Trusted, high-quality data and efficient use of data engineers’ time are critical success factors for AI/ML projects. Enterprise data is complex—it comes from several sources, in a variety of formats, and at varied speeds. For your machine learning projects on Apache Spark, you need a holistic approach to data engineering: finding & discovering, ingesting & integrating, server-less processing at scale, and data governance. Stop by this session for an overview on how to set up AI/ML projects for success while Informatica takes the heavy lifting out of your data engineering.
In his 10 years at Informatica, Louis has been helping customers to take a modern approach to data engineering to support their goals for Next Generation Analytics. He currently leads the EMEA Data Engineering Technical team helping customers on their data lake, streaming, cataloging and other enterprise cloud data management initiatives. Louis has over 20 years experience in the software industry in consulting, presales, implementation and delivery roles, specializing in data integration, application integration and high performance messaging for financial services.