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Letting Data Speak, AI Act!
Case Study
Revolutionizing Data Infrastructure for AI-Driven Green Energy Solutions
About the Client
A leading provider of AI-based Green Energy solutions catering to electricity generation and distribution companies. The client serves over 40 utility companies, managing vast amounts of energy-related data crucial for their AI-driven operations.
Challenge
The client faced several critical challenges in their data management and processing infrastructure:
Inefficient data handling: Their existing system struggled to effectively ingest and process data from 40+ utility companies, including diverse datasets such as energy consumption, meter location, billing information, and consumer demographics.
ETL tool limitations: The client was using Cloud Data Fusion (CDF) as their ETL tool, which was becoming increasingly difficult to manage and maintain as their operations scaled.
Slow client onboarding: The existing setup resulted in a time-consuming process for onboarding new utility companies, hindering the client's growth potential.
High cloud costs: The client was facing escalating Google Cloud costs due to inefficient data processing and resource utilization.
Time-consuming deployment process: Updates and deployments to the ETL pipelines were taking days, significantly impacting the agility of their operations.
Key Results
Accelerated new client onboarding speed by 2x to 3x, significantly enhancing business scalability
Slashed Google Cloud costs by 35% through optimized data processing and resource management
Reduced ETL pipeline deployment time from days to minutes, dramatically improving operational efficiency
Implemented a scalable solution capable of handling data from 40+ utility companies with diverse data types
Comparison between the existing solutions vs our “Templatized Matillion” solution.
Solution
JashDS developed and implemented a comprehensive data engineering solution to address the client's challenges:
Robust Data Pipeline Infrastructure:
Built scalable data pipelines and infrastructure capable of ingesting and processing data from over 40 utility companies
Designed the system to handle diverse data types including energy consumption data, meter location information, billing data, and consumer demographics
ETL Tool Upgrade:
Replaced the existing Cloud Data Fusion (CDF) ETL tool with Matillion
This transition significantly improved the manageability and maintenance of the ETL processes
Reusable Matillion Components:
Identified common data processing patterns across clients' ETL processes
Developed reusable Matillion components, creating Lego-like building blocks
These components can be seamlessly connected to create customized ETL pipelines
This innovation enabled the client's team to onboard new clients 2-3 times faster than before
Automated CI/CD Pipeline:
Implemented fully automated CI/CD pipelines using CircleCI
This automation allows for efficient updates to all data pipelines when one of the underlying Matillion blocks is upgraded or updated
Developed an automated regression test suite that runs post-upgrade to ensure no breakages occur in the updated pipelines
Reduced deployment time for ETL pipelines from days to minutes, significantly enhancing operational agility
Cost Optimization:
Rewrote SQL queries running on BigQuery to improve efficiency
Implemented quotas to keep infrastructure costs in check
Re-engineered data pipelines to perform more efficient data aggregation
These optimizations resulted in a 35% reduction in Google Cloud costs
Scalable and Efficient Solution:
The new "Templatized Matillion" solution significantly outperformed the existing solutions
Provided a scalable framework that can easily adapt to new clients and changing data requirements
Improved overall data processing efficiency, enabling faster insights and decision-making for the client's AI-based green energy solutions
Technologies Used
Matillion (ETL tool)
Google Cloud Platform (GCP)
BigQuery
CircleCI
SQL
CI/CD methodologies
Cloud infrastructure management
Other Case Study Items
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Analytics SaaS Platform for the Hospitality Industry
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