The Pepperdata team is ready to meet you this week at Booth #501 at the Spark+AI Summit at Moscone Center in San Francisco!
We’re excited to be participating in this week’s Spark+AI Summit in San Francisco. This is the largest data and machine learning conference in the world, and this year’s event promises to provide a unique opportunity for developers, data engineers, data scientists, and decision-makers to collaborate at the intersection of data and ML. Attendees can learn about the latest advances in Apache Spark and ML technologies like TensorFlow, MLflow, PyTorch as well as real-world enterprise AI best practices. At the Pepperdata booth (#501), we’ll be showcasing some of our latest advances in big data performance management.
Check out our free Big Data Cloud Migration Cost Assessment
It automatically identifies optimal cloud instances across cloud service providers, based on actual workloads, to support the most cost-effective implementation of a hybrid or multi-cloud migration strategy. The Pepperdata Big Data Cloud Migration Cost Assessment identifies optimal instances across four leading cloud service providers: AWS, Azure, Google Cloud Platform and IBM Cloud.
Pepperdata automatically provides optimal cloud instance recommendations based on the performance profiles, including CPU and memory usage, captured from your actual on-premises workloads and infrastructure, saving you significant time and expense. The assessment includes exactly what you need to decide on the most cost-effective implementation of your hybrid or multi-cloud strategy.
Attend a tutorial on Spark management led by one of our experts
On Wednesday, April 24, at 3:40 PM, our big data performance management expert Kirk Lewis will deliver an informative session titled Managing Spark in Multi-Tenant Clusters Using Big Data Analytics. Enterprises concentrate on leveraging big data for valuable insights, but they don’t often do the same thing when it comes to analyzing their own systems. To diagnose an issue, operators are left to look in cumbersome log files which are extremely difficult to work with and don’t include all the right data. This session will describe a metrics platform that continuously collects operational data over time (near real-time data stored for days, weeks and months) to quickly provide visibility into the big data system and drill down for Spark problem diagnosis, tuning and chargeback reporting.