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		09-17-2015
	
	
	
	
	
	
	
	
	
	
	
	
	
	
			
      
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		11-06-2015
	
		
		03:55 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 @Andrew Grande thanks updated.   @Jonas Straub no haven't tested it on larger datasets yet - almost afraid to 😉 
						
					
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		11-06-2015
	
		
		04:47 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
	
		14 Kudos
		
	
				
		
	
		
					
							 There are new Data Visualization, Data Explorer tabs that come part of Hive view which are really nice. I found this by accident so thought there may be others who are probably not aware of this either.  Environment details: Should work in vanilla cluster/sandbox as well but in my case the env is as below (setup using steps here😞  
 Kerborized HDP 2.3.2 w/ Ranger installed   Secure Ambari 2.1.2 (authenticating to users in IPA LDAP)  The sample salary data from sandbox has also been imported and was used for visualization   1. After logging to Ambari 2.1.2, open the Hive view (in this case I had to create a new instance of the view configured for kerberos).         2. Click the Data Visualization tab on the right and drag/drop description and salary fields onto x, y fields      3. Select from the various chart options to change the chart       4. Click transpose button:      5. Navigate to Data Explorer tab to explore your data using the different fields      So all in all: visualizations look good, are responsive (at least on this dataset), and seem to work without issue on kerborized cluster! 
						
					
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		11-06-2015
	
		
		02:56 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
	
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							 Just got off webex with @rgarcia@hortonworks.com  We were able to remove the failed Ranger/Metrics installation using something like the below:  su postgres
psql
\c ambari
DELETE FROM ambari.hostcomponentstate WHERE service_name IN ('RANGER');
DELETE FROM ambari.hostcomponentdesiredstate WHERE service_name IN ('RANGER');
DELETE FROM ambari.servicecomponentdesiredstate WHERE service_name IN ('RANGER');
DELETE FROM ambari.servicedesiredstate WHERE service_name IN ('RANGER');
DELETE FROM ambari.clusterservices WHERE service_name IN ('RANGER');
  Then make sure to restart Ambari  service ambari-server restart  Now re-install Ranger/Ambair metrics 
						
					
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		11-05-2015
	
		
		06:32 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
	
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							 @Sean Roberts had scripted this out. Steps here: https://github.com/seanorama/masterclass/tree/master/security#lab-configure-ambari-for-ldap  Here is the script itself  https://github.com/seanorama/ambari-bootstrap/blob/master/extras/ambari-ldap-ad.sh 
						
					
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		11-05-2015
	
		
		06:13 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
	
		1 Kudo
		
	
				
		
	
		
					
							 Actually the PR link in the jira above has the a lot more details. Sounds they have got it mostly working 
						
					
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		11-05-2015
	
		
		05:25 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 There might be more needed here. See this PR for JDBC interpreter for MySQL: https://github.com/apache/incubator-zeppelin/pull/60 
						
					
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		11-05-2015
	
		
		04:56 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
	
		1 Kudo
		
	
				
		
	
		
					
							 Can you run the yum install manually and see what happens?  yum install ambari-metrics-collector
 
						
					
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		11-05-2015
	
		
		03:45 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 First time you ran a cell containing spark, Zeppelin will bring up Spark AM on YARN (not every time you create new notebook). The zeppelin cell which declares the %dep should be run before the Spark AM comes up. If needed you can stop existing Spark app master by restarting the Spark interpreter via Interpreter tab in Zeppelin UI. 
						
					
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		11-05-2015
	
		
		03:25 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
	
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							 Its probably possible (because Nifi is REST API based) but in general, the recommendation is to create a template from existing flow ahead of time and importing into Nifi on your desired cluster (that would be easier to automate than automating the building of the whole flow) 
						
					
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