Databricks, which runs an Apache Spark-based data analytics platform, raises $250M Series E led by Andreessen Horowitz at a $2.75B valuation
Today Databricks is announcing that it has raised $250 million in a Series E funding round led by Andreessen Horowitz, a firm which has led three …
Context & Ripple Effects
This round extends a run that began with the $60M Series C led by NEA in late 2016, where Andreessen Horowitz participated, and continued when the same firm led the $140M Series D in August 2017. Leading again at $2.75B makes a16z Databricks' most consistent private-market backer across the Apache Spark platform's scaling years.
The $250M raise lands mid-arc: within roughly two years the company would be raising billion-dollar rounds, and the later coverage shows the pattern compounding — a $500M+ Series I at $43B in 2023 explicitly framed as pre-IPO, followed by a $5B equity-plus-$2B debt round at $134B in early 2026 with annualized revenue past $5.4B.
First-order effects
- Databricks gains $250M to scale its Spark-based analytics platform commercially, with Andreessen Horowitz consolidating its lead-investor position after back-to-back led rounds.
- NEA's Series C lead role is now clearly superseded: control of the cap table narrative has shifted to a16z, which has led every round since 2017.
Second-order effects
- Rival big-data analytics vendors face a competitor with fresh capital and an open-source distribution base in Apache Spark, pressuring them to raise comparably large rounds to fund platform breadth rather than point tools.
- Late-stage investors watching the Series C-to-E climb — $60M to $250M in about two years — get a template for pricing enterprise data-platform stakes before revenue fully matures.
Third-order effects
- If the trajectory holds, Databricks becomes a case study in the mega-round treadmill: each successive raise (through the $43B Series I and beyond) resets the bar for what a private data company can absorb while deferring an IPO that coverage keeps anticipating but never arrives.
- The open-source-core commercial model — Spark as free substrate, Databricks as paid platform — points toward data infrastructure consolidating around a few heavily capitalized platform companies rather than many tooling vendors.
The trend: Enterprise data platforms are compounding through ever-larger private rounds led by repeat investors, stretching the path from Series C to public markets into a decade-long arc.