McGraw-Hill Optimizes Analytics Workloads with AWS and Databricks
Performing data science workloads on data from disparate sources – data lake, data warehouse, streaming, and more – creates challenges for organizations needing to use their data to drive operational and product improvements. Textbook publisher McGraw-Hill needed to remove such data silos so it could transform its business model to accommodate a growing focus on digital learning. Specifically, the company wanted the ability to quickly perform complex analytics operations and enable collaboration between business analysts, data engineers, and data scientists.
McGraw-Hill deployed Databricks, a unified analytics platform that allows it to work efficiently with streaming data as well as historical data stored in data lakes on Amazon S3 and in multiple data warehouses. In this webinar, you’ll learn how Databricks, developed by the original creators of Apache Spark™, enables McGraw-Hill to analyze streaming and historical data at a scale and speed their previous solution simply couldn’t provide. Data science workloads that used to take weeks, now take hours.
McGraw Hill - Matthew Ashbourne, Lead Software Engineer, McGraw-Hill Education
Databricks - Brian Dirking, Sr Director of Partner Marketing
AWS - Pratap Ramamurthy, Partner Solutions Architect