Turn raw data into decisions
Your data already holds the answers about your customers, operations and growth. Graaho designs and builds the data platforms, pipelines and analytics on AWS that bring that data together, keep it trustworthy and make it ready for business intelligence, machine learning and generative AI.
Data platforms
Data lakes and warehouses on AWS, designed for governance, scale and cost.
Pipelines & integration
Batch and real-time pipelines that keep every system in sync.
Analytics & AI-ready data
Dashboards, insights and the data foundation your ML and AI need.
What we deliver
End-to-end data engineering, from ingestion to insight.
Data Platforms & Data Lakes
A single, governed source of truth
Modern data platforms on AWS with an Amazon S3 data lake at the core, structured for analytics, secured by design and built to scale with your business.
- Amazon S3 data lakes
- Data modeling
- Data warehousing
- Data cataloging
- Access control & encryption
- Storage cost optimization
Pipelines & Integration
Reliable data, on time, every time
Batch and real-time pipelines that ingest data from applications, databases and third-party systems, with orchestration, monitoring and data-quality checks.
- Batch & streaming ETL / ELT
- Workflow orchestration (Airflow)
- Database migration (AWS DMS)
- API & SaaS integration
- Data-quality checks
- Pipeline monitoring
Analytics, BI & AI-Ready Data
Insight for people, features for models
Dashboards and data visualization for decision makers, customer-behaviour analytics, and feature pipelines that feed recommendation engines and AI agents.
- BI dashboards & reporting
- Data visualization
- Customer behaviour analytics
- Feature pipelines for ML
- Real-time inference data
- Knowledge bases for generative AI
Built to be trusted
How we design every data platform.
Secure & governed
Encryption, least-privilege access and audit trails, so the right people see the right data.
Automated
Infrastructure as Code and orchestrated pipelines replace manual, error-prone data work.
Cost-aware
Storage tiering, serverless processing and right-sized compute keep data costs predictable.
AI-ready
Clean, well-modeled data that is ready for machine learning and generative AI from day one.
From scattered data to a working platform
Discover
Map your data sources, quality issues, reporting needs and compliance requirements.
Design
Define the target architecture, data model, security and cost plan on AWS.
Build
Deliver pipelines, the data lake and dashboards incrementally, with automated testing.
Operate & evolve
Monitor pipelines and costs, and extend the platform to new use cases and AI.
Real-time personalization at scale
Recommendation engine on AWS: ALGOREC
We built a cloud-native data and ML platform with an Amazon S3 data lake, a real-time inference pipeline, SageMaker model hosting and full automation with Airflow, driving 20% sales growth for the client.
Data technologies we use
AWS-native services and open-source tools, chosen to fit your stack.
Where data engineering pays off
Typical outcomes our clients look for.
The right resource model for every project
Fixed cost
Ideal for an MVP or a well-defined SOW, delivered on time and on budget by AWS-certified engineers.
Learn more 02Dedicated
Dedicated AWS resources and staff augmentation: certified engineers who work as part of your team.
Learn more 03On demand
On-demand support teams, by the hour. You specify the task; our certified engineers complete it.
Learn moreMore ways we can help
Put your data to work.
Book a free 30-minute consultation to review your data landscape and quick wins.