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 Furthermore, the tables across interpreter processes currently can’t be shared. For example, table from JDBC interpreter wouldn’t be accessible from SparkSQL or Python interpreters. So the idea here is to extend Zeppelin ResourcePool to share Table resources across interpreters. It would allow also to have one central Table menu to access and view table information of registered Table resources.

 Thus the critical question is “How Zeppelin can support large data handling and share across interpreters?”.
Here are already resolved issues and they can be clues to solving the problem. 

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