Improved data speed
Lower operational cost
Automated deployments
Simplified management
The challenge
Need for efficient data system
A leading adhesives manufacturer aimed to enhance data capabilities via ML and platform modernization. Their current system faced data performance bottlenecks, hindering insights. It also lacked process robustness, impacting departments. The client sought to build an in-house AWS solution to address these critical limitations and unlock data potential.
Key challenges
Need for faster data performance
Need for automation-enhanced data processes
Need for AWS-enabled analytics
The solution
End-to-End AWS Data lifecycle implementation
Data ingestion and DWH
Ingested S3 from prior platform
Used Redshift for scalable data store
Centralized data for analysis
Machine learning on AWS
Applied EMR for building predictive ML
Used Python for ML model execution
Used AWS compute for scalable ML runs
ETL pipeline with AWS Glue
Glue orchestrated ETL workflow
S3 as staging within ETL
EventBridge for workflow scheduling
Infra management via Terraform
Terraform for infra provisioning
Automated infra configuration
Consistent infra deployments
Implementation approach
1
AWS Glue for ETL workflow
Orchestrated data movement
Ensured reliable data flow
Managed data transformation
2
Amazon EMR for scalable ML
Scalable compute for ML models
Python integration for ML logic
Facilitated model deployment
3
Redshift for performant DWH
Robust data warehouse service
Optimized for BI and reporting
Efficient large dataset querying
4
Terraform and EventBridge management
Automated infra deployments
Scheduled pipeline executions
Streamlined operational tasks
The impact
Enhanced efficiency and lower operational costs
Reduced operational cost
Significant cost savings
Optimized resource use
Lower infrastructure spendurity posture
Enhanced data performance
Faster time to insights
Improved processing speed
Quicker data accessibility
Automated deployments
Consistent operations
Real-time data potential
Streamlined deployments
Simplified management
Centralized AWS billing
Easier system oversight
Simplified cost tracking
Looking ahead
Advanced analytics
Deeper ML exploration
Further automation
Automate more tasks
Scalability focus
Optimize for growth
