Leading Utility Company Modernizes Data Workflows with PDI’s AI-Driven Migration Framework, Empowering Big Data Scalability and Analytical Readiness
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35%
Reduction in manual effort |
90%
Legacy workflows migrated successfully
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45%
Fewer Data Errors
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50%
Faster Processing Times
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About the Customer
The client is one of the largest regulated utility providers in North America, supplying electricity and natural gas to millions of residential and commercial customers. With rising demands on infrastructure and data complexity, the utility’s engineering team manages billions of smart meter readings, event logs, and outage reports critical to real-time monitoring and long-term planning.
Customer Challenges
The customer had long utilized a proven, on-premises Informatica PowerCenter environment that effectively supported their foundational data workflows; as the organization’s data landscape expanded, the customer faced limitations in scalability and performance inherent to legacy architectures. During peak months, extended batch processing frequently delayed downstream analytics and billing cycles. The aging platform demanded costly infrastructure investments and imposed rigid licensing constraints. A decade accumulation of siloed ETL development had resulted in hundreds of mappings and custom shell scripts.
This legacy architecture not only increased maintenance overhead but also hindered integration with modern, distributed processing platforms. Manual QA cycles further slowed down innovation due to a lack of test automation and data parity checks. With an enterprise-wide directive to enable open-source and big data adoption, the utility needed to modernize its ETL ecosystem to PySpark, offering scalability, code-based transformation, and seamless integration with its evolving data lake strategy.
PDI Solution
Pacific Data Integrators delivered a strategic, AI-powered migration from PowerCenter to PySpark, leveraging its proprietary accelerators: the PowerCenter-to-PySpark
Code Converter and the
Data Validation Framework (DVF). These tools automated the conversion of mappings and ensured fidelity through side-by-side schema and data comparisons, all while minimizing manual intervention and operational risk.
Scope of Services
PDI executed a full-lifecycle transformation tailored to the client's enterprise architecture, covering everything from discovery to post-deployment enablement.
Key services included:
1. Discovery & Impact Analysis: PDI conducted an automated inventory of the client’s PowerCenter mappings, workflows, and dependencies. This helped identify redundant components and reusable logic, setting a strong foundation for efficient modernization.
2. AI-Driven Mapping Conversion: PowerCenter logic was translated into modular, reusable PySpark code using AI-driven tools. The code was parameterized and optimized for scalable execution on the client’s distributed environment.
3. Automated Validation and QA: Schema and row-level validation was performed using the Data Validation Framework (DVF), with automated regression testing and reporting to ensure accuracy and consistency across systems.
4. Deployment Automation: Robust CI/CD pipelines were built for PySpark jobs, integrated directly with the client’s existing Hadoop and Spark clusters to support seamless and automated deployments.
5. Production Cutover and Support: During go-live, PDI provided hyper-care support and conducted hands-on knowledge transfer sessions, enabling the client’s teams to confidently manage and enhance the new PySpark environment.
Transformative Business Impact
Accelerated Migration to Open Source: Through intelligent automation, PDI completed the migration of all critical ETL pipelines to PySpark months ahead of schedule. The transition avoided production downtime and met internal compliance checkpoints, while enabling long-term cost efficiency.
Lowered TCO and Infrastructure Overhead: By retiring the on-prem PowerCenter platform and adopting Spark-native processing, the client eliminated licensing costs, reduced server footprint, and consolidated processing to a scalable big data architecture.
Scalable Performance and Job Flexibility: Spark’s distributed computing power allowed the customer to handle growing data volumes with ease. Batch execution times improved by 50%, enabling real-time analytics and dynamic scaling.
Streamlined QA and Compliance: PDI’s DVF drastically reduced the time spent on manual QA cycles, delivering confidence in data integrity. Side-by-side comparisons ensured regulatory compliance across all migrated pipelines.
Future-Ready Data Architecture: With modular PySpark jobs integrated into the client’s Hadoop-based data lake, the organization can now pursue advanced analytics, AI/ML model development, and real-time streaming use cases. This foundation enables faster data pipelines, quicker innovation, and greater agility.
Faster Time to Insight: With improved data freshness and real-time availability, business units gained quicker access to actionable insights. Dashboards and reporting systems now reflect near-real-time metrics, accelerating decision-making across departments.
Quantifiable Results
Full Migration Success: 90% of PowerCenter workflows were converted to PySpark with functional parity
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Cost Optimization: Eliminated legacy platform costs and streamlined operational maintenance |
QA Time Reduction: Regression testing time reduced by 70%
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Zero Downtime:
Cutover occurred with no interruption to operational systems
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About Pacific Data Integrators:
Pacific Data Integrators (PDI) specializes in data management and analytics implementations. As a certified Informatica partner, PDI has extensive experience modernizing data platforms, helping clients leverage the latest technologies to drive business success. Their track record of delivering projects on schedule and on budget sets them apart as a trusted implementation partner.
Ready to Modernize Your ETL to PySpark?
Reach out to Pacific Data Integrators and discover how our automation-first approach can help you transform your legacy PowerCenter workflows into scalable, open-source PySpark solutions—faster, smarter, and with minimal disruption.