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       <h3 id="is-druid-a-data-warehouse-when-should-i-use-druid-over-redshift-bigquery-snowflake">Is Druid a data warehouse? When should I use Druid over Redshift/BigQuery/Snowflake?</h3>

 <p>Apache Druid is a new type of database to power real-time analytic workloads for
 event-driven data, and isn’t a traditional data warehouse.  Although Druid
 incorporates architecture ideas from data warehouses such as column-oriented
 storage, Druid also incorporates designs from search systems and timeseries
 databases. Druid&#39;s architecture is designed to handle many use cases that
 traditional data warehouses cannot.</p>

 <p>Druid offers the following advantages over traditional data warehouses:</p>

 <ul>
 <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 Kafka, 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 to augment your data warehouse if your use case requires:</p>

 <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>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 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>

 <p>Druid does share some characteristics with timeseries databases, but also
 combines ideas from analytic databases and search systems.  Like in timeseries
 databases, Druid is optimized for data where a timestamp is present.  Druid
 partitions data by time, and queries that include a time filter will be
 significantly faster than those that do not.  Aggregating metrics and filtering
 on dimensions (which are roughly equivalent to TSDBs&#39; tags) are also very fast when a
 time filter is present.  However, because Druid incorporates many architectural designs
 from analytics databases and search systems, it can significantly
 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">&#39;segment&#39;</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 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 designed for the cloud, and runs well in AWS, GCP, Azure, and other cloud environments.</p>

 <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>

 <p>A common streaming data oriented setup involving Druid looks like this:
 Raw data → Kafka → Stream processor (optional, typically for ETL) → Kafka (optional) → Druid → Application/user</p>

 <p>A common batch/static file oriented setup involving Druid looks like this:
 Raw data → Kafka (optional) → HDFS → ETL process (optional) → Druid → Application/user</p>

 <p>The same Druid cluster can serve both the streaming and batch path.</p>

 <h3 id="is-druid-in-memory">Is Druid in-memory?</h3>

 <p>The earliest iterations of Druid didn’t allow for data to be paged in from
 and out to disk, so it was often called an “in-memory” database. As Druid
 evolved, this limitation was removed. To provide a balance between hardware
 cost and query performance, Druid leverages memory-mapping to page data between
 disk and memory and extend the amount of data a single node can load up to the
 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
 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
 over-committed compared to the amount of memory available. Druid can also
 support complex configurations, such as configuring the most recent month of
 data in memory, while everything else is over-committed.</p>

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	<h3 id="is-druid-a-data-warehouse-when-should-i-use-druid-over-redshift-bigquery-snowflake">Is Druid a data warehouse? When should I use Druid over Redshift/BigQuery/Snowflake?</h3>

	<p>Apache Druid is a new type of database to power real-time analytic workloads for
	event-driven data, and isn’t a traditional data warehouse. Although Druid
	incorporates architecture ideas from data warehouses such as column-oriented
	storage, Druid also incorporates designs from search systems and timeseries
	databases. Druid's architecture is designed to handle many use cases that
	traditional data warehouses cannot.</p>

	<p>Druid offers the following advantages over traditional data warehouses:</p>

	<ul>
	<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 Kafka, 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 to augment your data warehouse if your use case requires:</p>

	<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>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 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>

	<p>Druid does share some characteristics with timeseries databases, but also
	combines ideas from analytic databases and search systems. Like in timeseries
	databases, Druid is optimized for data where a timestamp is present. Druid
	partitions data by time, and queries that include a time filter will be
	significantly faster than those that do not. Aggregating metrics and filtering
	on dimensions (which are roughly equivalent to TSDBs' tags) are also very fast when a
	time filter is present. However, because Druid incorporates many architectural designs
	from analytics databases and search systems, it can significantly
	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 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 designed for the cloud, and runs well in AWS, GCP, Azure, and other cloud environments.</p>

	<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>

	<p>A common streaming data oriented setup involving Druid looks like this:
	Raw data → Kafka → Stream processor (optional, typically for ETL) → Kafka (optional) → Druid → Application/user</p>

	<p>A common batch/static file oriented setup involving Druid looks like this:
	Raw data → Kafka (optional) → HDFS → ETL process (optional) → Druid → Application/user</p>

	<p>The same Druid cluster can serve both the streaming and batch path.</p>

	<h3 id="is-druid-in-memory">Is Druid in-memory?</h3>

	<p>The earliest iterations of Druid didn’t allow for data to be paged in from
	and out to disk, so it was often called an “in-memory” database. As Druid
	evolved, this limitation was removed. To provide a balance between hardware
	cost and query performance, Druid leverages memory-mapping to page data between
	disk and memory and extend the amount of data a single node can load up to the
	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
	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
	over-committed compared to the amount of memory available. Druid can also
	support complex configurations, such as configuring the most recent month of
	data in memory, while everything else is over-committed.</p>

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