Dataweave vs Databricks

Databricks has a disclosed valuation of $134B, while Dataweave's valuation is not publicly available, making a direct size comparison difficult. Databricks employs 6,000 people.

Databricks has raised more in total funding at $20.2B, compared to Dataweave's $22M — a gap of $20.2B. Combined, the two companies have completed 8 known funding rounds.

Databricks leads with an Awaira Score of 93/100, while Dataweave sits at 55/100. That 38-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Dataweave was founded by Krishnamurthy Rengarajan in 2011. Databricks was founded by Ali Ghodsi in 2013. Visit each company's profile on Awaira for a full founder biography.

Dataweave: Dataweave is a retail price intelligence and competitive data platform that continuously monitors millions of product listings across e-commerce marketplaces, brand websites, and retail channels, using AI to deliver real-time pricing analytics, assortment insights, and promotion tracking to retailers and brand manufacturers. The platform helps merchandising and pricing teams make data-driven decisions to stay competitive on digital shelves.\n\nThe company raised approximately $22M in Series B funding and serves major global retailers, consumer goods companies, and marketplaces that need systematic visibility into competitor pricing and shelf positioning. Dataweave processes billions of data points daily and provides structured analytics that replace manual price monitoring spreadsheets.\n\nAs e-commerce has shifted pricing from periodic catalog decisions to continuous dynamic optimization, the need for automated competitive price intelligence has become a core retail technology investment. Dataweave's decade of accumulated retail data and processing infrastructure gives it a scale advantage over newer entrants in the competitive intelligence category. Databricks: Databricks is an AI and data platform founded in 2013 that provides a unified analytics workspace for data engineering, data science, and machine learning. The company developed Databricks Lakehouse, which combines data lake and data warehouse capabilities, built on Apache Spark technology. Its platform enables organizations to process large-scale data, build machine learning models, and deploy AI applications through a single interface. The company offers several core products: Databricks SQL for analytics, Databricks Machine Learning for model development, and Databricks Jobs for workflow automation. The platform supports multi-cloud deployment across AWS, Azure, and Google Cloud. Databricks serves enterprises across various industries, with customers including organizations in financial services, technology, and healthcare sectors. As of its latest funding round, Databricks has raised $11.2 billion in total funding and maintains a valuation of $134 billion, positioning it among the highest-valued private AI and data companies. The company achieved Series J funding status, indicating significant capital accumulation and investor confidence. Databricks competes with platforms like Snowflake, Teradata, and cloud-native data solutions from major hyperscalers. The company's growth trajectory reflects strong market demand for integrated data and AI infrastructure, driven by increasing enterprise adoption of machine learning and data-driven decision-making. Databricks unified the traditionally separate data warehouse and data lake approaches through its Lakehouse architecture, creating a single platform for analytics and AI workflows.

Dataweave got there first, launching in 2011 — that's 2 years of extra runway. Databricks didn't arrive until 2013. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Dataweave has about 100-500 employees; Databricks has about 6,000. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Dataweave and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.