Real-time Analytics · Supply Chain Optimization
ElasticRun needed a better way to support store-level demand forecasting and supply chain analytics for rural commerce because pipeline slowdowns and brittle infrastructure made it hard to optimize inventory and delivery for millions of underserved stores.
Reported impact
ElasticRun improved store-level demand forecasting and supply chain analytics for rural commerce with measurable results including 10,000+ machine learning models managed, 90% fewer data pipeline slowdowns from large data volumes, and In cost savings on more than 20 systems, while giving teams a faster path from governed data to production decisions.
ElasticRun uses Databricks Data + AI Platform to support store-level demand forecasting and supply chain analytics for rural commerce. The implementation replaced a legacy state where pipeline slowdowns and brittle infrastructure made it hard to optimize inventory and delivery for millions of underserved stores. Rural commerce transactions and inventory and logistics data move through Lakeflow Jobs and Spark Declarative Pipelines and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Databricks SQL, MLflow, and Agent Bricks operationalize models, agents, and analytics that deliver demand forecasts and supply chain decisions.
Rural commerce transactions and inventory and logistics data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver demand forecasts and supply chain decisions.
By consolidating 2,500 reports into 50–60 dashboards, we’ve shifted our focus from troubleshooting to innovation,
Delta Lake gave us the ability to scale effortlessly, allowing us to focus on driving real-time insights instead of worrying about system failures.
ElasticRun, India’s only B2B e-commerce platform, connects brands with millions in rural family-owned businesses. The brand aims to target these hard-to-penetrate markets and drive growth in underserved areas of India. E
Our existing pipelines and data infrastructure had hit a glass ceiling. We needed a reliable data and AI platform that could meet our current data demands and scale as we continued to grow,
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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