Retail & ecommerce · Databricks
ElasticRun: Supporting underserved communities with better access to grocery and consumer goods
Public case study · not a Metric Oak engagement
- Machine learning models managed
- 10,000+
- Fewer data pipeline slowdowns from large data volumes
- 90%
- In cost savings on more than 20 systems
- 33%
What happened
The story in brief
An Indian B2B e-commerce platform serving rural family-owned stores needed reliable data infrastructure to optimize supply chains. Rebuilding on Databricks reduced pipeline slowdowns under large data volumes.
How it compares
- 33% in cost savings on more than 20 systems, against a median reported cost reduction of 50% across 16 retail & ecommerce results.
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Frequently asked questions
What results did ElasticRun report?
ElasticRun reported 10,000+ machine learning models managed; 90% fewer data pipeline slowdowns from large data volumes; 33% in cost savings on more than 20 systems, according to its customer story published by Databricks.
Which platform and technologies did ElasticRun use?
ElasticRun used Databricks, including Unity Catalog, Delta Lake, Lakeflow, Declarative pipelines, Databricks SQL, MLflow.
How do these results compare with other retail & ecommerce programs?
33% in cost savings on more than 20 systems, against a median reported cost reduction of 50% across 16 retail & ecommerce results.
Source
Customer story published by Databricks: Supporting underserved communities with better access to grocery and consumer goods (opens databricks.com in a new tab) on databricks.com. Figures as published, retrieved 27 October 2026. The summary above is Metric Oak's own. This is not a Metric Oak engagement.
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