Case Studies
Case Study 1 Financial Services Compliance
Challenge
A leading bank struggled to maintain regulatory compliance across its rapidly growing data landscape.
Solution
- Implemented Collibra workflows for automated policy validation.
- Integrated dashboards with Tableau for real-time compliance reporting.
- Developed connectors for seamless metadata ingestion from legacy systems.
Results
- Audit readiness improved by 40%.
- Regulatory penalties avoided, saving $1.5M annuall
Case Study 2 Healthcare Data Accuracy
Challenge
A healthcare provider faced challenges in maintaining consistent data quality across patient records and operational databases.
Solution
- Created automated lineage tracking workflows in Collibra.
- Provided DevOps pipelines for continuous data quality checks.
- Designed BI dashboards for executive oversight.
Results
- Patient record accuracy improved by 20%.
- Operational decision-making accelerated by 30%.
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Optimizing Metadata Ingestion with CI/CD Pipelines and Java Spring Boot
Diagram: CI/CD Pipeline for Metadata Ingestion
Key Takeaways:
- Java Spring Boot and CI/CD pipelines ensure reliable, scalable metadata ingestion.
- Integration with cutting-edge data storage solutions meets diverse business needs.
- Proactive monitoring minimizes downtime and optimizes ingestion performance.
RDI Technical innovations and practical integrations
Optimizing Metadata Ingestion with CI/CD Pipelines and Java Spring Boot
Pipeline Stages:
1. Source Control
- Java Spring Boot metadata ingestion service stored in Git repositories.
- Versioning ensures consistent code across teams and environments.
2. Continuous Integration (CI)
- Automated testing for Java Spring Boot components using tools like Jenkins or GitLab CI.
- Unit tests validate metadata schemas and ingestion logic.
- Static analysis tools ensure clean, compliant code
3. Continuous Deployment (CD)
- Containerized deployment using Docker.
- Kubernetes ensures scalability and resilience of the metadata ingestion pipeline.
- Environment configurations for different data storage backends.
4. Data Storage Integration
- Supports diverse storage systems, including:
- Cloud databases: BigQuery, Azure Data Lake, Iceberg.
- Relational databases : PostgreSQL, Oracle, SQL Server, Redshift.
5. Monitoring and Feedback
- Logging and metrics collection with Prometheus and Grafana.
- Real-time monitoring for ingestion success rates and latency.
Maximizing ROI with RDI’s Data Intelligence Solutions
Metrics and Key Outcomes:
1. Cost Efficiency :
- 25% reduction in overall governance costs through automation of workflows, policy enforcement, and streamlined operations.
- Example: Automating metadata ingestion and validation reduced manual intervention and operational overhead.
2. Productivity Gains:
- 60% of routine tasks automated, enabling teams to focus on high-value activities
- Examples: Automated compliance reporting, data catalog updates, and governance approvals.
Metrics and Key Outcomes:
3. Improved Decision-Making:
- Real-time analytics increased decision-making speed by 40%.
- Example: Integration of actionable insights dashboards with platforms like Tableau and Snowflake