Apache Spark
2 use cases using this technology
Figma trains AI Search models in 5 months using Amazon SageMaker AI
Figma
Design platform Figma built its AI infrastructure on Amazon SageMaker AI, using Amazon EMR with Apache Spark to process billions of design elements and the custom FigmaStep framework on SageMaker Pipelines to orchestrate training. Figma trained and deployed the models behind its AI Search feature within five months, launching it at Config 2024, and ran more than 10,000 SageMaker AI training jobs in 2025 to support features including the AI prompt-to-app tool Figma Make.
LSEG unifies 30 data systems, reducing product development time with Fabric
LSEG (London Stock Exchange Group)
LSEG, a trusted data partner to 44,000 customers in more than 170 countries, partnered with Microsoft to build a unified data platform on Microsoft Fabric, consolidating 30 systems, 1,200 datasets and 33 petabytes of data. Using Apache Spark on Fabric, Apache Airflow in Fabric, Microsoft Purview and OneLake, LSEG reduced product development timelines for new data products from years to months; onboarding data and creating a new product now happens in months, accelerating launches such as its ESG and Fundamentals data products. LSEG processes around 80,000 files daily on Spark on Fabric, consuming approximately 280,000 capacity units per day, with month-on-month consumption growing more than 50%. The company is also building AI-readiness by hosting Model Context Protocol (MCP)-powered data in OneLake and enabling financial professionals to build custom AI agents via Copilot Studio.