Company Information
A leading marine transportation and ship-assist services provider in the Pacific Northwest, the organization delivers safe, efficient, and sustainable maritime operations. Its services span tug and barge transport, ship docking, and marine logistics, supporting a wide range of commercial and industrial clients.
The Challenge
The client had a well-established analytics ecosystem but encountered growing performance and scalability challenges as data volumes and business demands expanded. Key areas identified for improvement included:
- Data Extraction & Query Performance: Increasing data volumes led to slower extraction and query times, impacting reporting responsiveness.
- Pipeline Reliability & Data Quality: Existing pipelines occasionally failed or produced inconsistent outputs, reducing confidence in reporting.
- Operational Costs: The data infrastructure incurred high compute and storage costs, limiting efficiency and scalability.
- Scalability & Best Practices: The legacy pipelines were not designed with modern data engineering standards, constraining maintainability and automation.
Recognizing these challenges, the client partnered with ProCogia to modernize their data ecosystem and establish a scalable, cost-efficient, and future-ready foundation on Azure.
The Approach
ProCogia designed and implemented a comprehensive modernization strategy to enhance performance, reduce costs, and improve reliability:
- Architecture Assessment & Optimization:
Conducted a detailed review of existing pipelines to identify inefficiencies, duplication, and automation opportunities. Root causes of latency and data quality issues were analyzed to inform redesign decisions. - Modern Data Architecture:
Adopted the Medallion Architecture, segmenting data into:- Bronze: Raw ingestion and archival layer
- Silver: Cleaned and curated data for analysis
- Gold: Business-ready, transformed data for reporting and KPIs
- Azure Data Factory Pipelines:
Re-engineered pipelines using Azure Data Factory (ADF) to orchestrate end-to-end workflows, automate refresh schedules, and manage dependencies. - Lambda Architecture for Near Real-Time Data:
Implemented a Lambda architecture to keep near real-time data refreshed every five minutes, ensuring dashboards and reports always reflect the latest information. - Optimization & Automation Enhancements:
Introduced reusable PySpark modules, unit tests, and incremental data loading mechanisms to reduce full-load operations, lower compute usage, and ensure data freshness.
Architecture
Results
Significant Cost Reduction: Achieved approximately 70% reduction in operational costs and 98% reduction in storage costs. Pipeline processing times improved by ~50%, delivering faster insights and better performance.
Improved Data Quality & Deduplication: Incremental loading and validation ensured no data duplication, reducing compute overhead and improving reporting accuracy for timely business decisions.
Modernized & Scalable Infrastructure: The new solution embedded data engineering best practices — modular code, unit testing, naming standards, robust error handling, monitoring, and version control — ensuring long-term scalability and maintainability.
Reduced Technical Debt & Improved Maintainability: Comprehensive documentation, proactive code management, and process automation minimized future maintenance effort and enabled sustainable growth of the data platform.
Conclusion
With ProCogia’s modernization on Azure, the client now has a scalable, automated data foundation built for growth—delivering faster, more reliable reporting while cutting operational costs by ~70% and dramatically reducing storage spend. The improved pipelines, governance, and engineering best practices ensure the platform remains maintainable, efficient, and ready to support evolving maritime operations and analytics needs.
