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Software / COMPANY INTELLIGENCE

Databricks

Provides a data and AI platform combining data engineering, analytics, governance, databases, and tools for building AI applications.

01 / THE BUSINESS

How Databricks creates value.

An enterprise data platform sells computing and software for analytics, governance and AI. Existing data workflows may support expansion, but cloud competition and consumption economics remain central.

SectorSoftware
Founder coverage3 profiles
Research checkedSep 18, 2026

02 / PEOPLE BEHIND THE COMPANY

Meet the builders.

Ali Ghodsi

Co-founder

Ali Ghodsi

Ali Ghodsi is a co-founder of Databricks. A distributed-systems researcher and member of Databricks’ founding team. The business grew from open-source data technology into an enterprise platform.

Explore founder

Co-founder

Matei Zaharia

Matei Zaharia is a co-founder of Databricks. Created Apache Spark, one of the technologies behind the company’s origins.

Explore founder

Co-founder

Reynold Xin

Reynold Xin is a co-founder of Databricks. The company’s founding and leadership material provides context for its work in data & ai platforms.

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Selected founders; historical founding roles are distinct from current management positions.

03 / CAPITAL & OWNERSHIP

Follow the financing.

Selected disclosed events, newest first. Announcement values describe that transaction and date; they are not current share prices. Debt, equity and secondary transactions are identified separately.

A financing figure is not established here.

Use the original company sources below to check new announcements. We have not substituted an estimate for a disclosed round.

04 / IN THEIR OWN WORDS

Watch. Listen. Form your view.

Interviews reflect the speaker’s perspective at publication. Company statements are not independent verification.

05 / EDITORIAL ANALYSIS

The opportunity. The open questions.

Our interpretation of the business model and cited sources, not a valuation or a recommendation.

What could work

The case to investigate

  • The convergence of enterprise data infrastructure and generative AI deployment.
  • A stronger product position becomes economically meaningful when customers renew, expand and pay enough to cover delivery costs.
What could go wrong

Pressure-test the thesis

  • Cloud-platform competition.
  • Complex deployments.
  • Private-market pricing.

What to watch next

01

Net retention and workload growth — what changed in the latest original disclosure?

02

AI product adoption — what changed in the latest original disclosure?

03

Large-enterprise expansion — what changed in the latest original disclosure?

YOUR RESEARCH WORKSPACE

Keep the questions that matter.

Use this checklist to record what you have investigated. Your notes stay in this browser.

06 / RESEARCH TRAIL

Go straight to the source.

Research checked Sep 18, 2026. Selected public disclosures, not a complete capitalization table or securities-filing search. Missing information is left unconfirmed. No investment availability or IPO date is implied.