Background
A gaming data-driven organisation needed a reliable and scalable ETL solution to ingest, transform, and load large volumes of customer and transaction data into Amazon Redshift. By implementing AWS Glue jobs integrated with Redshift, the company automated data pipelines, reduced operational overhead, and enabled faster business insights at scale.
The Challenge
The data company faced several challenges with its existing ETL processes:
- Manual data transformation scripts led to inconsistent data quality.
- High operational overhead in maintaining on-premises ETL jobs.
- Difficulty scaling pipelines as data volumes grew.
- Limited visibility into job performance and error handling.
- Slow data availability in Redshift impacted downstream reporting and analytics.
Our Solution
AWS Glue was selected to modernise the ETL framework.
- AWS Glue Jobs to process raw data from Amazon S3 and transactional sources, applying business rules and cleaning before loading.
- Glue Crawlers to automate schema discovery and catalogue metadata in the AWS Glue Data Catalog.
- Integration with Amazon Redshift using Glue’s native connectors to efficiently load transformed datasets.
- Job Monitoring & Orchestration with AWS Glue Studio and Amazon CloudWatch for visibility and alerts.
- Scalability & Serverless Architecture ensured data pipelines scaled automatically with data volume without infrastructure management.
The Result
Automation: Reduced manual ETL maintenance by 70%, freeing up engineering resources.
- Faster Insights: Data availability in Redshift improved from hours to near real-time (sub-15 minutes).
- Data Quality: Implemented data validation rules in Glue, leading to a 95% reduction in downstream data errors.
- Cost Optimisation: ETL costs decreased by 40% through Glue’s serverless pay-per-use model compared to legacy on-premises ETL.
- Scalability: Seamlessly handled peak data loads during month-end processing with no downtime.
