y42 vs Databricks
Side-by-side comparison of AI visibility scores, market position, and capabilities
Berlin Germany full-stack data platform; raised $31M+; combines ELT pipeline, dbt-based transformation, and BI in a single no-code/low-code environment.
About
y42 is a full-stack data platform founded in 2020 and headquartered in Berlin, Germany. The company was founded by Hung Dang and Fabian Schuh to build a unified platform that covers the entire modern data stack — ELT data ingestion, dbt-based SQL transformation, and business intelligence visualization — in a single integrated product. y42's thesis is that the fragmentation of the modern data stack, while enabling best-of-breed component selection, also creates significant operational overhead from maintaining multiple tools with separate authentication, monitoring, and support relationships. y42 integrates these layers into a single, cloud-hosted environment.\n\ny42 raised $31 million in funding from investors including Sequoia Capital, La Famiglia, and Creandum. The platform's ELT component provides pre-built connectors to more than 200 data sources, with the data delivered directly into the customer's own cloud data warehouse — Snowflake, BigQuery, or Redshift — ensuring data ownership and compliance. The transformation layer is powered by dbt under the hood, allowing analytics engineers familiar with dbt to work in their existing paradigm while benefiting from y42's visual interface and managed execution. The BI layer provides a drag-and-drop dashboard builder that connects to the transformed data models in the warehouse.\n\ny42 is particularly popular in the European market among data teams at growing technology companies and scale-ups that want the full modern data stack without the complexity of managing and integrating three or four separate tools. Its single-vendor support model and GDPR-compliant European data infrastructure make it a strong fit for EU-based organizations with compliance requirements.
$4.8B revenue run-rate; 55% YoY growth; $134B valuation (Series L). Mosaic AI for enterprise LLM fine-tuning and inference; Unity Catalog for data governance. DBRX open-source model; every major enterprise AI deployment runs on the lakehouse.
About
Databricks was founded in 2013 by the original creators of Apache Spark — Ali Ghodsi, Matei Zaharia, and five other UC Berkeley researchers — to unify data engineering, analytics, and machine learning on a single platform. The company commercialized the lakehouse architecture, combining the flexibility of data lakes with the reliability of data warehouses. Databricks runs on AWS, Azure, and GCP and leads the commercial distribution of the open-source Delta Lake and MLflow projects.\n\nThe platform includes the Databricks Lakehouse for unified data processing, Unity Catalog for governance and lineage tracking, and Mosaic AI for enterprise LLM fine-tuning, model serving, and generative AI application development. It supports data engineering, SQL analytics, BI, feature engineering, and model training within a single governance perimeter, serving enterprises in financial services, healthcare, manufacturing, and media.\n\nDatabricks achieved a $4.8 billion annualized revenue run-rate in early 2025 with 55% year-over-year growth and a $62 billion valuation from its Series L round — one of the most valuable private software companies globally. Its dual role as the leading commercial lakehouse vendor and steward of influential open-source projects gives it a unique ecosystem advantage as enterprises accelerate investment in AI infrastructure.
AI Visibility Head-to-Head
Key Details
Capabilities & Ecosystem
Also Compare
Track AI Visibility in Real Time
Monitor how your brand performs across ChatGPT, Gemini, Perplexity, Claude, and Grok daily.