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SEO audit: Content analysis

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Title Druid | Use Cases
Text / HTML ratio 68 %
Frame Excellent! The website does not use iFrame solutions.
Flash Excellent! The website does not have any flash contents.
Keywords cloud Druid data application metrics attributes user users queries search applications performance commonly databases activity compute timeseries filter analytics fit Unlike
Keywords consistency
Keyword Content Title Description Headings
Druid 28
data 16
application 9
metrics 9
attributes 9
user 7
Headings
H1 H2 H3 H4 H5 H6
1 7 0 1 0 0
Images We found 0 images on this web page.

SEO Keywords (Single)

Keyword Occurrence Density
Druid 28 1.40 %
data 16 0.80 %
application 9 0.45 %
metrics 9 0.45 %
attributes 9 0.45 %
user 7 0.35 %
users 7 0.35 %
queries 6 0.30 %
search 5 0.25 %
applications 4 0.20 %
performance 4 0.20 %
commonly 4 0.20 %
databases 4 0.20 %
activity 4 0.20 %
compute 4 0.20 %
timeseries 4 0.20 %
filter 4 0.20 %
analytics 4 0.20 %
fit 3 0.15 %
Unlike 3 0.15 %

SEO Keywords (Two Word)

Keyword Occurrence Density
Druid is 10 0.50 %
can be 7 0.35 %
used to 6 0.30 %
of attributes 5 0.25 %
Druid can 5 0.25 %
such as 5 0.25 %
is commonly 4 0.20 %
able to 4 0.20 %
and filter 4 0.20 %
commonly used 4 0.20 %
be used 4 0.20 %
a userfacing 3 0.15 %
to measure 3 0.15 %
timeseries databases 3 0.15 %
slice and 3 0.15 %
and dice 3 0.15 %
you want 3 0.15 %
set of 3 0.15 %
and search 3 0.15 %
search and 3 0.15 %

SEO Keywords (Three Word)

Keyword Occurrence Density Possible Spam
is commonly used 4 0.20 % No
Druid can be 4 0.20 % No
can be used 4 0.20 % No
set of attributes 3 0.15 % No
slice and dice 3 0.15 % No
search and filter 3 0.15 % No
Druid is commonly 3 0.15 % No
by being able 2 0.10 % No
queries and power 2 0.10 % No
is a great 2 0.10 % No
Druid is a 2 0.10 % No
and filter on 2 0.10 % No
being able to 2 0.10 % No
deployed Druid to 2 0.10 % No
accelerate queries and 2 0.10 % No
to accelerate queries 2 0.10 % No
Druid to accelerate 2 0.10 % No
great fit if 2 0.10 % No
have deployed Druid 2 0.10 % No
Organizations have deployed 2 0.10 % No

SEO Keywords (Four Word)

Keyword Occurrence Density Possible Spam
Druid can be used 4 0.20 % No
Druid is commonly used 3 0.15 % No
any set of attributes 2 0.10 % No
a great fit if 2 0.10 % No
Organizations have deployed Druid 2 0.10 % No
have deployed Druid to 2 0.10 % No
along any set of 2 0.10 % No
Druid to accelerate queries 2 0.10 % No
to accelerate queries and 2 0.10 % No
accelerate queries and power 2 0.10 % No
queries and power applications 2 0.10 % No
Druid is a great 2 0.10 % No
is a great fit 2 0.10 % No
a userfacing application and 2 0.10 % No
great fit if you 2 0.10 % No
issues Unlike many traditional 2 0.10 % No
userfacing application and you 2 0.10 % No
application and you want 2 0.10 % No
and you want your 2 0.10 % No
you want your users 2 0.10 % No

Druid.io Spined HTML

Druid | Use Cases Technology Use Cases Powered By Docs Community Download MENU MENU Use Cases Streaming and operational data Druid often works well with any event-oriented, timeseries, or telemetry data, expressly streaming datasets from Apache Kafka. Druid provides exactly once consumption semantics from Apache Kafka and is wontedly used as a sink for event-oriented Kafka topics. Druid moreover works well for batch data sets. Organizations have deployed Druid to slide queries and power applications where the input data are static files. Druid is a unconfined fit if you are developing a user-facing using and you want your users to be worldly-wise to self service their own questions. Network flows Druid is wontedly used to collect and unriddle network flows. Druid is used to summarily slice and dice spritz data withal any set of attributes. Druid helps with network spritz wringer by stuff worldly-wise to ingest large amounts of spritz records, and by stuff worldly-wise to group or rank wideness dozens of nature at query time at interactive speeds. These nature often include cadre nature like IP and port, as well as nature widow through enhancement such as geolocation, service, application, facility, and ASN. Druid's worthiness to handle flexible schemas ways that you can add any nature you want. User worriedness and policies Druid is often used for clickstreams, viewstreams, and worriedness streams. Specific use cases include measuring user engagement, tracking A/B test data for product releases, and understanding usage patterns. Druid can compute user metrics such as unshared counts both exactly and approximately. This midpoint measures such as daily zippy users can be computed in under a second approximately (with 98% avg. accuracy) to view unstipulated trends, or computed exactly to present to key stakeholders. Furthermore, Druid can be used for funnel analysis, and to measure how many users took one action, but did not take flipside action. Such wringer is useful is tracking user signups for a product. Druid’s search and filter capabilities enable rapid, easy drill-downs of users withal any set of attributes. Measure and compare user worriedness by age, gender, location, and much more.Usingperformance management Druid is often used to track the operational data generated by applications. Similar to the user worriedness use case, this data can be well-nigh how users are interacting with an using or it can be the metrics emitted by the using itself. Druid can be used to drill into how variegated components of an using are performing, identify bottlenecks, and troubleshoot issues. Unlike many traditional solutions, there are few limits to the volume, complexity, and throughput of the data. Rapidly unriddle using events with thousands of attributes, and compute ramified metrics on load, performance, and usage. For example, rank API endpoints based on 95th percentile query latency, and slice and dice how these metrics transpiration based on any ad-hoc set of nature such as time of day, user demographic, or datacenter location. 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. Unlike many traditional timeseries databases, Druid is an analytics engine at heart. Druid combines ideas of timeseries databases, column-oriented supersensual databases, and search systems. Druid supports time-based partitioning, column-oriented storage, and search indexes in a single system. This ways time-based queries, numerical aggregations, and search and filter queries are all extremely fast. You can include millions of unique dimension values with your metrics, and summarily group and filter on any combination of dimensions (dimensions in Druid are similar to tags in TSDBs). You can group and rank on tags, and compute a variety of ramified metrics. Furthermore, you search and filter on tag values orders of magnitude faster than in traditional timeseries databases. Digital marketing Druid is wontedly used to store and query online razzmatazz data. This data typically comes from ad servers and is hair-trigger to measure and understand razzmatazz wayfarers performance, click through rates, conversion rates (attrition rates), and much more. Druid was initially designed to power a user-facing analytics using for digital razzmatazz data. Druid has seen substantial production use for this type of data and the largest users in the world have petabytes of data stored on thousands of servers. 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. OLAP and merchantry intelligence Druid is wontedly used for BI use cases. Organizations have deployed Druid to slide queries and power applications. Unlike SQL-on-Hadoop engines such as Presto or Hive, Druid is designed for upper concurrency and sub-second queries, powering interactive data exploration through a UI. In unstipulated this makes Druid a largest fit for truly interactive visual analytics. Druid is a unconfined fit if you need a user-facing using and you want your users to be worldly-wise to run their own self service drill-down queries. You can either develop your own using using Druid's API or use once of the many off the shelf applications that work with Druid. 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