Select Page
An example of the job graph showing stages 0 through 3 for a spark job.

Monitoring on Azure HDInsight part 4: Workload metrics and logs

This is the fourth blog post in a four-part series on monitoring on Azure HDInsight. Monitoring on Azure HDInsight Part 1: An Overview discusses the three main monitoring categories: cluster health and availability, resource utilization and performance, and job status ... continue reading
ambari_dashboard

Monitoring on Azure HDInsight Part 3: Performance and resource utilization

This is the third blog post in a four-part series on Monitoring on Azure HDInsight. Part 1 is an overview that discusses the three main monitoring categories: cluster health and availability, resource utilization and performance, and job status and logs ... continue reading

Manage Azure HDInsight clusters using .NET, Python, or Java

We are pleased to announce the general availability of the new Azure HDInsight management SDKs for .NET, Python, and Java. Highlights of this release More languages: In addition to .NET, you can now easily manage your HDInsight clusters using Python ... continue reading
ambari_dashboard

Monitoring on Azure HDInsight Part 2: Cluster health and availability

This is the second blog post in a four-part series on Monitoring on Azure HDInsight. "Monitoring on Azure HDInsight Part 1: An Overview" discusses the three main monitoring categories: cluster health and availability, resource utilization and performance, and job status ... continue reading
ambari_components

Monitoring on Azure HDInsight Part 1: An Overview

Azure HDInsight offers several ways to monitor your Hadoop, Spark, or Kafka clusters. Monitoring on HDInsight can be broken down into three main categories: Cluster health and availability Resource utilization and performance Job status and logs Two main monitoring tools ... continue reading