@Shihab The data nodes typically have the same disk size; however, this is not required nor necessarily a best practice. There will be no performance issues unless you have slow performing hardware (heterogeneous hardware). In that case when you launch a job it will run map task on any node which has capacity to run the task. It may select a node which is lower performing them others. If you have heterogeneous storage, that is fine with hortonworks since you pre-configure the storage policies based on the storage type. here is more info on that. This way you can avoid performance issues related to hetergenoous hardware. ... View more

@Wayne Vovil Are you running this on CentOS? If so try this (Found this on HCC post here): To use Ambari to manage (start/stop) the Zeppelin service, run the following commands on the node running Ambari server. For example, on CentOS 6.*: yum install -y git VERSION=`hdp-select status hadoop-client | sed 's/hadoop-client - \([0-9]\.[0-9]\).*/\1/'` sudo git clone https://github.com/hortonworks-gallery/ambari-zeppelin-service.git /var/lib/ambari-server/resources/stacks/HDP/$VERSION/services/ZEPPELIN sudo service ambari-server restart On a node (call it 'Node A') that is not running Ambari server, install the nss package: yum install -y nss Once Ambari is back up and you've install the nss package to "Node A", in Ambari, go Actions -> Add service -> check Zeppelin service -> Place the Zeppelin service on Node A in the assign masters step and click Next -> Next -> Next -> Deploy. The installation will start once you click Deploy Once complete, the Zeppelin Notebook service will be running. You can navigate to http://<FQDN of Node A>:9995 or follow the stepshere to create the Ambari view. ... View more

@Naresh Jella Standalone is useful when you do not want end users having access to the ambari service that is operating the cluster. this allow you to federate the activities by use case (querying data vs cluster administion) Views can be deployed and managed in one or more separate “standalone” Ambari Servers. Running “standalone” Ambari Server instances is useful when users who will access views will not have (and should not) have access to that Ambari Server that is operating the cluster. As well, you can run one or more separate Ambari Server instances “standalone” for a scale-out approach to handling a large number of users. I recommend both operational and standalone ambari be in same versions. ... View more

@suresh krish please take a look at our docs here to enable spark thrift server. ... View more