DataPelago unveiled a Universal Data Processing Engine designed to accelerate any engine, including open-source ones, on any hardware and for any data type to support GenAI and analytics workloads. The engine is offered as an end-to-end solution or can integrate with Substrait-based open source frameworks to turbocharge Spark and Trino, delivering disruptive price/performance without changing applications or workflows. It integrates with existing data stores and lakehouse platforms to avoid data migration and vendor lock-in. Architecturally the engine comprises three layers—DataVM (a domain-specific ISA for data operators), DataOS (maps data operations to heterogeneous accelerated computing elements), and DataApp (a pluggable integration layer)—which together claim one to two orders of magnitude performance gains versus today’s query engines. Targeted use cases include analyzing billions of transactions while maintaining data freshness, AI-driven threat detection at wire-line speeds, and scalable deployment of training, fine-tuning, and RAG inference pipelines. Co-founder and CEO Rajan Goyal, who has 20+ years building accelerated computing solutions, has assembled a multidisciplinary team to develop the technology and bring it to market; the company launched from stealth with $47 million in funding.

Total raised

$47M

Funding rounds

1

Latest round

Equity

Latest activity

Oct 2024