Fuel Analysis Case Study
SQL Server
PythonSFTP
Azure Data FactoryETLAbout Energy Client
Fixed the system of Predictive Intelligence that integrates internal data with the largest unified source of world trends and econometrics — reducing pre-staging database processing time from 10–11 hours to just 30 minutes.
MagnusMinds engineered a high-performance ETL solution that transformed a 10-hour manual data processing bottleneck into a fully automated 30-minute pipeline. Our SQL Server and SSIS expertise allowed us to identify and implement a specific parallelism technique that delivered results far beyond the client's initial expectations — in under one week.
Our Approach
Challenge & Solution
The Challenge
The client needed zip files containing fuel analysis data from various fuel branch units and organizations to be loaded into their pre-staging databases. Each zip file contained approximately 3 GB of data and was taking 10 to 11 hours to process — a critical bottleneck for their analytics pipeline. The challenge was to drastically reduce processing time and make the ETL pipeline as fast as possible.
Our Solution
We reviewed their entire process and found they were using Python Scripts inside the database. We then conceptualized and executed a new approach: designed an SSIS-based ETL pipeline that downloads zip files from SFTP, extracts them, dumps data into SQL staging tables, processes staging data per business rules, and archives extracted files with email notifications on errors. We implemented parallelism to load data concurrently, reducing processing time first from 10–11 hours to 1 hour. Continuing to optimize, we discovered a specific SSIS technique that further cut processing to just 30 minutes — completing the entire project in approximately one week.
What We Built
Key Features
SSIS ETL pipeline with parallelism
SFTP automated file download and extraction
SQL Server staging table processing
Business rules transformation layer
Automated file archiving after processing
Email notification on package errors
Impact
Results & Outcomes
Processing time cut from 10–11 hours to 30 minutes
3 GB zip files processed in under 30 minutes
Automated SFTP download, extract, and archive pipeline
Email alerts on ETL errors for proactive monitoring
Stack
Technologies Used
Client
Energy Client
Industry
Energy / Data Engineering
Technologies
SSIS, SQL Server, Python…
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