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Data Infrastructure · Data & AI Platforms
Databricks
Provides a data intelligence platform spanning analytics, governance, and AI workloads.
- Research status
- Research coverage
- Headquarters
- San Francisco, CA
- Coverage
- 4 product lines mapped
- Last reviewed
- September 2026
Product map
What the company builds
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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.
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 founderCo-founder
Matei Zaharia
Matei Zaharia is a co-founder of Databricks. Created Apache Spark, one of the technologies behind the company’s origins.
Explore founderCo-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.
Explore founderSelected 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.
Databricks
Databricks — official video channel
Product demonstrations, company presentations and original updates, linked from the company’s own website.
Visit original material ↗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.
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.
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.
The convergence of enterprise data infrastructure and generative AI deployment.
Private-company information is incomplete, episodic, and difficult to verify. Treat every data point as a starting point for diligence—not a substitute for company-approved materials, legal review, or investment analysis.
Add to my private currentSignals to monitor
Net retention and workload growthAI product adoptionLarge-enterprise expansionRisks to underwrite
Cloud-platform competitionComplex deploymentsPrivate-market pricing Underwriting map
What an investor should actually diligence
Underwrite the platform as a workload consolidation and expansion story: storage, compute, governance, analytics, and AI should increase spend per customer without creating uncontrolled cloud pass-through costs.
Buyer map
01
Chief data and technology officers
02
Data engineering teams
03
Analytics and AI teams
04
Security and governance leaders
Economics to request
01
Consumption growth by cohort
02
Net retention and workload expansion
03
Cloud infrastructure gross margin
04
Sales-cycle duration and implementation cost
05
Revenue concentration by cloud partner
Moat tests
01
Open formats still produce durable platform attachment
02
Governance creates cross-workload switching costs
03
AI workloads expand the core rather than cannibalize it
04
Partners do not disintermediate the platform
Milestone map
01
Large-enterprise wins
02
New workload adoption
03
Margin expansion
04
Ecosystem and marketplace growth
Comparable lensesCloud data platformsData warehousesAnalytics infrastructureAI development platforms
Underwriting framework
How to diligence Databricks
A company-specific starting point based on the operating realities of data infrastructure. Replace assumptions with verified company materials, customer evidence, legal documents, and expert work.
Consumption growth and net retention
Cloud cost and gross-margin efficiency
Large-customer concentration
Sales efficiency and workload expansion
Mission-critical workloads in production
Multi-product adoption
Data migration and switching costs
Governed AI usage on the same platform
Cloud data warehouses
Database platforms
Analytics and observability vendors
Hyperscaler-native alternatives
What workload starts the relationship?
Where does pricing power come from?
Can hyperscalers bundle the feature?
How portable is the customer’s data and logic?
No securities are offered here.
SMYC does not represent that shares are available, transferable, appropriately priced, or suitable for any person. Private securities are illiquid, speculative, subject to transfer restrictions, and may result in total loss.