Learn how to monitor performance and resource utilization on Azure HDInsight by keeping tabs on metrics, such as CPU, memory, and network usage, to better understand how your cluster is handling your workloads and whether you have enough resources to complete the task at hand.
Apache Kafka is one of the most popular open source streaming platforms today. However, deploying and running Kafka remains a challenge for most.
We are excited to announce the preview of the autoscale feature for Azure HDInsight. This feature enables enterprises to become more productive and cost-efficient by automatically scaling clusters up or down based on the load or a customized schedule.
Migrating big data workloads to the cloud remains a key priority for our customers and Azure HDInsight is committed to making that journey simple and cost effective. HDInsight partners with Unravel whose mission is to reduce the complexity of delivering reliable application performance when migrating data from on-premises or a different cloud platform onto HDInsight.
The Jupyter Notebook on HDInsight Spark clusters is useful when you need to quickly explore data sets, perform trend analysis, or try different machine learning models. Not being able to track the status of Spark jobs and intermediate data can make it difficult for data scientists to monitor and optimize what they are doing inside the Jupyter Notebook.
We are pleased to announce the general availability of the new Azure HDInsight management SDKs for .NET, Python, and Java.
Today we’re announcing the general availability of Apache Hadoop 3.0 on Azure HDInsight. In partnership with Cloudera, Microsoft Azure is the first cloud provider to offer customers the benefit of the latest innovations in the most popular open source analytics projects, with unmatched scalability, flexibility, and security.
As a high-availability service, Azure HDInsight ensures that you can spend time focused on your workloads, not worrying about the availability of your cluster.
Azure HDInsight offers several ways to monitor your Hadoop, Spark or Kafka clusters. They can be broken down into three main categories: cluster health and availability, resource utilization and performance, and job status and logs.
n the current era, companies generate huge volumes of data every second. Whether it be for business intelligence, user analytics, or operational intelligence; ingestion, and analysis of streaming data requires moving this data from its sources to the multiple consumers that are interested in it.