Nextdata

Euno, founded in 2023 in Israel, helps data teams at large organizations manage data governance.

Founded in July 2023 and headquartered in London, UK, Orchestra was co-founded by Hugo Lu (CEO) and Will Davies (CTO). It started as a data pipeline orchestration and observability tool for lean data teams, offering 100+ integrations and a serverless orchestration engine, and in 2026 expanded into an "Agentic Control Plane" unifying data pipelines with governed AI agent runtimes. Disclosed customers include Experian, Chobani, the San Francisco Giants, Trust & Will, Graniterock and Powell Industries.

Cloudera is a Santa Clara-based enterprise data lakehouse and hybrid data platform company founded in 2008; it IPO'd in 2017, merged with Hortonworks in 2019 and was taken private by KKR and CD&R for about $5.3B in 2021, and now focuses on private AI and data management across hybrid clouds.

Ekai, founded in 2024 and headquartered in Cambridge, Massachusetts, builds a semantic-modeling platform that gives enterprise AI systems verified business context. Domain experts define business meaning through structured interviews, which Ekai verifies against the company's own data-warehouse records before publishing governed, AI-ready semantic models and production dbt pipelines with accountable authorship. The platform integrates natively with Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, Postgres, ClickHouse and DuckDB without moving customer data outside the client's environment. Ekai says semantic-modeling work that traditionally takes three to six months can be completed in as little as six hours; it is SOC 2 Type II certified and a Snowflake Startup Accelerator participant.

DQLabs builds Prizm, an AI-native enterprise platform that unifies data observability, data quality, and data context into one continuously validated system for large organizations.

Upriver, founded in 2024 in San Francisco, USA, deploys AI agents that connect to enterprise data systems to automatically discover and fix data quality issues, maintain data pipelines, and create new datasets, targeting enterprise data teams to address the high labor cost of data quality maintenance.