Data EngineeringDelivered

Cloud Data Warehouse Migration on AWS

Re-architecting on-prem ETL into AWS for 80% better resource utilisation

Spearheaded a seamless data-warehouse migration to AWS for a pan-African financial-services group — transitioning 20–30 ETL scripts and stored procedures with Glue, Redshift and CloudFormation to optimise resource utilisation by 80%.

AWSAWS GluePySparkSQLPL-SQLAmazon RedshiftAWS S3CloudFormationRDSSSIS

Problem Statement

An on-premises data warehouse was quietly capping the business: limited scalability, high operational costs and restricted access for analytics teams — a combination that slowed decision-making and held back data-driven growth.

On-prem warehouse offered limited scalability and high fixed operational cost.
Analytics teams had restricted, slow access to the data they needed.
Infrastructure constraints throttled data processing and decision-making.

Headline Outcomes

+80%job re-architecting

Resource utilisation

Reducedelastic compute

Operational cost

Minimalseamless cutover

Migration disruption

The Solution

A carefully sequenced, low-disruption migration to the AWS cloud — re-architecting 20–30 ETL streams and stored procedures with Glue, Redshift, S3 and CloudFormation, then layering in data-governance and cataloguing so quality and accessibility improved alongside cost.

Migrated 20–30 ETL scripts and stored procedures with Python, PySpark and SQL.

Re-architected jobs on AWS Glue, Redshift, S3 and CloudFormation for elastic scale.

Optimised resource utilisation by 80% through job re-architecting and right-sizing.

Defined data-governance policies and a data catalogue with business stakeholders.

System Architecture

How the data flows

On-Prem Warehouse

Legacy source

ETL Re-architect

Python · PySpark · SQL

AWS Glue

Managed ETL

Redshift + S3

Cloud warehouse

Governance & Catalog

Quality + access

Result 01

Delivered an 80% gain in resource utilisation with lower running costs.

Result 02

Improved data quality and accessibility through governance and cataloguing.

Result 03

Executed the cutover with minimal disruption to critical analytics.

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