Case Studies | SaaS B2B

A structured AWS governance and optimisation programme

AWS Governance and Optimisation Programme

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The programme organised the work into four focus areas, each tracked as a set of concrete, prioritised actions.

Secure data science at scale

Data scientists needed to work with much larger datasets, including queries over multi-terabyte data in Amazon S3, without weakening the security posture around that access.

Security and monitoring

The team needed to consolidate security alerting across CloudTrail and CloudWatch, review third-party monitoring integrations, and tighten IAM controls such as credential rotation and file-upload malware scanning.

Networking and account structure

Public subnets needed redesigning towards a private and NAT gateway model, and the business needed to explore segregated AWS accounts and organisation-level controls for both permissions and cost.

Cost and automation

Compute savings plans, standardised self-service provisioning through CloudFormation and Service Catalog, and Config-based compliance alerting were all needed to keep a growing estate efficient and controlled.

Solution

6 TB

Dataset scale queried in Amazon S3 via Amazon Athena

£11k/mo

AWS spend brought under structured cost governance

4

Governance focus areas tracked from open to close

By the numbers:

  • 6 TB - Dataset scale queried in Amazon S3 via Amazon Athena
  • £11k/mo - AWS spend brought under structured cost governance
  • 4 - Governance focus areas tracked from open to close
Changes

The engagement gave the client a governed, continuously reviewed AWS platform, with each area of risk scoped into trackable actions and the highest-impact architectural items funnelled into a Well-Architected review. Several items, including secure Athena-based data access, were delivered and closed during the programme.

  • Secure data access delivered — Athena-based query access for data science was put into production, with Lake Formation options assessed for the next stage.
  • Stronger security baseline — IAM controls, credential handling and centralised security alerting were reviewed and tightened, with malware-scanning options identified for S3 uploads.
  • Clearer account and network design — Private and NAT-based networking and AWS Organizations controls were scoped to replace ad hoc public subnets and give top-down permission and cost control.
  • Cost discipline — Compute savings plans and self-service provisioning through CloudFormation and Service Catalog were assessed to keep spend efficient as the estate grew.
  • Handover — Every item was tracked with an owner, status and next action, leaving the client with a living governance record rather than an one-off report.

With a governance rhythm and a Well-Architected review in place, the client is positioned to take on the deeper architectural changes with confidence and to keep their platform optimised as their data workloads continue to grow.

AWS Stack

Amazon S3

For large-scale data storage underpinning the analytics workloads.

Amazon Athena

For secure, serverless querying of multi-terabyte datasets.

AWS Identity

And Access Management for least-privilege access and credential controls.

AWS CloudFormation

And AWS Service Catalog for standardised, self-service provisioning.

AWS CloudTrail

And Amazon CloudWatch for centralised monitoring and security alerting.

AWS Config

For compliance rules and drift detection, and AWS Organizations for account-level governance.

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