Merge pull request #60 from implydata/autobuild
Autobuild
diff --git a/faq.html b/faq.html
index b640697..9354601 100644
--- a/faq.html
+++ b/faq.html
@@ -137,38 +137,41 @@
<p>Druid offers the following advantages over traditional data warehouses:</p>
<ul>
-<li>Low latency streaming ingest, and direct integration with messages buses such as
-Apache Kafka.</li>
-<li>Time-based partitioning, which enables performant time-based
-queries.</li>
-<li>Fast search and filter, for fast ad-hoc slice and dice.</li>
-<li>Minimal schema design, and native support for semi-structured and nested data.</li>
+<li>Much lower latency for OLAP-style queries</li>
+<li>Much lower latency for data ingest (both streaming and batch)</li>
+<li>Out-of-the-box integration with Apache Kakfa, AWS Kinesis, HDFS, AWS S3, and more</li>
+<li>Time-based partitioning, which enables performant time-based queries</li>
+<li>Fast search and filter, for fast slice and dice</li>
+<li>Minimal schema design and native support for semi-structured and nested data</li>
</ul>
-<p>Consider using Druid over a data warehouse if you have streaming data, and
-require low-latency ingest as well as low-latency queries. Also consider Druid
-if you need ad-hoc analytics. Druid is great for slice and dice and drill
-downs. Druid is also often used over a data warehouse to power interactive
-applications, where support for high concurrency queries is required.</p>
+<p>Consider using Druid to augment your data warehouse if your use case requires:</p>
-<h3 id="is-druid-a-sql-on-hadoop-solution-when-should-i-use-druid-over-presto-hive">Is Druid a SQL-on-Hadoop solution? When should I use Druid over Presto/Hive?</h3>
+<ul>
+<li>Powering an user-facing application</li>
+<li>Low-latency query response with high concurrency</li>
+<li>Instant data visibility</li>
+<li>Fast ad-hoc slice and dice</li>
+<li>Streaming data</li>
+</ul>
-<p>Druid supports SQL and can load data from Hadoop, but is not a SQL-on-Hadoop
-system. Modern SQL-on-Hadoop solutions are used for the same use cases as data
-warehouses, except they are designed for architectures where compute and
-storage are separated systems, and data is loaded from storage into the compute
-layer as needed by queries.</p>
-
-<p>The previous section on Druid vs data warehouses also applies to Druid versus
-SQL-on-Hadoop solutions.</p>
+<p>To summarize, Druid shines when the use cases involves real-time analytics and
+where the end-user (technical or not) wants to apply numerous queries in rapid
+succession to explore or better understand data trends. </p>
<h3 id="is-druid-a-log-aggregation-log-search-system-when-should-i-use-druid-over-elastic-splunk">Is Druid a log aggregation/log search system? When should I use Druid over Elastic/Splunk?</h3>
-<p>Druid uses inverted indexes (in particular, compressed bitmaps) for fast searching and filtering, but it is not generally considered a search system.
-While Druid contains many features commonly found in search systems, such as the ability to stream in structured and semi-structured data and the ability to search and filter the data, Druid isn’t commonly used to ingest text logs and run full text search queries over the text logs.
-However, Druid is often used to ingest and analyze semi-structured data such as JSON.</p>
+<p>Druid uses inverted indexes (in particular, compressed bitmaps) for fast
+searching and filtering, but it is not generally considered a search system.
+While Druid contains many features commonly found in search systems, such as
+the ability to stream in structured and semi-structured data and the ability to
+search and filter the data, Druid isn’t commonly used to ingest text logs and
+run full text search queries over the text logs. However, Druid is often used
+to ingest and analyze semi-structured data such as JSON.</p>
-<p>Druid at its core is an analytics engine and as such, it can support numerical aggregations, groupBys (including multi-dimensional groupBys), and other analytic workloads faster and more efficiently than search systems.</p>
+<p>Druid at its core is an analytics engine and as such, it can support numerical
+aggregations, groupBys (including multi-dimensional groupBys), and other
+analytic workloads faster and more efficiently than search systems.</p>
<h3 id="is-druid-a-timeseries-database-when-should-i-use-druid-over-influxdb-opentsdb-prometheus">Is Druid a timeseries database? When should I use Druid over InfluxDB/OpenTSDB/Prometheus?</h3>
@@ -183,15 +186,22 @@
outperformance TSDBs when grouping, searching, and filtering on tags that are
not time, or when computing complex metrics such as histograms and quantiles.</p>
+<h3 id="does-druid-separate-storage-and-compute">Does Druid separate storage and compute?</h3>
+
+<p>Druid creates an indexed copy of raw data that is highly optimized for
+analytic queries. Druid runs queries over this indexed data, called a <a href="/docs/latest/design/segments.html">'segment'</a>
+in Druid, and does not pull raw data from an external storage system as needed
+by queries. </p>
+
<h3 id="how-is-druid-deployed">How is Druid deployed?</h3>
<p>Druid can be deployed on commodity hardware in any *NIX based environment.
-A Druid cluster consists of several different processes, each designed to do a small set of things very well (ingestion, querying, coordination, etc).
-Many of these processes can be co-located and deployed together on the same hardware as described <a href="/docs/latest/tutorials/quickstart">here</a>.</p>
+A Druid cluster consists of several different services, each designed to do a small set of things very well (ingestion, querying, coordination, etc).
+Many of these services can be co-located and deployed together on the same hardware as described <a href="/docs/latest/tutorials/quickstart">here</a>.</p>
-<p>Druid was initially created in the cloud, and runs well in AWS, GCP, Azure, and other cloud environments.</p>
+<p>Druid was designed for the cloud, and runs well in AWS, GCP, Azure, and other cloud environments.</p>
-<h3 id="where-does-druid-fit-in-my-existing-hadoop-based-data-stack">Where does Druid fit in my existing Hadoop-based data stack?</h3>
+<h3 id="where-does-druid-fit-in-my-big-data-stack">Where does Druid fit in my big data stack?</h3>
<p>Druid typically connects to a source of raw data such as a message bus such as Apache Kafka, or a filesystem such as HDFS.
Druid ingests an optimized, column-oriented, indexed copy of your data and serves analytics workloads on top of it.</p>
@@ -214,7 +224,7 @@
size of its disks.</p>
<p>Individual Historicals can be configured with the maximum amount of data
-they should be given. Coupled with the Coordinator’s ability to assign data to
+they should be given. Coupled with the Coordinator’s ability to assign data to
different “tiers” based on different query requirements, Druid is essentially a
system that can be configured across a wide spectrum of performance
requirements. All data can be in memory and processed, or data can be heavily
diff --git a/index.html b/index.html
index 838507b..af224bb 100644
--- a/index.html
+++ b/index.html
@@ -166,7 +166,7 @@
</p>
</div>
<div class="feature">
- <span class="fa fa-globe fa"></span>
+ <span class="fa fa-cloud fa"></span>
<h5>Deploy in AWS/GCP/Azure, hybrid clouds, Kubernetes, and bare metal</h5>
<p>
Druid can be deployed in any *NIX environment on commodity hardware, both in the cloud and on premise. Deploying Druid is easy: scaling up and down is as simple as adding and removing Druid services.
diff --git a/technology.html b/technology.html
index a9a7d83..92b4a9b 100644
--- a/technology.html
+++ b/technology.html
@@ -126,7 +126,7 @@
<div class="row">
<div class="col-md-10 col-md-offset-1">
<p>Apache Druid is an open source distributed data store.
-Druid’s core design combines ideas from <a href="https://en.wikipedia.org/wiki/Online_analytical_processing">OLAP/analytic databases</a>, <a href="https://en.wikipedia.org/wiki/Time_series_database">timeseries databases</a>, and <a href="https://en.wikipedia.org/wiki/Full-text_search">search systems</a> to create a unified system for a broad range of <a href="/use-cases">use cases</a>. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture.</p>
+Druid’s core design combines ideas from <a href="https://en.wikipedia.org/wiki/Data_warehouse">data warehouses</a>, <a href="https://en.wikipedia.org/wiki/Time_series_database">timeseries databases</a>, and <a href="https://en.wikipedia.org/wiki/Full-text_search">search systems</a> to create a unified system for real-time analytics for a broad range of <a href="/use-cases">use cases</a>. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture.</p>
<div class="image-large">
<img src="img/diagram-2.png" style="max-width: 360px">
diff --git a/use-cases.html b/use-cases.html
index 2c60583..79c67de 100644
--- a/use-cases.html
+++ b/use-cases.html
@@ -125,47 +125,23 @@
<div class="container">
<div class="row">
<div class="col-md-10 col-md-offset-1">
- <h2 id="streaming-and-operational-data">Streaming and operational data</h2>
+ <h2 id="real-time-analytics-and-intelligence">Real-time analytics and intelligence</h2>
-<p>Apache Druid generally works well with any event-oriented, clickstream, timeseries, or telemetry data, especially streaming datasets from <a href="https://kafka.apache.org/">Apache Kafka</a>.
-Druid provides <a href="/docs/latest/development/extensions-core/kafka-ingestion">exactly once consumption semantics</a> from Apache Kafka and is commonly used as a sink for event-oriented Kafka topics.</p>
+<p>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.</p>
-<p>Druid also works well for batch data sets.
-Organizations have deployed Druid to accelerate queries and power applications where the input data is one or more static files.
-Druid is a great fit if you are developing a user-facing application and you want your users to be able to self service their own questions.</p>
+<p>Common application areas for Druid include:</p>
-<p>Some common high level use cases of Druid include:</p>
+<ul>
+<li>Clickstream analytics (web and mobile analytics)</li>
+<li>Risk/fraud analysis</li>
+<li>Network telemetry analytics (network performance monitoring)</li>
+<li>Server metrics storage</li>
+<li>Supply chain analytics (manufacturing metrics)</li>
+<li>Application performance metrics</li>
+<li>Business intelligence / OLAP</li>
+</ul>
-<div class="features">
- <div class="feature">
- <span class="fa fa-rocket fa"></span>
- <h5>Analyze performance</h5>
- <p>
- Create interactive dashboards with full drill down capabilities. Analyze performance of digital products, track mobile app usage, or monitor site reliability.
- </p>
- </div>
- <div class="feature">
- <span class="fa fa-exclamation-triangle fa"></span>
- <h5>Diagnose problems</h5>
- <p>
- Find the root cause of issues. Troubleshoot netflow bottlenecks, analyze security threats, or diagnose software crashes.
- </p>
- </div>
- <div class="feature">
- <span class="fa fa-users fa"></span>
- <h5>Find commonalities</h5>
- <p>
- Find common attributes among events. Identify shared components in defective products, or determine patterns in top performing products.
- </p>
- </div>
- <div class="feature">
- <span class="fa fa-money-bill-wave-alt fa"></span>
- <h5>Increase efficiency</h5>
- <p>
- Improve product engagement. Optimize ad-spend in digital marketing campaigns or increase user engagement in online products.
- </p>
- </div>
-</div>
+<p>Some of these use cases are described in more detail below. For an overview of technical differentiation, please see the <a href="/faq">FAQ</a>.</p>
<h2 id="user-activity-and-behavior">User activity and behavior</h2>
