Intelligent Data Pipelines Built to Clean, Prevent, and Scale — Powered by Azure & Databricks
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Client Overview
The Challenge
Their system faced anomalies in three core domains:
Product information
Customer profiles
Order transactions
These issues impacted downstream dashboards, supply chain decisions, and even customer experience. The goals were two-fold:
Product information
Customer profiles
Order transactions
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Key Constraints
High Data Volume: Millions of records spread across rapidly growing tables necessitated real-time or near real-time anomaly detection and handling.
Limited Native Features: The RMS platform lacked modern validation and detection capabilities; external pipelines were necessary.
Enterprise Standards: The solution had to be secure, compliant, and fully cloud-native; no legacy scripts or manual workflows.
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Our Approach
We engineered a robust data pipeline using Azure, Qlik Replicate, and Databricks, structured around a Medallion Architecture to ensure clarity, observability, and scale.
Step 1: Azure-Based Data Ingestion
Migrated data using a two-tiered approach:
- Full load for historical records.
- Change Data Capture (CDC) for real-time updates.
- Used Qlik Replicate for low-latency, high-throughput data movement from RMS to Azure.
Step 2: Medallion Architecture in Azure Data Lake
- Bronze Layer: Raw RMS data.
- Silver Layer: Cleaned and normalized data.
- Gold Layer: Aggregated, anomaly-free datasets ready for reporting and model training.
Step 3: PySpark + ML Pipelines in Databricks
- Built custom PySpark jobs to clean and normalize product codes, orders, and profiles.
- Applied ML techniques like Isolation Forest and DBSCAN for anomaly detection.
- Scheduled via Databricks Workflows for full automation.
Step 4: Real-Time Anomaly Prevention
- Incoming data was validated using learned anomaly patterns.
- Flagged or suspicious records were auto-logged and alerted.
- The system continuously learned and adapted over time.
The Outcome
90%+ Reduction in Anomalous Data Across RMS Tables
Real-Time Validation Prevented Bad Data from Entering Production
Fully Automated, Low-Latency Pipelines Using Qlik + Databricks
Cloud-Native and Scalable Design Supporting Millions of Records Daily
Reliable Data Enabled Confident Decisions Across Multiple Business Units
What Made This Work
Layered Architecture: Medallion structure ensured clean separation of raw, refined, and curated data for transparency and control.
ML-Powered Detection: Machine learning helped uncover subtle, pattern-based anomalies that traditional rules missed entirely.
Real-Time Ingestion: Qlik Replicate enables fast and efficient data sync without the need for legacy ETL tools.
Databricks Flexibility: Unified batch and stream processing under one scalable platform using PySpark and MLlib.
Workflow Automation: Replaced manual tasks with scheduled, orchestrated jobs for cleaning and transformation.
Improved Trust: Transparent audit trails and alerts boosted stakeholder confidence in the RMS data ecosystem.
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Need to bring structure, accuracy, and automation to your enterprise data systems? From ingestion to intelligent validation, we design end-to-end data workflows that scale with confidence.
Want to make the most out of your data systems?Let’s talk.CASE STUDIES
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