Added TFL-based examples to the intro.
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@ -15,3 +15,39 @@ Elasticsearch indexes in minutes--no code, no additional infrastructure required
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=== Data Discovery and Visualization
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=== Data Discovery and Visualization
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Let's take a look at how you might use Kibana to explore and visualize data.
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We've indexed some data from Transport for London (TFL) that shows a week's
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worth of transit (Oyster) card usage.
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From Kibana's Discover page, we can submit search queries, filter the results, and
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examine the data in the returned documents. For example, we could search for all
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of the trips that ended at the Baker Street station:
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image::images/TFL-BakerStreet.jpg[Discover]
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Right away, we can see that more people exit at Baker Street between 10:00 PM and 1:00 AM
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than any other time of day. By default, the Discover page shows a time-series chart
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and the first 500 entries that match the search criteria. You can change the time
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filter, interact with the chart to drill down into the data, and view the details
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of particular documents. For more information about exploring your data from the
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Discover page, see <<discover>>.
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You can construct visualizations of your search results from the Visualization page.
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Each visualization is associated with a search. For example, we can create a histogram
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that shows the weekly London commute traffic via tube using a a search that excludes
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incomplete trips and trips by bus. The Y-axis is the number of trips. The X-axis shows
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the day and time. By adding a sub-aggregation, we can see the top 3 end stations during
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each hour:
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image::images/TFL-CommuteHistogram.jpg[Visualize]
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You can save and share visualizations and add them to dashboards to make it easy
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to monitor and share particular metrics. For example, we could create a dashboard
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that displays several visualizations of the TFL data:
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image::images/TFL-Dashboard.jpg[Dashboard]
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For more information about creating and sharing visualizations, see <<visualize>>.
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For more information about working with Dashboards, see <<dashboard>>.
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