Unified Data Pipeline for Supply Chain Optimization
Coca-Cola's global supply chain generates massive volumes of shipment data from disparate sources across continents. The existing infrastructure lacked the processing power and integration needed for real-time visibility. Delayed insights, manual reconciliation, and limited optimization capabilities created operational bottlenecks at scale. Vrahad Analytics was engaged to build an end-to-end data pipeline using Azure Databricks and Azure Data Factory that would transform supply chain visibility from batch-delayed to real-time, enabling data-driven decision making at every level of the logistics operation.
Client
Coca-Cola
Cloud Platform
Azure
Duration
4 Months
The Challenge
Coca-Cola's supply chain operations span dozens of countries and hundreds of distribution centers, generating enormous data volumes daily. The existing data infrastructure was not designed for the real-time analytics demands of modern global logistics.
Shipment data originated from dozens of disparate source systems — ERPs, warehouse management systems, IoT sensors, third-party logistics providers — each with different formats, update frequencies, and data quality levels.
The existing batch processing architecture introduced 12-24 hour delays in supply chain visibility, meaning logistics managers were always making decisions based on yesterday's data rather than current conditions.
Manual data reconciliation across regions consumed significant analyst time, with teams spending more effort on data wrangling than on actual supply chain optimization and strategic planning.
The lack of a unified data model across regions made it impossible to generate consistent global reports or perform cross-regional analytics for route optimization and demand forecasting.
Performance bottlenecks in the existing Spark jobs meant that even batch processing was increasingly slow as data volumes grew, with some jobs taking 8+ hours to complete.
No real-time alerting or anomaly detection for supply chain disruptions, meaning issues like delayed shipments or inventory shortages were only discovered after they had already impacted operations.
Our Solution
We built an end-to-end data pipeline architecture using Azure Databricks for complex transformations and Azure Data Factory for orchestration. The centerpiece was a real-time shipment tracking system that provided instant supply chain visibility across all regions. On-site collaboration ensured tight alignment with business requirements at every stage.
Designed a unified data ingestion layer using Azure Data Factory that consolidated shipment data from 30+ source systems into a single, consistent pipeline with automated schema mapping and data quality validation at ingestion.
Built high-performance transformation pipelines in Azure Databricks using optimized Spark code, leveraging Delta Lake for ACID transactions and time-travel capabilities that enabled point-in-time supply chain analysis.
Developed a real-time shipment tracking system using Structured Streaming that processes events as they arrive, providing sub-minute latency for supply chain visibility across all global regions.
Created a standardized global data model that normalizes shipment, inventory, and logistics data across all regions into a consistent schema, enabling apples-to-apples comparisons and global roll-up reporting.
Implemented automated anomaly detection that flags supply chain disruptions — delayed shipments, unusual volume patterns, route deviations — in real-time, enabling proactive intervention before issues cascade.
Optimized Spark job performance through advanced techniques including dynamic partition pruning, broadcast joins for dimension tables, and Z-ordering on Delta Lake tables, reducing processing times by 70%.
Implementation Phases
Requirements & Data Discovery
3 WeeksWorked on-site with Coca-Cola's supply chain and data teams to map all data sources, understand business KPIs, define the target data model, and establish performance benchmarks for the new pipeline.
Pipeline Architecture & ADF Orchestration
4 WeeksDesigned and implemented the Azure Data Factory orchestration layer, including parameterized pipelines for each data source, error handling, retry logic, and monitoring dashboards for pipeline health.
Databricks Transformation Development
5 WeeksBuilt all transformation logic in Azure Databricks using PySpark and Delta Lake. Implemented the bronze → silver → gold medallion pattern with data quality checks at each tier. Optimized Spark configurations for performance.
Real-Time Streaming Layer
3 WeeksImplemented the real-time shipment tracking system using Structured Streaming, processing events from Azure Event Hubs and writing to Delta Lake tables with sub-minute latency for dashboard consumption.
Testing, Optimization & Go-Live
3 WeeksConducted end-to-end testing with production-scale data, optimized query performance, deployed monitoring and alerting, and executed a phased go-live across regions starting with North America.
Technologies Used
Key Results
Measurable outcomes and business impact delivered through this engagement.
Delivered real-time shipment tracking for global supply chain visibility, reducing data latency from 12-24 hours to under 1 minute for critical logistics metrics.
Unified 30+ disparate data sources into a single, consistent pipeline with automated schema validation and data quality enforcement at every stage.
Significantly improved logistics efficiency through data-driven insights, enabling route optimization and proactive disruption management that reduced average delivery times.
Optimized pipeline performance with advanced Spark coding techniques, reducing batch processing times by 70% (from 8+ hours to under 2.5 hours) and enabling more frequent data refreshes.
Enabled faster, data-driven decision making for supply chain management with self-service analytics dashboards that provide real-time KPIs to managers across all regions.
Implemented automated anomaly detection that catches supply chain disruptions within minutes, enabling proactive intervention and reducing disruption impact by an estimated 40%.
Before vs. After Comparison
Data Latency
Before
12-24 hours
After
< 1 minute
99.9% faster
Batch Processing
Before
8+ hours
After
2.5 hours
70% faster
Data Sources Unified
Before
Siloed systems
After
30+ unified
Single pipeline
Manual Reconciliation
Before
40+ hrs/week
After
< 5 hrs/week
87% reduction
Disruption Detection
Before
Hours to days
After
Minutes
Real-time alerts
Regional Coverage
Before
Partial views
After
Global unified
100% coverage
Pipeline Scale Metrics
Data Source Distribution
Supply Chain Data Capability — Before vs After
Data Latency Reduction Over Time (Hours)
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