AWS Data Engineering
AWS Data Engineering Consultancy
We design and build production-grade data platforms on AWS — from data lakes and analytics pipelines to real-time streaming architectures and AI-ready data foundations. Engineering-led delivery for enterprise environments.
Why organisations need AWS data engineering expertise
AWS offers an extensive data services ecosystem — S3, Glue, Athena, Redshift, Kinesis, Lake Formation, Step Functions, and more. The challenge for most organisations is not choosing a service, but designing an architecture that uses the right services in the right combination for their specific requirements.
We frequently see organisations that have adopted AWS but are struggling with data platforms that are expensive, slow, unreliable, or ungoverned. Common patterns include over-engineered architectures using too many services, lift-and-shift migrations that replicate on-premises patterns in the cloud, and data lakes that have become data swamps with no cataloguing or quality controls.
Our AWS data engineering consulting is focused on designing and building platforms that are simple, scalable, cost-effective, and production-grade. We favour proven architectural patterns over cutting-edge complexity, and we design for operational maintainability from day one.
AWS data capabilities we deliver
Data Lake Architecture
S3-based data lakes with Lake Formation governance, Glue cataloguing, Iceberg table formats, and query layers using Athena or Redshift Spectrum. Designed for cost efficiency, governance, and scalability from terabytes to petabytes.
Analytics Pipelines
Production-grade ETL and ELT pipelines using Glue, Step Functions, Lambda, and EventBridge. Event-driven architectures that process data reliably with built-in error handling, retry logic, observability, and alerting.
Real-Time Streaming
Kinesis Data Streams, MSK (managed Kafka), and Flink for real-time data ingestion, transformation, and delivery. Sub-second data availability for operational dashboards, fraud detection, and real-time analytics workloads.
Cost & Performance Optimisation
Right-sizing compute instances, implementing S3 storage tiering, reserved capacity planning, query optimisation across Redshift and Athena, and eliminating waste through architecture redesign. We typically reduce AWS data platform costs by 30–40%.
AWS services we work with
We have production experience across the full AWS data ecosystem. We select services based on your specific requirements — not because they are new or trendy, but because they are the right tool for the job.
Example engagement
AWS Data Platform Modernisation — Logistics Industry
AWS + Glue + Redshift + dbt + Terraform
A large logistics organisation was running a legacy on-premises SQL Server data warehouse that was slow, expensive, and unable to support new analytics requirements. Reporting latency was 48 hours, data quality was poor, and the engineering team spent most of their time maintaining existing systems rather than building new capabilities.
Challenges
- — Legacy on-premises SQL Server warehouse at capacity
- — 48-hour reporting latency impacting operations
- — No automated data quality monitoring
- — High infrastructure costs with no scalability path
- — Engineering team entirely consumed by maintenance
Outcomes delivered
- ✓ Cloud-native data platform on AWS (S3 + Glue + Redshift)
- ✓ Reporting latency reduced from 48 hours to 45 minutes
- ✓ Automated data quality checks across all pipelines
- ✓ 60% reduction in infrastructure costs
- ✓ Engineering team freed to build new analytics capabilities
How we approach AWS engagements
Every AWS engagement begins with a technical assessment of your current architecture. We review your data flows, service usage, cost structure, security posture, and operational processes. This gives us a clear baseline and identifies the highest-impact improvements.
We then design a target architecture that addresses your specific challenges — whether that is cost reduction, performance improvement, new capability enablement, or migration from legacy systems. Our designs are pragmatic: we favour simplicity and proven patterns over architectural novelty.
Implementation follows an iterative approach with 2–4 week delivery cycles. We build with infrastructure as code (Terraform or CDK), automated testing, CI/CD pipelines, and operational runbooks from day one. The goal is a platform your team can confidently own and operate after our engagement ends.
Need help with your AWS data platform?
Book a free technical discovery call to discuss your AWS architecture and challenges.
Book a Discovery CallFrequently Asked Questions
Should I use Redshift or Athena for analytics?
It depends on your workload patterns. Athena is ideal for ad-hoc queries and variable workloads where you pay per query. Redshift is better for consistent, high-frequency analytical workloads where dedicated compute delivers better performance and cost predictability. Many organisations use both — Redshift for core analytics and Athena for exploratory queries on the data lake.
How do you handle data lake governance on AWS?
We implement Lake Formation for centralised access control, Glue Data Catalog for metadata management, and automated quality checks using custom validation frameworks or tools like Great Expectations. We also establish tagging strategies, partitioning conventions, and lifecycle policies to keep the lake organised and cost-efficient.
Can you migrate our on-premises data warehouse to AWS?
Yes. We handle end-to-end migrations from SQL Server, Oracle, Teradata, and other on-premises warehouses to AWS. This includes schema redesign for cloud-native patterns, pipeline rebuild, historical data migration, parallel running periods, and cutover planning. We ensure zero data loss and minimal disruption.
How do you manage AWS data platform costs?
We implement reserved capacity where appropriate, S3 intelligent tiering for storage, compute auto-scaling, resource tagging for cost allocation, and regular architecture reviews to eliminate waste. We also set up AWS Budgets with alerts and Cost Explorer dashboards for ongoing visibility.
Do you use Terraform or CloudFormation?
We primarily use Terraform for infrastructure as code because of its multi-cloud support, superior state management, and mature module ecosystem. However, we also work with CDK and CloudFormation where organisations have existing investments in those tools.
Can you help prepare our AWS platform for AI and ML?
Yes. We design data platforms with AI readiness in mind — governed feature stores, accessible training data, SageMaker integration, and data pipelines that serve ML models reliably. We ensure your data foundations support AI workloads before introducing model development.