Data pipeline observability built for Spark-heavy data engineering teams. Proactively prevent bad data and quickly root cause pipeline issues, automatically. On-prem or cloud.
Total raised: $16.5M
Funding Rounds 2
| Date | Series | Amount | Investors |
| 30.04.2026 | Series A | $12M | Hyde Park ... |
| 14.08.2024 | Seed | $4.5M | - |
Mentions in press and media 6
| Date | Title | Description |
| 02.05.2026 | Definity embeds agents inside Spark pipelines to catch failures before they reach agentic AI systems | For most data engineering teams, managing pipeline reliability often means waiting for an alert, manually tracing failures across distributed jobs and clusters, and fixing problems after they've already hit the business. Agentic AI needs th... |
| 30.04.2026 | Definity Raises $12 Million Series A To Advance Agentic Data Engineering Platform | Definity, an agentic data engineering platform purpose-built to operate and optimize enterprise lakehouse and Spark data pipelines, has raised an oversubscribed $12 million Series A financing, bringing its total funding to $16.5 million. Th... |
| 29.04.2026 | definity Raises $12M in Series A Funding | definity, a Chicago, IL-based provider of an agentic data engineering platform, raised $12M in Series A funding. The round was led by GreatPoint Ventures, with participation from Dynatrace and existing investors StageOne Ventures and Hyde P... |
| 14.08.2024 | definity Raises $4.5M in Seed Funding | definity, a Chicago, IL-based provider of a data application observability & remediation platform for Spark data analytics environments, raised $4.5M in Seed funding. The round was led by StageOne Ventures, with participation from Hyde ... |
| - | definity | “Observe, fix, and optimize Lakehouse & Spark pipelines, in-motion. Easily optimize cost, proactively prevent data incidents, and troubleshoot 10x faster - out-of-the-box.” |
| - | definity | “Observe, fix, and optimize Spark pipelines, in-motion. Proactively prevent data downtime and quickly root cause pipeline issues, automatically.” |