Client Data Quality Automation & Alerts using Databricks
Automated pipeline in Databricks validates manual file uploads, triggers real-time alerts and reduces errors, delays and manual effort by 90%.
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Introduction
A UK based retail MNC offering clothing, food and home products across physical and online channels relies heavily on high quality data for seamless global operations. The client faced recurring issues in manual file uploads, such as schema mismatches, missing fields, and incorrect columns, causing downstream interruptions and inefficiencies. To address this, Cloudaeon developed an automated client data quality management and notification process using Azure Databricks, PySpark and Logic Apps. The solution validates and processes manual files, integrates with existing pipelines and enhances data accuracy, reducing rework and improving overall operational efficiency.
Challenges
Manual file uploads at the source led to recurring data quality issues, incorrect file names, schema mismatches, missing fields and disordered columns, causing downstream delays and extended debugging cycles.
Key issues included:
Incorrect file names or extensions
Schema mismatches or disordered columns
Null or missing values in key fields
Time consuming manual validation
As several processes depended on manual file inputs, automating validation was critical to prevent system failures, reduce operational overhead and ensure data integrity.
Solution
A fully automated data validation and notification system was developed to eliminate manual errors and improve downstream reliability.
Tech stack used:
Azure Databricks: For data processing and validation
PySpark: To apply schema and data quality checks
Azure Logic Apps: To trigger real-time email alerts
SharePoint/Blob Storage: As the file ingestion source
Since most client operations already ran on Azure, the solution integrated seamlessly without additional tech stacks. Custom schema validations and alert templates enabled instant error detection and correction, ensuring faster and more reliable data processing.
Impact
Conclusion
The automated Databricks pipeline eliminated manual file errors by validating each column, checking for nulls and verifying file names and formats. Real-time alerts via Azure Logic Apps ensured immediate correction, enabling continuous, error free ingestion into downstream applications. Want to know what automated Databricks pipelines can do for you? Let's talk.
