Chris Santiago, Director of Solutions Engineering, Unravel
Apache Spark is the leading technology for big data processing, on-premises and in the cloud. Spark powers advanced analytics, AI, machine learning, and more. Spark provides a unified infrastructure for all kinds of professionals to work together to achieve outstanding results.
However, as Spark’s importance grows, so does the importance of Spark reliability - and troubleshooting Spark problems is hard. Needed information is scattered across multiple, voluminous log files, which need expert analysis to understand. And these log files disappear when a cluster is removed - or, after a few weeks or months, organizations reclaim precious storage space by deleting them.
Would your organization benefit from rapid troubleshooting for your Spark workloads? If you’re running significant workloads on Spark, then you may be looking for ways to find and fix problems faster and better - and to find new approaches that steadily reduce your problems over time.
Join Chris Santiago, Director of Solutions Engineering at Unravel Data and see how Unravel can deliver:
- Enhanced observability through the use of additional sensors, plus intelligent curation and presentation of existing log and other data
- End-to-end monitoring, measurement, and troubleshooting of apps using Spark, Hadoop, Kafka, and related technologies.
- AI-powered recommendations and automated actions to enable pre-emptive fixes of problems with your big data pipelines and applications, before they happen.
- Detailed insights, plain language recommendations, and auto-tuning of apps to make the most of your Spark environment.
Don’t wait. Register today for this informative and actionable webinar.