Databricks

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Databricks topic overview

The analysis highlights History, Art, Companies and Products as prominent areas in the source structure around Databricks.

Related topics: 88
Source areas: 5
Connected nodes: 93
Extracted relationships: 108
Concept neighborhoods: 31
Bridge connections: 93

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Products · 24 topics

Partnerships and acquisitions · 22 topics

History · 17 topics

Overview · 15 topics

Finance · 10 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Founded: 2013; 13 years ago (2013)
Founders: Ali Ghodsi · Andy Konwinski · Ion Stoica · Patrick Wendell · Reynold Xin
Industry: Computer software Artificial intelligence
Headquarters: San Francisco, California, United States
Net income: Free cash flow positive (2025)
Number of employees: 10,000 (2026)

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

History

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Databricks connects Entity context

The extracted context around Databricks shows recurring relationship patterns in the source. For example, Databricks → Alphabet Inc, Amazon Web Services, Andreessen Horowitz, August, Blackstone Inc, CapitalG, Coatue Management, December, February, Franklin Templeton, Google's MapReduce, In, In August, In December, In July, In September, Just, MGX, Microsoft, Other Another extracted example is Databricks → AI, Arcion, ChatGPT, ChatGPT-like, Dolly, German, In, In June, In March, In May, In October, Labs, MosaicML, Okera, OpenAI's ChatGPT, Redash, Tabular. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

related to Funding · 25

Databricks → Alphabet Inc, Amazon Web Services, Andreessen Horowitz, August, Blackstone Inc, CapitalG, Coatue Management, December, February, Franklin Templeton, Google's MapReduce, In, In August, In December, In July, In September, Just, MGX, Microsoft, Other

related to Acquisitions · 17

Databricks → AI, Arcion, ChatGPT, ChatGPT-like, Dolly, German, In, In June, In March, In May, In October, Labs, MosaicML, Okera, OpenAI's ChatGPT, Redash, Tabular

related to 2013–2021 · 16

Databricks → Ali Ghodsi, AMPLab, Andy Konwinski, Apache Spark, Arsalan Tavakoli-Shiraji, Azure Databricks, Berkeley, California, Ion Stoica, Matei Zaharia, Microsoft Azure, Patrick Wendell, Reynold Xin, Scala, The, University

related to Products · 10

Databricks → AI, Apache Iceberg, Apache Spark, Delta Lake, Industry, The, This, To, Unlike, While

Products · 8

Databricks → Agent Bricks, Databricks Apps, Genie, Lakebase, Lakeflow, Lakehouse, Lakewatch, Unity Catalog

related to Corporate partnerships · 8

Databricks → Anthropic, Anthropic's AI, AWS, Buy, In December, In March, Wiz, Workday

Founders · 7

Databricks → Ali Ghodsi, Andy Konwinski, Arsalan Tavakoli, Ion Stoica, Matei Zaharia, Patrick Wendell, Reynold Xin

related to 2022–present · 5

Databricks → AI, Data Intelligence Platform, December, MosaicML, The

related to Revenue · 4

Databricks → As, CNBC, February, June

Founded · 1

Databricks → 2013; 13 years ago (2013)

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

data ai platform billion company open-source 2025 announced 2026 intelligence june funding agent revenue round cloud models architecture analytics microsoft

Databricks relationships Subject–Predicate–Object triples

TTTA extracted 108 structured relationships around Databricks. Examples in this analysis include Databricks → Founded → 2013; 13 years ago (2013) and Databricks → Founders → Ali Ghodsi. The table shows each extracted connection, where it came from and its confidence.

Subject	Predicate	Object	Confidence	Src
Databricks	Founded	2013; 13 years ago (2013)	1.00	infobox
Databricks	Founders	Ali Ghodsi	1.00	infobox
Databricks	Founders	Andy Konwinski	1.00	infobox
Databricks	Founders	Ion Stoica	1.00	infobox
Databricks	Founders	Patrick Wendell	1.00	infobox
Databricks	Founders	Reynold Xin	1.00	infobox
Databricks	Founders	Matei Zaharia	1.00	infobox
Databricks	Founders	Arsalan Tavakoli	1.00	infobox
Databricks	Headquarters	San Francisco, California, United States	1.00	infobox
Databricks	Industry	Computer software Artificial intelligence	1.00	infobox
Databricks	Net income	Free cash flow positive (2025)	1.00	infobox
Databricks	Number of employees	10,000 (2026)	1.00	infobox
Databricks	Products	Genie	1.00	infobox
Databricks	Products	Lakebase	1.00	infobox
Databricks	Products	Lakehouse	1.00	infobox
Databricks	Products	Lakeflow	1.00	infobox
Databricks	Products	Unity Catalog	1.00	infobox
Databricks	Products	Agent Bricks	1.00	infobox
Databricks	Products	Lakewatch	1.00	infobox
Databricks	Products	Databricks Apps	1.00	infobox
Databricks	Revenue	$6.9 billion (80% YoY) (ARR, Jun 2026)	1.00	infobox
Databricks	Type	Private	1.00	infobox
Databricks	Website	databricks.com	1.00	infobox

Related concept clusters Concept neighborhoods

The concept neighborhoods around Databricks bring nearby vocabulary together. In this analysis, examples include Ai, Platform and Billion. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

Databricks
- Ai
- Platform
- Billion
- Announced
- June
- Released
- Intelligence
- Funding
- Products
- Company
- Building
- Architecture
databricks
- Ai
- Platform
- Billion
- Announced
- June
- Released
- Intelligence
- Funding
- Products
- Company
- Building
- Architecture
apache spark
- Apache
- Spark
- California
- Open-source
- Delta
- Lake
- Lakebase
- Database
- Open
- Agent
- Intelligence
- June
data analytics
- Databricks
- Intelligence
- Platform
- Ai
- Genie
- Company
- Architecture
- Analytics
- Data
- Agent
- Delta
- Including
data lakehouse
- Databricks
- Platform
- Ai
- Intelligence
- Company
- Analytics
- Agent
- Architecture
- Delta
- Genie
- Including
- Lake
data warehouses
- Databricks
- Platform
- Ai
- Intelligence
- Company
- Analytics
- Agent
- Architecture
- Delta
- Genie
- Including
- Lake
data lakes
- Databricks
- Platform
- Ai
- Intelligence
- Company
- Analytics
- Agent
- Architecture
- Delta
- Genie
- Including
- Lake
unstructured data
- Databricks
- Platform
- Ai
- Intelligence
- Company
- Analytics
- Agent
- Architecture
- Delta
- Genie
- Including
- Lake

Connections between topic areas Semantic bridges

For Databricks, one of the stronger structural bridges in this analysis connects Databricks with Products. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Databricks — Products · splits 69 ⟂ 25

Databricks — Partnerships and acquisitions · splits 71 ⟂ 23

Databricks — History · splits 76 ⟂ 18

Databricks — Overview · splits 78 ⟂ 16

Databricks — Finance · splits 83 ⟂ 11

Map overview Semantic statistics

Databricks

Nodes94

Edges93

Triples108

Avg. degree1.98

Density0.021277

Components1

Source & methodology

TTTA analyzes the structure around Databricks to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Art, Companies & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Databricks · EN edition · Analysis: TopicsToTalkAbout