AWS Data & Analytics
Connect & Engineer My Data
Automated batch and real-time data pipelines designed and built on AWS.
The challenge
Where organisations get stuck
Data needs to move reliably between systems before it can create value.
- Delayed information
- Data quality problems
- Manual processes
- Broken integrations
- Unreliable analytics
How Analytics Hive helps
AWS Data Integration & Engineering
What the engagement includes
- Data pipelines
- ETL/ELT
- Real-time streaming
- Data transformation
- Data integration
- Data quality
- Workflow orchestration
AWS services we build on
- Glue
- Lambda
- Step Functions
- Kinesis
- MSK
- EventBridge
Expected outcome
From fragmented data movement to reliable, automated data pipelines.
What we deliver
Tangible outputs, not slideware
Typical artefacts from a data & analytics engagement, tailored to the scope we agree with you.
- Data strategy, maturity assessment and roadmap
- AWS lakehouse and data warehouse architecture
- Automated ingestion and transformation pipelines
- Data quality, cataloguing and lineage controls
- Executive and operational dashboards
- Predictive analytics models tied to business decisions
- Data governance and ownership model
Our approach
How the work runs
- 01
Assess
Understand your business objectives, current environment, risks and opportunities.
- 02
Architect
Design the right AWS target architecture and transformation roadmap.
- 03
Build
Implement the cloud, data, AI and security capabilities required.
- 04
Secure
Embed security, governance, resilience and compliance into the environment.
Business outcomes
What the business gains
- 01
Timely data
- 02
Fewer manual steps
- 03
More trustworthy analytics
AWS context
Built on AWS-native capability
Data & Analytics work at Analytics Hive is delivered on AWS and aligned to the AWS Well-Architected Framework.
- Amazon S3 data lake with AWS Lake Formation
- Amazon Redshift and Amazon Athena
- AWS Glue, DMS and Amazon Kinesis
- Amazon QuickSight for business intelligence
- Amazon EMR for large-scale processing
- AWS Glue Data Catalog and Data Quality
- Amazon SageMaker for predictive analytics
Built around enterprise priorities
Security-first
Protect workloads, identities and data with controls designed in from the start.
Scalable
Architect AWS environments that grow with demand instead of constraining it.
Resilient
Design for availability, continuity and tested recovery, not best-effort uptime.
Cost-conscious
Optimise infrastructure spend without compromising performance or resilience.
Next step
Engineer My Data Platform
Related services
More data & analytics services
Discover how cloud, AI, and data can revolutionize your business
Tell us about your goals and we will come back to you with a practical next step, whether that is a discovery workshop, an architecture review, or a detailed proposal.
ROI-driven solutions