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canonical: 'https://druid.apache.org/use-cases'
---
+
## Power real-time analytics & data applications
Apache Druid is a database that is most often used for powering use cases where real-time ingest, fast query performance, and high uptime are important. As such, Druid is commonly used for powering GUIs of analytical applications, or as a backend for highly-concurrent APIs that need fast aggregations. Druid works best with event-oriented data.
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## Common use case examples
-<div class="features">
-<div class="feature">
-<span class="fa fa-users fa"></span>
+### User activity and behavior
-## User activity and behavior
Druid is often used for clickstreams, viewstreams, and activity streams.
Specific use cases include measuring user engagement, tracking A/B test data for product releases, and understanding usage patterns.
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Druid’s search and filter capabilities enable rapid, easy drill-downs of users along any set of attributes.
Measure and compare user activity by age, gender, location, and much more.
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-## Network flows
+### Network flows
Druid is commonly used to collect and analyze network flows.
Druid is used to arbitrarily slice and dice flow data along any set of attributes.
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These attributes often include core attributes like IP and port, as well as attributes added through enhancement such as geolocation, service, application, facility, and ASN.
Druid's ability to handle flexible schemas means that you can add any attributes you want.
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-## Digital marketing
+### Digital marketing
Druid is commonly used to store and query online advertising data.
This data typically comes from ad servers and is critical to measure and understand advertising campaign performance, click through rates, conversion rates (attrition rates), and much more.
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Druid can be used to compute impressions, clicks, eCPM, and key conversion metrics, filtered on publisher, campaign, user information, and dozens of other dimensions supporting full slice and dice capability.
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-## Application performance management
+### Application performance management
Druid is often used to track the operational data generated by applications.
Similar to the user activity use case, this data can be about how users are interacting with an application or it can be the metrics emitted by the application itself.
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Rapidly analyze application events with thousands of attributes, and compute complex metrics on load, performance, and usage.
For example, rank API endpoints based on 95th percentile query latency, and slice and dice how these metrics change based on any ad-hoc set of attributes such as time of day, user demographic, or datacenter location.
-</div>
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-## IoT and device metrics
+### IoT and device metrics
Druid can be used as a time series solution for server and device metrics.
Ingest machine generated data in real-time, and perform rapid ad-hoc analytics to measure performance, optimize hardware resources, or identify issues.
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You can group and rank on tags, and compute a variety of complex metrics.
Furthermore, you search and filter on tag values orders of magnitude faster than in traditional timeseries databases.
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-## OLAP and business intelligence
+### OLAP and business intelligence
Druid is commonly used for BI use cases.
Organizations have deployed Druid to accelerate queries and power applications.
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Druid is a great fit if you need a user-facing application and you want your users to be able to run their own self service drill-down queries.
You can either develop your own application using Druid's API or use one of the [many off the shelf applications](/libraries) that work with Druid.
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