Case Studies | SaaS B2B

Migrating a R&D workflow from Google Cloud to AWS

R&D Workflow Migration

About

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

Challenge

The challenge had three focus areas, each an aspect of moving a live research platform onto AWS without disruption.

Analytics constrained by offline processing

Complex queries and heavier data processing were being run offline or through BigQuery, limiting how quickly the client could analyse its data. The migration had to replace that with managed AWS analytics on Amazon Redshift or Amazon EMR, improving processing speed while reducing operational cost.

An always-on model running up cost

The Hugging Face model deployed on GCP ran continuously, incurring cost even when idle. Moving it to Amazon SageMaker with auto-scaling would limit runtime to strictly necessary periods, so the platform pays for the model only when it is actually used.

Moving data without losing it

A research platform cannot afford data loss or corruption during a migration. Every designated dataset had to move from BigQuery and offline databases into Amazon S3 with integrity and security intact, and with a reliable pipeline to keep it current.

Solution

Rather than lift everything at once, the migration was split so the data foundation is proven first and the machine learning workload follows on a solid base.

50%

Of the migration cost contributed by AWS funding

$1,920/mo

Projected optimised AWS running cost (projected)

By the numbers:

  • 50% - Of the migration cost contributed by AWS funding
  • $1,920/mo - Projected optimised AWS running cost (projected)
Changes

The engagement gives the client a defined, AWS-funded path off Google Cloud and onto a modern data platform. Phase 1 moves the research data and analytics workflow onto S3 with Redshift or EMR, and Phase 2 completes the picture by putting the machine learning model on SageMaker with cost-controlling auto-scaling.

  • Analytics modernised — Offline and BigQuery processing is replaced by managed Amazon Redshift or Amazon EMR analytics against data in Amazon S3, improving processing capability.
  • Cost tied to usage — Moving the model to Amazon SageMaker with auto-scaling limits runtime to when the model is actually needed, removing the cost of an always-on deployment.
  • Data integrity protected — A reliable transfer pipeline moves every designated dataset off GCP into S3 without loss or corruption, with ingestion keeping it current.
  • Funded and phased — With AWS contributing half of the project cost and a clear two-phase plan, the client modernises on a controlled, low-risk footing.
  • Handover — The migration is delivered against a signed-off scope with a technical competence check and operational validation of the AWS setup under real conditions.

With the data foundation migrated, the client is positioned to complete the SageMaker model deployment and build further data-driven research initiatives on a platform designed for scale. This migration is the base that the next phase of work stands on.

AWS Stack

Amazon S3

For central, secure storage of the migrated research datasets.

Amazon Redshift

For managed analytics on large datasets and complex queries.

Amazon EMR

For scalable data processing as an alternative to Redshift, replacing offline workflows.

Amazon SageMaker

For deploying the Hugging Face model with cost-efficient auto-scaling.

AWS Lambda

For automating tasks and tying the data, analytics and model steps together.

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