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testing clickstream tutorial from 2-0 for #48
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@ZacBlanco
ZacBlanco committed Dec 28, 2015
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# Hortonworks Tutorials

- [How to Visualize Website CLickstream Data](how-to-visualize-website-clickstream-data-2-0/how-to-visualize-website-clickstream-data-2-0.md)
- [How to Visualize Website Clickstream Data](tutorials/how-to-visualize-website-clickstream-data-2-0/how-to-visualize-website-clickstream-data-2-0.md)
70 changes: 35 additions & 35 deletions ...e-website-clickstream-data-2-0/how-to-visualize-website-clickstream-data-2-0.md
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Expand Up @@ -64,47 +64,47 @@ Here’s a summary of the data we’re working with:

Let’s start by looking at the raw Omniture website data. In the Hortonworks Sandbox, click the File Browser icon in the toolbar at the top of the page, then select the `Omniture.0.tsv.gz` file.

![](/assets/2-0/clickstream/tutorial-10/01_file_browser.jpg)
![](/assets/2-0/clickstream/01_file_browser.jpg)

* The raw data file appears in the File Browser, and you can see that it contains information such as URL, timestamp, IP address, geocoded IP address, and user ID (SWID).

The Omniture log dataset contains about 4 million rows of data, which represents five days of clickstream data. Often, organizations will process weeks, months, or even years of data.

![](/assets/2-0/images/02_omniturelog_raw.jpg)
![](/assets/2-0/clickstream/02_omniturelog_raw.jpg)

* Now let’s look at the users table using HCatalog. Click the HCat icon in the toolbar at the top of the page, then click **Browse Data** in the users row.

![](/assets/2-0/images/03_select_users_table.jpg)
![](/assets/2-0/clickstream/03_select_users_table.jpg)

* The users table appears, and you can see the SWID, birth date, and gender columns.

![](/assets/2-0/images/04_users_table.jpg)
![](/assets/2-0/clickstream/04_users_table.jpg)

* You can also use HCatalog to view the data in the products table, which maps product categories to website URLs.

![](/assets/2-0/images/05_products_table.jpg)
![](/assets/2-0/clickstream/05_products_table.jpg)

* In the “Loading Data into the Hortonworks Sandbox” tutorial, we used Apache Hive to join the three data sets into one master set. This is easily accomplished in Hadoop, even when working with millions or billions of rows of data.

First, we used a Hive script to generate an “omniture” view that contained a subset of the data in the Omniture log table.

![](/assets/2-0/images/06_omniture_script.jpg)
![](/assets/2-0/clickstream/06_omniture_script.jpg)

* To view the omniture data in Hive, click **Table**, then click **Browse Data**in the omniture row.

![](/assets/2-0/images/07_browse_omniture.jpg)
![](/assets/2-0/clickstream/07_browse_omniture.jpg)

* The query results will appear, and you can see that the results include the data from the Omniture log table that were specified in the query.

![](/assets/2-0/images/08_omniture_query_data.jpg)
![](/assets/2-0/clickstream/08_omniture_query_data.jpg)

* Next, we created a “webloganalytics” script to join the omniture website log data to the CRM data (registered users) and CMS data (products). This Hive query executed a join to create a unified dataset across our data sources.

![](/assets/2-0/images/09_webloganalytics_query.jpg)
![](/assets/2-0/clickstream/09_webloganalytics_query.jpg)

* You can view the data generated by the webloganalytics script in Hive as described in the preceding steps.

![](/assets/2-0/images/10_webloganalytics_table.jpg)
![](/assets/2-0/clickstream/10_webloganalytics_table.jpg)

Now that we have reviewed the refined website data, we can access the data with Excel.

Expand All @@ -116,35 +116,35 @@ In this section, we will use Excel Professional Plus 2013 to access the refined

* In Windows, open a new Excel workbook, then select **Data > From Other Sources > From Microsoft Query**.

![](/assets/2-0/images/11_open_query.jpg)
![](/assets/2-0/clickstream/11_open_query.jpg)

* 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 support ODBC.

![](/assets/2-0/images/12_choose_data_source.jpg)
![](/assets/2-0/clickstream/12_choose_data_source.jpg)

* After the connection to the sandbox is established, the Query Wizard appears. Select the webloganalytics table in the Available tables and columns box, then click the right arrow button to add the entire webloganalytics table to the query. Click **Next** to continue.

![](/assets/2-0/images/13_query_wizard1.jpg)
![](/assets/2-0/clickstream/13_query_wizard1.jpg)

* On the Filter Data screen, click **Next** to continue without filtering the data.

![](/assets/2-0/images/14_query_wizard2.jpg)
![](/assets/2-0/clickstream/14_query_wizard2.jpg)

* On the Sort Order screen, click **Next** to continue without setting a sort order.

![](/assets/2-0/images/15_query_wizard3.jpg)
![](/assets/2-0/clickstream/15_query_wizard3.jpg)

* Click **Finish** on the Query Wizard Finish screen to retrieve the query data from the sandbox and import it into Excel.

![](/assets/2-0/images/16_query_wizard4.jpg)
![](/assets/2-0/clickstream/16_query_wizard4.jpg)

* On the Import Data dialog box, click **OK** to accept the default settings and import the data as a table.

![](/assets/2-0/images/17_import_data.jpg)
![](/assets/2-0/clickstream/17_import_data.jpg)

* The imported query data appears in the Excel workbook.

![](/assets/2-0/images/18_data_imported.jpg)
![](/assets/2-0/clickstream/18_data_imported.jpg)

Now that we have successfully imported Hortonworks Sandbox data into Microsoft Excel, we can use the Excel Power View feature to analyze and visualize the data.

Expand All @@ -162,47 +162,47 @@ Data visualization can help you optimize your website and convert more visits in

In the Excel workbook with the imported webloganalytics data, select **Insert > Power View** to open a new Power View report.

![](/assets/2-0/images/19_open_powerview.jpg)
![](/assets/2-0/clickstream/19_open_powerview.jpg)

* 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.

![](/assets/2-0/images/20_powerview_initial_popout.jpg)
![](/assets/2-0/clickstream/20_powerview_initial_popout.jpg)

* Let’s start by taking a look at the countries of origin of our website visitors. In the Power View Fields area, leave the **country** checkbox selected, and clear all of the other checkboxes. The data table will update to reflect the selections.

![](/assets/2-0/images/21_country_selected.jpg)
![](/assets/2-0/clickstream/21_country_selected.jpg)

* On the Design tab in the top menu, click **Map**.

![](/assets/2-0/images/22_open_map.jpg)
![](/assets/2-0/clickstream/22_open_map.jpg)

* The map view displays a global view of the data. Now let’s take a look at a count of IP address by state. First, drag the **ip** field into the SIZE box.

![](/assets/2-0/images/23_add_ip_count.jpg)
![](/assets/2-0/clickstream/23_add_ip_count.jpg)

* Drag **country** from the Power View Fields area into the Filters area, then select the **usa** checkbox.

![](/assets/2-0/images/24_filter_by_usa.jpg)
![](/assets/2-0/clickstream/24_filter_by_usa.jpg)

* Next, drag **state** into the LOCATIONS box. Remove the **country** field from the LOCATIONS box by clicking the down-arrow and then **Remove Field**.

![](/assets/2-0/images/25_state_to_locations.jpg)
![](/assets/2-0/clickstream/25_state_to_locations.jpg)

* Use the map controls to zoom in on the United States. Move the pointer over each state to display the IP count for that state.

![](/assets/2-0/images/26_ip_count_by_state.jpg)
![](/assets/2-0/clickstream/26_ip_count_by_state.jpg)

* Our dataset includes product data, so we can display the product categories viewed by website visitors in each state. To display product categories in the map by color, drag the **category** field into the COLOR box.

![](/assets/2-0/images/27_category_by_color.jpg)
![](/assets/2-0/clickstream/27_category_by_color.jpg)

* The map displays the product categories by color for each state. Move the pointer over each state to display detailed category information. We can see that the largest number of page hits in Florida were for clothing, followed by shoes.

![](/assets/2-0/images/28_category_by_color_florida.jpg)
![](/assets/2-0/clickstream/28_category_by_color_florida.jpg)

* Now let’s look at the clothing data by age and gender so we can optimize our content for these customers. Select **Insert > Power View** to open a new Power View report.

![](/assets/2-0/images/29_new_powerview1.jpg)
![](/assets/2-0/clickstream/29_new_powerview1.jpg)

To set up the data, set the following fields and filters:

Expand All @@ -212,15 +212,15 @@ To set up the data, set the following fields and filters:

After setting these fields and filters, select **Column Chart > Clustered Column** in the top menu.

![](/assets/2-0/images/tutorial-10/30_open_clustered_column1.jpg)
![](/assets/2-0/clickstream/30_open_clustered_column1.jpg)

* To finish setting up the chart, drag **age** into the AXIS box. Also, remove **ip** from the AXIS box by clicking the down-arrow and then **Remove Field**. The chart shows that the majority of men shopping for clothing on our website are between the ages of 22 and 30\. With this information, we can optimize our content for this market segment.

![](/assets/2-0/images/tutorial-10/31_clothing_by_age.jpg)
![](/assets/2-0/clickstream/31_clothing_by_age.jpg)

* Let’s assume that our data includes information about website pages (URLs) with high bounce rates. A page is considered to have a high bounce rate if it is the last page a user visited before leaving the website. By filtering this URL data by our target age group, we can find out exactly which website pages we should optimize for this market segment. Select **Insert > Power View** to open a new Power View report.

![](/assets/2-0/images/tutorial-10/32_new_powerview2.jpg)
![](/assets/2-0/clickstream/32_new_powerview2.jpg)

To set up the data, set the following fields and filters:

Expand All @@ -232,15 +232,15 @@ To set up the data, set the following fields and filters:

After setting these fields and filters, select **Column Chart > Clustered Column** in the top menu.

![](/assets/2-0/images/tutorial-10/33_open_clustered_column2.jpg)
![](/assets/2-0/clickstream/33_open_clustered_column2.jpg)

* The chart shows that we should focus on optimizing four of our website pages for the market segment of men between the ages of 22 and 30\. Now we can redesign these four pages and test the new designs based on our target demographic, thereby reducing the bounce rate and increasing customer retention and sales.

![](/assets/2-0/images/tutorial-10/34_urls_for_age_group.jpg)
![](/assets/2-0/clickstream/34_urls_for_age_group.jpg)

* You can use the controls in the upper left corner of the map to sort by Count of URL in ascending order.

![](/assets/2-0/images/tutorial-10/35_urls_for_age_group_sorted.jpg)
![](/assets/2-0/clickstream/35_urls_for_age_group_sorted.jpg)

Now that you have successfully analyzed and visualized Hortonworks Sandbox data with Microsoft Excel, you can see how Excel and other BI tools can be used with the Hortonworks platform to derive insights about customers from various data sources.

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