Prophecy

Name: Prophecy
Brand: Prophecy

AI data prep and analysis: transform data into insights with AI pipelines.

Est 2017|Valuation $278M|Raised $116.2M

LinkedIn 33K|Web traffic 5K|FTEs 171|HQ Palo Alto, California, US

MarketsData Transformation Data Preparation

01Competitive Landscape

Narrative analysis

Prophecy vs dbt Cloud - Overlap: both are transformation-centric and emphasize software engineering practices. - Differences: Prophecy emphasizes Spark and Databricks workflows and a visual+code paradigm; dbt is a code-first SQL transformation framework with a broad community and warehouse focus. - Buyers often select dbt when they prefer an industry-standard, SQL-first transformation framework. Organizations may consider Prophecy when Spark pipelines, multi-step ETL, and a visual collaboration layer are important. Prophecy vs Matillion - Both target visual pipeline development. Matillion emphasizes visual ELT with a consumption pricing model and broad connector coverage. Prophecy emphasizes two-way visual/code workflows and generated Spark/SQL code. Prophecy vs platform-native Databricks tooling - A platform-native approach (notebooks, Workflows, Delta Live Tables) can reduce vendor count and simplify procurement and security, but may not provide the same visual collaboration layer.

Market adoption and trends

Prophecy is venture-backed with multiple financing rounds and has received press coverage. Growth and adoption figures reported in press items are vendor-published and were not independently audited in the sources reviewed.

	Prophecy	dbt Cloud	Matillion
Starting Price	Entry enterprise tier: thousands/month (public); Enterprise = contact vendor	Developer: free; Starter: low-hundreds per user/month; Enterprise = custom	Pricing is consumption/credits-based; public page does not list a single entry price in the excerpt reviewed
Typical fit	Databricks/Spark-focused organizations wanting a visual+code workflow with Git-based lifecycle	SQL-first analytics engineering teams standardizing transformations as code	Cloud ELT/ETL teams wanting visual pipelines and broad cloud integrations
Key Strength	Two-way visual↔code workflow; generated Spark/SQL output; packaging/plugins	Large community and ecosystem; code-first SQL modeling	Visual ELT focus and enterprise packaging; consumption model
Key Weakness	Review platform confusion; enterprise pricing opacity; may be less suitable for small SQL-only teams	Less native for Spark-heavy ETL; seat-based costs; code-first model requires engineering discipline	Consumption-based pricing can be harder to forecast; may be heavier than dbt for pure SQL modeling

01Competitive Landscape

Narrative analysis

Prophecy vs dbt Cloud

Overlap: both are transformation-centric and emphasize software engineering practices.
Differences: Prophecy emphasizes Spark and Databricks workflows and a visual+code paradigm; dbt is a code-first SQL transformation framework with a broad community and warehouse focus.
Buyers often select dbt when they prefer an industry-standard, SQL-first transformation framework. Organizations may consider Prophecy when Spark pipelines, multi-step ETL, and a visual collaboration layer are important.

Prophecy vs Matillion

Both target visual pipeline development. Matillion emphasizes visual ELT with a consumption pricing model and broad connector coverage. Prophecy emphasizes two-way visual/code workflows and generated Spark/SQL code.

Prophecy vs platform-native Databricks tooling

A platform-native approach (notebooks, Workflows, Delta Live Tables) can reduce vendor count and simplify procurement and security, but may not provide the same visual collaboration layer.

Market adoption and trends

Prophecy is venture-backed with multiple financing rounds and has received press coverage. Growth and adoption figures reported in press items are vendor-published and were not independently audited in the sources reviewed.

	Prophecy	dbt Cloud	Matillion
Starting Price	Entry enterprise tier: thousands/month (public); Enterprise = contact vendor	Developer: free; Starter: low-hundreds per user/month; Enterprise = custom	Pricing is consumption/credits-based; public page does not list a single entry price in the excerpt reviewed
Typical fit	Databricks/Spark-focused organizations wanting a visual+code workflow with Git-based lifecycle	SQL-first analytics engineering teams standardizing transformations as code	Cloud ELT/ETL teams wanting visual pipelines and broad cloud integrations
Key Strength	Two-way visual↔code workflow; generated Spark/SQL output; packaging/plugins	Large community and ecosystem; code-first SQL modeling	Visual ELT focus and enterprise packaging; consumption model
Key Weakness	Review platform confusion; enterprise pricing opacity; may be less suitable for small SQL-only teams	Less native for Spark-heavy ETL; seat-based costs; code-first model requires engineering discipline	Consumption-based pricing can be harder to forecast; may be heavier than dbt for pure SQL modeling