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Letting Data Speak!
Case Study
Modernizing Data Ingestion for Green Energy AI
About the Client
A provider of AI-based Green Energy solutions serving 40+ utility companies in electricity generation and distribution.
Challenge
The client faced the complex task of ingesting data from various tenants with legacy systems into a modern data warehouse. This process needed to align with the latest data warehouse specifications efficiently and effectively while maintaining data integrity and accuracy throughout the transition.
Key Results
Reduced pipeline creation time by 40% through the implementation of the pipeline_builder library for automating the pipeline creation process.
Reduced onboarding time for a new tenant by 50% (8 weeks to 4 weeks)
Improved data accessibility and reliability for 40+ utility companies
Streamlined pipeline creation process, reducing manual coding efforts by 80%
Solution
To address the challenge, JashDS developed a robust tool called the pipeline_builder library. The solution involved:
Defined a standard template to capture data mapping rules
Designing an intelligent pipeline_builder library that can create data pipelines by translating data mapping rules into boilerplate pipeline code.
The automation covered 80% to 90% of standard mapping rules like renaming data columns, extracting data from a field using regex, etc. The remaining 10 to 20% of customization was the manual coding effort required by the pipeline developers.
Developing ingest, export, and master jobs to automate and streamline data processing.
Developed automated test cases from the data mapping rules. These are executed as part of the nightly integration tests and have identified several regression issues till now.
Technologies Used
GCP - Google Cloud Storage, Dataflow, Composer (Airflow), Cloud Functions
Matillion
CircleCI
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Revolutionizing Data Infrastructure for AI-Driven Green Energy Solutions
JashDS revolutionized a green energy tech company's data infrastructure by implementing a scalable Matillion-based ETL solution and automated CI/CD processes, resulting in 2-3x faster client onboarding and a 35% reduction in Google Cloud costs. The comprehensive solution included reusable components, optimized SQL queries, and efficient data aggregation techniques, enhancing the client's ability to process vast amounts of utility data from 40+ companies and support their AI-driven green energy initiatives.
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