ElasticRun helps small businesses improve supply chain with Databricks

Overview

Indian B2B e-commerce platform ElasticRun rebuilt its data infrastructure on Databricks with Delta Lake, Spark Declarative Pipelines and MLflow to manage over 10,000 machine learning models for supply chain and demand forecasting, cutting data pipeline slowdowns by 90% and IT costs by 33%.

The challenge

ElasticRun's open source data infrastructure, ElasticRun Connect, required multiple scale-ups and refactoring beyond a certain scale, causing pipeline delays at least twice a month due to data volume, while its supply chain software, ElasticRun Logistics, needed real-time data for inventory management, shipment tracking and demand forecasting that the existing infrastructure could not support.

The solution

ElasticRun adopted the Databricks Data + AI Platform with Delta Lake's medallion architecture (Bronze, Silver, Gold layers) at the core, deployed Databricks Spark Declarative Pipelines to automate data transformations across environments, used Databricks Lakeflow Jobs to streamline data ingestion and real-time inventory adjustments, and relied on MLflow to manage over 10,000 machine learning models plus Databricks SQL and Unity Catalog for governed access for over 1,500 internal users.

Reported business value

Adoption of Databricks resulted in a 90% reduction in pipeline slowdowns and infrastructure incidents, a 33% cost savings across more than 20 systems, a 25% improvement in internal net promoter score, migration of 80% of jobs within six months, and consolidation of 2,500 reports into 50–60 dashboards.