Legacy enterprise data warehouse (EDW) architecture, geared toward day-to-day workloads associated with operational querying, reporting, and analytics, are often ill-equipped to handle the volume of data, traffic, and varied data types associated with a modern, ad-hoc analytics platform. Faced with challenges of increasing pipeline speed, aggregation, and visualization in a simplified, self-service fashion, organizations are increasingly turning to some combination of Spark, Hadoop, Kafka, and proven analytical databases like Vertica as key enabling technologies to optimize their EDW architecture. Join us to learn how successful organizations have developed real-time streaming solutions with these technologies for range of use cases, including IOT predictive maintenance.
Myles Collins has over 3 decades of database/data warehouse experience specializing in large datasets, the last 5 of these in the Vertica group at Hewlett Packard Enterprise. He has been a data processing consultant and created systems for eclectic organizations including Harvard, Fidelity, MA Department of Revenue, Lycos, and EOHHS.