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As of January 31, 2021, this tutorial references legacy products that no longer represent Cloudera’s current product offerings.

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You will use Zeppelin's JDBC Hive Interpreter to perform SQL queries against the noSQL HBase table "tweets_sentiment" for the sum of happy and sad tweets and perform visualizations of the results.


  • Enabled Connected Data Architecture
  • Setup the Development Environment
  • Acquired Twitter Data
  • Cleaned Raw Twitter Data
  • Built a Sentiment Classification Model
  • Deployed a Sentiment Classification Model


Implement a Zeppelin Notebook to Visualize Sentiment Scores

Create Hive Table Mapping to HBase Table

To visualize the data stored in HBase, you can use zeppelin's JDBC Hive Interpreter:

CREATE EXTERNAL TABLE IF NOT EXISTS tweets_sentiment(`key` BIGINT, `handle` STRING, `language` STRING, `location` STRING, `sentiment` DOUBLE, `tweet_id` BIGINT)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES("hbase.columns.mapping" = ":key,social_media_sentiment:twitter.handle,social_media_sentiment:twitter.language,social_media_sentiment:twitter.location,social_media_sentiment:twitter.sentiment,social_media_sentiment:twitter.tweet_id")
TBLPROPERTIES("hbase.table.name" = "tweets_sentiment");


Load a Sample of the Data

Load data from the Hive table:

SELECT * FROM tweets_sentiment;


Visualize Sentiment Score Per Language in Bar Chart

To see each tweet's sentiment score per language, copy and paste the following query.

SELECT language, sentiment FROM tweets_sentiment;



Congratulations! You just learned to write Hive code to access an HBase table, query against the table and visualize the data using Zeppelin's JDBC Hive Interpreter.

Further Reading

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