As of January 31, 2021, this tutorial references legacy products that no longer represent Cloudera’s current product offerings.
Please visit recommended tutorials:
- How to Create a CDP Private Cloud Base Development Cluster
- All Cloudera Data Platform (CDP) related tutorials
Your next objective is to act as a Data Analyst and use Apache Zeppelin built-in visualization tools to provide insights on your data, such as illustrating which countries have the most extreme temperature and the amount of NORMAL events there are compared to HOT and COLD. You will also illustrate which HVAC units result in the most
- Enabled CDA for your appropriate system
- Set up the Development Environment
- Acquired HVAC Sensor Data
- Cleaned Raw HVAC Data
- Visualize HVAC Building Temperature Characteristics Per Country
- Visualize Extreme Temperature in Buildings Having HVAC Products
Click on Zeppelin Notebook service in Ambari stack, in the box on the rightside called Quick Links, click on Zeppelin UI.
Click Create new note.
Insert Note Name as
jdbc for default interpreter, then click Create.
We will use the JDBC Hive interpreter to run Hive queries and visualize the results.
First we need to select the columns from
hvac_sensors.hvac_building table that will illustrate hvac_building country location, whether temperature in a hvac_building is extreme and what the temprange level is for an hvac_building. Copy and paste the Hive query:
%jdbc(hive) SELECT country, extremetemp, temprange FROM hvac_sensors.hvac_building LIMIT 1000;
Select the bar chart button located just under the query to change the table visualization to bar chart.
Let's further configure this chart, click settings to open up more available fields.
- Arrange the fields according to the following image.
- Drag the field
temprangeinto the groups box.
- Click SUM on
extremetempand change it to COUNT.
- Make sure that
countryis the only field under Keys.
From the chart above we can see the countries that HVAC Buildings come from that have the most extreme temperature indicated by a count for extremetemp associated with temprange level for NORMAL events there are compared to HOT and COLD.
Is it possible to figure out which buildings might need HVAC upgrades, and which do not? Let’s determine that answer in the steps ahead...
Let's create one more note to visualize which types of HVAC systems result in the least amount of
Copy and paste the Hive query into the next Zeppelin note:
%jdbc(hive) select hvacproduct, extremetemp from hvac_sensors.hvac_building;
Arrange the fields according to the following image so we can recreate the chart below.
Now choose bar chart, then arrange the fields for
- Make sure that
hvacproductis in the Keys box.
- Make sure that
extremetempis in the Values box and that it is set to COUNT.
Now we can see which HVAC units result in the most
extremetemp readings. Thus we can make a more informed decision when purchasing new HVAC systems.
We’ve successfully gained more insights on our data by visualizing certain key attributes using Apache Zeppelin. We learned to visualize a graph that shows the countries that have the most extreme temperature and the amount of NORMAL events there are compared to HOT and COLD. We learned to see which HVAC units result in the most
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