Client Objective
Client Introduction
Challenge
The primary challenges faced by the client were:
- Data Volume: The bank was dealing with a rapidly growing volume of data generated from various sources, including transaction records, customer interactions in SAP CRM, transactional databases (RDS), and logs stored in Amazon S3.
- Data Silos: Data was scattered across different systems and formats, leading to inefficiencies in data accessibility and analysis.
- Reporting and Analytics: The client needed to streamline their data infrastructure to enable swift reporting and analytics. Delays in data retrieval and analysis were hindering operational and strategic decision-making.
What We Did
To address these challenges, the service provider designed and implemented a comprehensive data storage optimization solution. The services provided included:
- Data Warehouse and Data Lake Setup: A data warehouse and data lake were designed to consolidate data from various sources, including logs, audit records, and transaction data. These sources included SAP CRM, RDS transactional databases, and S3 logs.
- Data Pipelines and ETL Jobs: Data pipelines and ETL (Extract, Transform, Load) processes were developed to automate the extraction, transformation, and loading of data from these diverse sources into AWS Redshift, a powerful data warehousing solution.
- Data Modeling in AWS Redshift: A structured data model was created within AWS Redshift, allowing for efficient data storage and retrieval.
- Data Curation in S3: Data was curated in Amazon S3 to support insights and trend analysis. AWS Glue and PySpark were utilized for data processing and analysis, hosted inside a Virtual Private Cloud (VPC).
- Automation and Monitoring: Automation was achieved through triggers and monitoring mechanisms using AWS CloudWatch and Eventbridge to ensure data processing and analysis tasks ran smoothly and efficiently.
Outcome
The implementation of data storage optimization led to several significant outcomes:
- Reporting and Analytics Efficiency: The time-to-insights (TAT) for reporting and analytics processes improved by over 90%. This meant that the bank's operational and strategic decision-making processes were greatly expedited, enabling them to respond to changing market dynamics more swiftly.
- Streamlined Operations: By consolidating data from various sources and automating data processing and analysis, the bank's day-to-day operations were significantly streamlined. This led to improved operational efficiency and customer service.
- Cost Savings: Efficient data management reduced unnecessary data storage costs and improved resource utilization.
- Enhanced Data Accessibility: Centralizing data made it more accessible to relevant teams and departments, enabling better-informed decisions at all levels of the organization.
Conclusion
