Challenge
Our client's enterprise operated multiple disparate systems, including Oracle, PostgreSQL, MySQL, and MongoDB database instances, alongside telemetry, CRM, ERP, and operational feeds. Data was siloed across regions and formats, making analytics inconsistent, delayed, and unreliable. The business needed a unified platform capable of consolidating structured, semi-structured, and streaming data for real-time insights while maintaining high reliability, governance, and security standards.
The complexity of managing these heterogeneous systems created significant challenges:
- Data Silos: Information was trapped in isolated systems, preventing cross-functional analytics
- Inconsistent Formats: Different data structures across systems made integration difficult
- Delayed Insights: Batch processing delays meant decision-makers were working with outdated information
- Governance Gaps: Lack of unified metadata and lineage tracking compromised data quality and compliance
- Scalability Concerns: Existing infrastructure couldn't handle growing data volumes efficiently
Solution
DataWired Solutions architected a centralized data lakehouse on AWS S3, integrating all sources into a unified analytical environment. We implemented automated ELT pipelines using Airflow and Spark to handle batch and streaming ingestion, while Terraform provisioned and managed cloud infrastructure reliably across multiple regions.
Architecture Overview
Our solution leveraged a modern data lakehouse architecture that combines the flexibility of data lakes with the performance of data warehouses:
Data Ingestion Layer
- Automated ELT pipelines using Apache Airflow for orchestration
- Apache Spark for processing both batch and streaming data
- Support for structured (Oracle, PostgreSQL, MySQL), semi-structured (MongoDB), and streaming data sources
Storage Layer
- AWS S3 as the centralized data lakehouse foundation
- Partitioned storage optimized for analytical queries
- Multi-region deployment for disaster recovery and compliance
Infrastructure as Code
- Terraform for provisioning and managing cloud infrastructure
- Automated deployment across multiple AWS regions
- Version-controlled infrastructure ensuring reliability and reproducibility
Data Quality & Governance
- Comprehensive metadata management system
- Schema evolution tracking and enforcement
- Entity-relationship modeling for master data consistency
- Data lineage tracking for compliance and auditing
Observability & Monitoring
- Prometheus for metrics collection
- Grafana dashboards for visualization
- Proactive issue detection and SLA adherence monitoring
- Performance tracking across all data pipelines
Key Technical Decisions
- Data Lakehouse Approach: Chose AWS S3 over traditional data warehouses to handle diverse data types and scale cost-effectively
- ELT over ETL: Extract-Load-Transform pattern allows for faster ingestion and more flexible transformation
- Infrastructure as Code: Terraform ensures consistent, repeatable deployments across environments
- Unified Metadata: Centralized metadata management enables better governance and discoverability
Impact
The implementation delivered significant business value across multiple dimensions:
Consolidated Data Architecture
- Single Source of Truth: Consolidated data from heterogeneous systems into a unified platform
- Cross-Functional Analytics: Enabled analytics across previously siloed business units
- Improved Data Quality: Standardized metadata and master data management practices
Performance Improvements
- 65% Reduction in Processing Latency: Enabled near real-time reporting for operational and financial teams
- Faster Query Performance: Optimized storage and partitioning improved analytical query speeds
- Scalable Infrastructure: Cloud-native architecture handles growing data volumes efficiently
Business Enablement
- Enhanced Governance: Improved compliance and trust in data through better tracking and lineage
- AI/ML Readiness: Reliable, clean datasets empowered business users for advanced analytics initiatives
- Operational Dashboards: Real-time KPIs and dashboards support faster decision-making
Strategic Value
- Future-Proof Architecture: Scalable foundation supports continued growth and new use cases
- Cost Optimization: Cloud-native approach reduced infrastructure costs while improving performance
- Competitive Advantage: Faster insights enable more responsive business decisions
Conclusion
This solution demonstrated DataWired Solutions' ability to tackle complex, multi-source data environments, delivering scalable, secure, and high-performing analytics platforms for enterprise decision-making. The unified data lakehouse architecture not only solved immediate challenges but also positioned the organization for future growth and innovation.
The success of this project showcases our expertise in:
- Enterprise data architecture and integration
- Cloud-native solutions on AWS
- Data governance and quality management
- Real-time analytics and streaming data processing
- Infrastructure automation and DevOps practices
