How To Analyze Machine and Sensor Data
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Tutorial 14: Analyzing Machine and Sensor Data
This tutorial is for HDP version 2.3 of the Hortonworks Sandbox – a single-node Hadoop cluster running in a virtual machine. Download the Hortonworks Sandbox to run this and other tutorials in the series.
This tutorial describes how to refine data from heating, ventilation,
and air conditioning (HVAC) systems using the Hortonworks Data Platform,
and how to analyze the refined sensor data to maintain optimal building
Demo: Here is the video of Enable Predictive Analytics with
Hadoop as a demo of what
you’ll be doing in this tutorial.
A sensor is a device that measures a physical quantity and transforms it
into a digital signal. Sensors are always on, capturing data at a low
cost, and powering the “Internet of Things.”
Potential Uses of Sensor Data
Sensors can be used to collect data from many sources, such as:
- To monitor machines or infrastructure such as ventilation equipment,
bridges, energy meters, or airplane engines. This data can be used
for predictive analytics, to repair or replace these items before
- To monitor natural phenomena such as meteorological patterns,
underground pressure during oil extraction, or patient vital
statistics during recovery from a medical procedure.
In this tutorial, we will focus on sensor data from building operations.
Specifically, we will refine and analyze the data from Heating,
Ventilation, Air Conditioning (HVAC) systems in 20 large buildings
around the world.
- Hortonworks Sandbox (installed and running)
- Hortonworks ODBC driver installed and configured (if using Microsoft Excel for reporting analysis)
- Tutorial 7: Installing and Configuring the Hortonworks ODBC driver
on Windows 7
- Tutorial 11: Installing and Configuring the Hortonworks ODBC driver
on Mac OS X
- Microsoft Excel 2013 Professional Plus (optional)
- In this tutorial, the Hortonworks Sandbox is installed on an Oracle
VirtualBox virtual machine (VM) – your screens may be different.
- If you plan on using the Microsoft Excel for the analysis and reporting section install the ODBC driver that matches the version of Excel you are
using (32-bit or 64-bit).
- If choosing to use Excelm you will use the Power View feature in Microsoft
Excel 2013 to visualize the sensor data. Power View is currently
only available in Microsoft Office Professional Plus and Microsoft
Office 365 Professional Plus.
- Note, other versions of Excel will work, but the visualizations will
be limited to charts. You can connect to any other visualization
tool you like.
- If not using Excel, you will be able to use Apache Zeppelin to analyze and report on the data from this tutorial.
To refine and analyze HVAC sensor data, we will:
- Download and extract the sensor data files.
- Load the sensor data into the Hortonworks Sandbox.
- Run two Hive scripts to refine the sensor data.
- Access the refined sensor data with Microsoft Excel or Apache Zeppelin.
- Visualize the sensor data using Excel Power View or Apache Zeppelin.
Step 1: Download and Extract the Sensor Data Files
You can download the sample sensor data contained in a compressed
(.zip) folder here:
Save the SensorFiles.zip file to your computer, then extract the
files. You should see a SensorFiles folder that contains the
HVAC.csv – contains the targeted building temperatures, along with
the actual (measured) building temperatures. The building
temperature data was obtained using Apache Flume. Flume can be used
as a log aggregator, collecting log data from many diverse sources
and moving it to a centralized data store. In this case, Flume was
used to capture the sensor log data, which we can now load into the
Hadoop Distributed File System (HFDS). For more details on Flume,
refer to Tutorial 13: Refining and Visualizing Sentiment Data
building.csv – contains the “building” database table. Apache Sqoop
can be used to transfer this type of data from a structured database
Step 2: Load the Sensor Data into the Hortonworks Sandbox
- Navigate to the ambari login by going to the web address
- Login with the username
Once logged in to the sandbox, navigate to the icon at the top right of the header and view the dropdown menu.
- Note the view you are greeted with once you open the
HDFS FilesAmbari View.
- You can view the path that you’re currently in
- You can create new directories and upload files using the button on the top.
You can also download whole directories as
.zipfiles, move directories, and delete them as well. These options are highlighted in blue for each folder or file in a directory.
- Navigate to the
- Create the directory
databy using New Directory at the top of the page.
- Once inside the directory, click Upload then Browse. Navigate to the
HVAC.csvfile that is part of the .zip file that was downloaded earlier.
- Click Upload to upload the CSV file to HDFS.
Follow the same procedure to upload the
After uploading both files, the page should look similar to the following image:
Now user the menu on the header again to access the dropdown menu containing the link to the
Ambari Hive View
- Here we’re going to define two tables that will contain data from our
- Copy and paste the following query into the worksheet and click Execute to create the table hvac_stage.
create table hvac_stage (
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
- After the previous query has executed, Execute this next next query to create the table buildings_stage.
create table buildings_stage
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
- After both queries have succeeded you should see
buildings_stagein the Database Explorer on the left hand side of the screen.
- Now use the following query to load our tables with data from our data files that we previously uploaded to HDFS.
LOAD DATA INPATH '/tmp/data/HVAC.csv' OVERWRITE INTO TABLE HVAC_stage;
- Repeat the previous step with the following query to load the table
LOAD DATA INPATH '/tmp/data/building.csv' OVERWRITE INTO TABLE buildings_stage;
- Now refresh the Database Explorer. You should see all of the tables appear on the left hand side after clicking
- If you click the small square icon to the right hand side of any table it will immediately create a query that gives you a small sample of data from the table you selected.
- Do this for the table
buildings_stage. Make sure both tables have all columns populated and that it is possible to execute and see the results of both queries.
Lastly, we want to remove the header rows that are present when we query the tables in Hive. To do this we simply need to write to commands. One for each table.
ALTER TABLE buildings_stage SET TBLPROPERTIES ("skip.header.line.count"="1");
ALTER TABLE hvac_stage SET TBLPROPERTIES ("skip.header.line.count"="1");
- Now that we have both tables loaded in, we want to get better performance in Hive, so we’re going to create new tables that utilize the highly efficient ORC file format. This will allow for faster queries when our datasets are much much larger.
- Execute the following query to create a new table
hvacthat is stored as an ORC file.
CREATE TABLE hvac STORED AS ORC AS SELECT * FROM HVAC_stage;
- Repeat the previous step, except this time we will make a table for
CREATE TABLE buildings STORED AS ORC AS SELECT * FROM buildings_stage;
Step 3: Run Two Hive Scripts to Refine the Sensor Data
We will now use two Hive scripts to refine the sensor data. We hope to
accomplish three goals with this data:
- Reduce heating and cooling expenses.
- Keep indoor temperatures in a comfortable range between 65-70
Identify which HVAC products are reliable, and replace unreliable
equipment with those models.
First, we will identify whether the actual temperature was more than
five degrees different from the target temperature.
- Create a new worksheet in the Hive view and paste the following Hive query into your window.
CREATE TABLE hvac_temperatures as
select *, targettemp - actualtemp as temp_diff,
IF((targettemp - actualtemp) > 5, 'COLD',
IF((targettemp - actualtemp) < -5, 'HOT', 'NORMAL'))
IF((targettemp - actualtemp) > 5, '1',
IF((targettemp - actualtemp) < -5, '1', 0))
AS extremetemp from hvac;
This query creates a new table
hvac_temperaturesand copies data from the
- After you paste the query use Execute to create the new table.
On the Query Results page, use the slider to scroll to the right. You
will notice that two new attributes appear in the
The data in the temprange column indicates whether the actual
- NORMAL – within 5 degrees of the target temperature.
- COLD – more than five degrees colder than the target
- HOT – more than 5 degrees warmer than the target
If the temperature is outside of the normal range,
assigned a value of 1; otherwise its value is 0.
- Next we will combine the hvac and hvac_temperatures data sets.
Create a new worksheet in the hive view and use the following query to create a new table
hvac_building that contains data from the
hvac_temperatures table and the
create table if not exists hvac_building
as select h.*, b.country, b.hvacproduct, b.buildingage, b.buildingmgr
from buildings b join hvac_temperatures h on b.buildingid = h.buildingid;
Use Execute to run the query that will produce the table with the intended data.
After you’ve successfully executed the query, use the database explorer to load a sample of the data from the new
Now that we’ve constructued the data into a useful format, we can use different reporting tools to analyze the results.
In this tutorial you can choose to report with
Step 4a: Access the Refined Sensor Data with Microsoft Excel
In this section, we will use Microsoft Excel Professional Plus 2013 to
access the refined sentiment data.
In Windows, open a new Excel workbook, then select Data > From
Other Sources > From Microsoft Query.
On the Choose Data Source pop-up, select the Hortonworks ODBC data
source you installed previously, then click OK.
The Hortonworks ODBC driver enables you to access Hortonworks data
with Excel and other Business Intelligence (BI) applications that
After the connection to the Sandbox is established, the Query Wizard
appears. Select the “hvac_building” table in the Available tables
and columns box, then click the right arrow button to add the entire
“hvac_building” table to the query. Click Next to continue.
On the Filter Data screen, click Next to continue without
filtering the data.
On the Sort Order screen, click Next to continue without setting
a sort order.
Click Finish on the Query Wizard Finish screen to retrieve the
query data from the Sandbox and import it into Excel.
On the Import Data dialog box, click OK to accept the default
settings and import the data as a table.
The imported query data appears in the Excel workbook.
Now that we have successfully imported the refined sensor data into
Microsoft Excel, we can use the Excel Power View feature to analyze and
visualize the data.
Step 5: Visualize the Sensor Data Using Excel Power View
We will begin the data visualization by mapping the buildings that are
most frequently outside of the optimal temperature range.
In the Excel worksheet with the imported “hvac_building” table,
select Insert > Power View to open a new Power View report.
The Power View Fields area appears on the right side of the window,
with the data table displayed on the left. Drag the handles or click
the Pop Out icon to maximize the size of the data table.
In the Power View Fields area, select the checkboxes next to the
country and extremetemp fields, and clear all of the other
checkboxes. You may need to scroll down to see all of the check
In the FIELDS box, click the down-arrow at the right of the
extremetemp field, then select Count (Not Blank).
Click Map on the Design tab in the top menu.
The map view displays a global view of the data. We can see that the
office in Finland had 814 sensor readings where the temperature was
more than five degrees higher or lower than the target temperature.
In contrast, the German office is doing a better job maintaining
ideal office temperatures, with only 363 readings outside of the
Hot offices can lead to employee complaints and reduced
productivity. Let’s see which offices run hot.
In the Power View Fields area, clear the extremetemp checkbox
and select the temprange checkbox. Click the down-arrow at the
right of the temprange field, then select Add as Size.
Drag temprange from the Power View Fields area to the Filters
box, then select the HOT checkbox. We can see that the buildings
in Finland and France run hot most often.
Cold offices cause elevated energy expenditures and employee
In the Filters box, clear the HOT checkbox and select the
COLD checkbox. We can see that the buildings in Finland and
Indonesia run cold most often.
Our data set includes information about the performance of five
brands of HVAC equipment, distributed across many types of buildings
in a wide variety of climates. We can use this data to assess the
relative reliability of the different HVAC models.
Open a new Excel worksheet, then select Data > From Other Sources
> From Microsoft Query to access the hvac_building table. Follow
the same procedure as before to import the data, but this time only
select the “hvacproduct” and “extremetemp” columns.
In the Excel worksheet with the imported “hvacproduct” and
“extremetemp” columns, select Insert > Power View to open a new
Power View report.
Click the Pop Out icon to maximize the size of the data table. In
the FIELDS box, click the down-arrow at the right of the extremetemp
field, then select Count (Not Blank).
Select Column Chart > Stacked Columnin the top menu.
Click the down-arrow next to sort by hvacproduct in the upper
left corner of the chart area, then select Count of extremetemp.
We can see that the GG1919 model seems to regulate temperature most
reliably, whereas the FN39TG failed to maintain the appropriate
temperature range 9% more frequently than the GG1919.
We’ve shown how the Hortonworks Data Platform (HDP) can store and
analyze sensor data. With real-time access to massive amounts of
temperature and other types of data on HDP, your facilities department
can initiate data-driven strategies to reduce energy expenditures and
improve employee comfort.
Step 4b: Access the Refined Sensor Data with Apache Zeppelin
Apache Zeppelin makes data reporting easy on Hadoop. It has direct connections to Apache Spark and Hive in your cluster and allows you to create visualizations and analyze your data on the fly.
To start you’re going to need to open up the Apache Zeppelin view in Ambari.
Start by navigating back to the Ambari Dashboard at
- Use the dropdown menu to open the Zeppelin View.
- From here we’re going to need to create a new Zeppelin Notebook.
- Notebooks in Zeppelin is how we differentiate reports from one another.
- Hove over Notebook. Use the dropdown menu and Create a new note.
- Name the note HVAC Analysis Report and then Create Note.
- Head back to the Zeppelin homepage.
- Use the Notebook dropdown menu to open the new notebook HVAC Analysis Report.
- Zeppelin integrates with Hadoop by using things called interpreters.
- In this tutorial we’ll be working with the Hive interpreter to run Hive queries in Zeppelin, then visualize the results from our Hive queries directly in Zeppelin.
- To specify the Hive interpreter for this note, we need to put
%hiveat the top of the note. Everything afterwards will be interpreted as a Hive query.
- Type the following query into the note, then run it by clicking the Run arrow or by using the shortcut Shift+Enter.
select country, extremetemp, temprange from hvac_building
- After running the previous query we can view a chart of the data by clicking the chart button located just under the query.
- Click settings to open up more advanced settings for creating the chart. Here you can experiment with different values and columns to create different types of charts.
- 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.
- Awesome! You’ve just created your first chart using Apache Zeppelin.
- From this chart we can see which countries have the most extreme temperature and how many NORMAL events there are compared to HOT and COLD.
- From this data it could be possible to figure out which buildings might need HVAC upgrades, and which do not.
- Let’s try creating one more note to visualize which types of HVAC systems result in the least amount of
- Paste the following query into the blank Zeppelin note following the chart we made previously.
select hvacproduct, extremetemp from hvac_building
- Now use Shift+Enter to run the note.
- Arrange the fields according to the following image so we can recreate the chart below.
- 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
extremetempreadings. Thus we can make a more informed decision when purchasing new HVAC systems.
Apache Zeppelin gives you the power to connect right to your Hadoop cluster to quickly obtain results from the data inside of Hadoop without having to export data to any other sources.
It’s also important to note that Zeppelin contains many, many interpreters that can be utilized to obtain data in a variety of ways.
One of the default interpreters included with Zeppelin is for Apache Spark. With the popularity of Apache Spark rising, you can simply write Spark scripts to execute directly on Apache Zeppelin to obtain results from your data in a matter of seconds.
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