operational Excellence
Automated pipeline monitoring, alerting, and self-healing workflows via CloudWatch and Step Functions.
Reliability
Multi-AZ data lake architecture with automated backups, versioning, and point-in-time recovery on S3.
Performance Efficiency
Athena and Redshift Spectrum for serverless, sub-second analytics — no cluster management required.
Cost Optimisation
S3 intelligent tiering, lifecycle policies, and FinOps dashboards built in from the start — no bill surprises.
Talk to an Expert
We take on a limited number of new engagements each quarter. Book a free session with one of our AWS-certified solution architects — we'll review your data stack, identify the gaps, and map out your path to a governed, analytics-ready lakehouse.