AI/MLGrowthUsage-Based
Databricks
Data and AI platform unifying data engineering, science, and analytics. $1.6B+ ARR by creating the Lakehouse architecture.
Founded: 2013San Francisco7,000+ employeesFunding: $4,200,000,000
Usage-Based
revenueModel
Growth Timeline
2013
Founded
2015
Growth phase
2017
Scale phase
Tools & Technology
Lessons Learned
- 1.Open source creates category definition power
- 2.Data platform economics improve with usage
- 3.Community of data engineers is the best sales force
Ehsan's Growth Analysis
Databricks created the Lakehouse category by combining data lakes and data warehouses. By open-sourcing Apache Spark and Delta Lake, they defined how modern data teams work. When you create the category, you own the first position. That positioning power drove them to $1.6B ARR.
EJ
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council
Frequently Asked Questions
How did Databricks grow?
Databricks created the Lakehouse category by combining data lakes and data warehouses. By open-sourcing Apache Spark and Delta Lake, they defined how modern data teams work. When you create the catego
What growth tactics does Databricks use?
Databricks uses Open Source Growth, Community-Led Growth, Outbound Sales.
What tools does Databricks use?
Key tools include Apache Spark, Delta Lake, Python.