Databricks company overview
About
Expert Vetted Data
Public disclosures rarely capture a company's internal dynamics or the true state of its technology. Gain first-hand insight by speaking with former Databricks executives.
Ownership & Key Financials
Revenue
Investors
Revenue / FTE
Ownership
FTEs
Products & Services
Other
Description: Supporting services and specialized solutions that complement the core data platform offering.Name: Training & Certification, Professiona• ••••••••, ••••••••• & ••••••••••, •••••••• •••••••••, •••••••• ••••••••••••••• ••••: •••• ••• ••••••••••••••, ••••••••, ••••••••-•••••••• •••••••••, ••• ••••••••• •• ••• ••••••••••• ••••••••: •••••••••••••• ••••••••, ••••••••••••• ••••••••, •••-••••• ••••••••••••, ••••••••• •••••, ••••••••-•••••••• •••••••••
Core Data Platform
Description: Unified data platform based on lakehouse architecture that covers data warehousing, data engineering, data science, and machine learning ••• •••••••••: •••••••••• •••• •••••••••••• ••••••••, •••• •••••••, •••• •••••••••••, •••••••• ••••••••••••, •••••••••• ••••••••••••, •••••••••• ••••••••• ••••••••, •••• •••••••••••, ••••• •••••••••• ••••: •••• •• ••••••••••• ••• ••••• •••• •••••••• ••••• ••••••••• •••••••••, •••• •••••••••••, •••• •••••••, ••• ••••••• •••••••• ••••••••••••••• ••••••••: •••••••••• •••••••, •••• ••••••••••••, •••••• •••••••••••, ••••-•••• •••••••••, ••••••••• •••••••••, ••••••• •••••••• •••••••••, •••••••••• ••••••••, •••• ••••••• ••••••••••••
Pricing & Go-to-market
Typical Contract Length
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Pricing model
Usage-based consumption model with Databricks Units (DBUs) billed per second or per token for AI workloads, offering pay-as-you-go flexibility. Custom••• ••• ••• ••• •••••••••-••• ••••••••• •••• ••••••• ••••••••• •• •••••••• ••• •-• •••• •••••••••••, •••• •••••-•••••••• ••••••• ••• •• •••, •••••, ••• •••.
Average Sales Value
••••••••••••• $•••,•••
Average Sales Cycle
• •• •• ••••••
Growth Review
| Company Name | Revenue | FTE | Proprietary Insights | HQ | Ownership Type |
|---|---|---|---|---|---|
 Databricks | $2•••m '•• | •,••• '•• | - | USA | V•••••••• |
 Amazon Web Services (AWS) | - | - | •••••••••• ••• ••• ••••••• •• ••••••••••• ••••••• •• ••• ••••• •••• •••••••• ••••••, •••• ••• ••••••... | - | - |
 Snowflake | - | - | - | - | P••••• |
| G Google Cloud - Minnesota | - | - | - | - | - |
Experts highlight Databricks's operational execution and market approach, while noting enterprise buying cycles as a potential constraint.
What does Origin provide on Databricks?
Origin provides a structured company snapshot of Databricks, combining expert-led insights with analysis across business model, customers, competitors, and market dynamics. The profile is designed to support research, competitive analysis, and commercial due diligence workflows.
How is Origin's analysis of Databricks different from traditional company databases?
Traditional company databases often focus on surface-level metadata such as ownership, funding, and company descriptions. Origin complements these sources with qualitative insights informed by expert interviews, helping teams understand how Databricks operates, competes, and creates value in practice.
Is Origin suitable for researching private companies like Databricks?
Yes. Origin is built to support research on private companies, where public information can be limited or inconsistent. It focuses on insight depth and operational context, which can be useful when evaluating companies like Databricks for investment, partnership, or competitive analysis.
Where does Origin's information on Databricks come from?
Origin insights are derived from expert interviews conducted by Dialectica, combined with structured analysis and secondary validation where appropriate. This approach prioritises first-hand operational perspectives alongside supporting evidence, rather than relying solely on aggregated public data.
Can Origin support commercial due diligence on Databricks?
Origin can support early-stage commercial due diligence by helping teams quickly understand positioning, value drivers, customer dynamics, and potential risks related to Databricks. It is typically used to shape hypotheses and focus areas before deeper primary research.
How does Origin compare to Crunchbase or PitchBook for analysing Databricks?
Crunchbase and PitchBook primarily focus on company metadata, ownership, funding history, and transactions. Origin complements those sources by adding qualitative, insight-led analysis focused on business fundamentals, go-to-market execution, customer reality, and competitive positioning for companies like Databricks.
How do teams use Origin to evaluate VC-backed companies like Databricks?
For VC-backed companies, Origin is often used to assess product differentiation, early go-to-market motion, buyer behaviour, and the credibility of growth narratives. It can help teams understand where Databricks is winning, what is driving adoption, and which constraints may affect scaling.
What does Origin typically reveal about pricing and go-to-market strategy for enterprise software companies like Databricks?
For enterprise software, Origin often focuses on pricing logic, contract structure, buyer personas, procurement friction, and channel strategy. For companies like Databricks, this can help teams assess how revenue is created, what drives expansion, and where sales cycles or retention may impact outcomes.
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