Autoscale for Azure HDInsight is now generally available across all regions for Apache Spark and Hadoop workloads. This feature makes it possible to operate your big data analytics workloads in a more cost-efficient and productive way, so you can drive higher use of your HDInsight clusters and pay only for what you need.
Depending on your requirements, you can choose between load-based and schedule-based autoscaling. Load-based Autoscale can scale the cluster size up and down based on the current resource needs while schedule-based Autoscale can change the cluster size based on the predefined schedule.
Autoscale support for HBase and LLAP worload is also public preview. Read more details.