Automated Data Quality & Notifications with Azure Databricks, PySpark, Logic Apps & SharePoint
Data accuracy is mission critical in retail, yet manual file processing often leads to schema mismatches, missing fields and naming errors that disrupt downstream systems. A leading UK based retail MNC faced frequent reporting delays, rising debugging costs and customer dissatisfaction due to these recurring data quality issues.
To resolve this, the company implemented an Automated Client Data Quality Management and Notification Process using Azure Databricks, PySpark, Azure Logic Apps and SharePoint/Blob Storage. The system automates file validation, schema and null checks and triggers instant email alerts for any detected errors, eliminating manual intervention and reducing processing time significantly.
The results were transformative: manual review time dropped from 30 minutes to under 5, error alerts became instantaneous, data ingestion delays reduced by 90% and client satisfaction surged. This scalable automation framework now ensures higher data integrity and faster operations and can be easily extended to other data intensive sectors such as finance, logistics and healthcare.
Author
Nikhil
Mohod
I'm a Data Engineer with 8 years of experience specialising in the Azure data ecosystem. I design and implement scalable data pipelines, lakes and ETL/ELT solutions using tools like ADF, Airflow, Databricks, Synapse and SQL Server. Focused on building high-quality, secure, and optimised cloud data architecture.
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Nikhil
Mohod
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In today’s retail environment, data accuracy is critical for smooth operations. For a leading UK based retail MNC, processing manual files often introduces errors such as schema mismatches, incorrect file names, or missing fields, which frequently impact downstream applications. These issues, including inconsistent schemas, missing fields and incorrect file formats or names, significantly increase processing time and disrupt downstream systems.
Challenges
Missing reporting and reconciliation deadlines affecting timely decision making.
Rising operational and debugging costs due to manual error handling.
Delayed processing leading to reduced customer satisfaction.
Threats to overall data integrity and trust across systems.
Solution
Tools and Technologies Used:
Azure Databricks: High performance data processing and schema validation.
PySpark: Custom validation logic for schema, null checks, and metadata.
Azure Logic Apps: Automated error notifications via email.
SharePoint / Blob Storage: Sources for file ingestion.
Step-by-Step Walkthrough:
File Upload: Upload the file to SharePoint.
Validation: Databricks + PySpark flow helps in checking the schema, file name and null values.
Error Notification: Logic Apps helps in triggering an email with a detailed error.
Continuous Retry: The process is repeated till the file is processed completely.
Impact
Conclusion
The automated pipeline successfully eliminated manual intervention, significantly reducing processing time while enabling instant error detection through automated notifications. This improvement enhanced downstream process reliability and overall data quality, leading to higher client satisfaction and reduced operational costs. Moreover, the solution’s scalability allows it to be seamlessly extended to other industries such as finance, logistics and healthcare. Want to see how this could work in your environment? Talk to an expert now.
