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| <h5>Build fast, modern data analytics applications</h5> |
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| Druid is designed for <a href='/use-cases'>workflows</a> where fast ad-hoc analytics, instant data visibility, or supporting high concurrency is important. As such, Druid is often used to power UIs where an interactive, consistent user experience is desired. |
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| <h5>Easy integration with your existing data pipelines</h5> |
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| Druid streams data from message buses such as <a href='http://kafka.apache.org/'>Kafka</a>, and <a href='https://aws.amazon.com/kinesis/'>Amazon Kinesis</a>, and batch load files from data lakes such as <a href='https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html'>HDFS</a>, and <a href='https://aws.amazon.com/s3/'>Amazon S3</a>. Druid supports most popular file formats for structured and semi-structured data. |
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| <h5>Fast, consistent queries at high concurrency</h5> |
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| Druid has been <a href='https://imply.io/post/performance-benchmark-druid-presto-hive'>benchmarked</a> to greatly outperform legacy solutions. Druid combines novel storage ideas, indexing structures, and both exact and approximate queries to return most results in under a second. |
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| <h5>Broad applicability</h5> |
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| Druid <a href='/use-cases'>unlocks new types of queries and workflows</a> for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data. |
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| 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. |
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| Druid is proven in production at the <a href='/druid-powered'>world’s leading companies</a> at massive scale. |
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| Get help from a <a href='/community/'>wide network of community members</a> about using Druid. |
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| <span class ="title">Upstream Processing with Apache Flink for Immediate Intelligence</span><br> |
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| <a href="https://blog.hellmar-becker.de/2022/03/20/druid-data-cookbook-quantiles-in-druid-with-datasketches/"> |
| <span class="title">Druid Data Cookbook: Quantiles in Druid with Data Sketches</span><br> |
| <span class="text-muted">Hellmar Becker - </span> |
| <span class="text-muted">Imply</span><br> |
| <span class="text-muted">Mar 20 2022</span> |
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| <a href="https://blog.hellmar-becker.de/2022/02/09/druid-data-cookbook-ingestion-transforms/"> |
| <span class="title">Druid Data Cookbook: Ingestion Transforms</span><br> |
| <span class="text-muted">Hellmar Becker - </span> |
| <span class="text-muted">Imply</span><br> |
| <span class="text-muted">Feb 9 2022</span> |
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| <a href="https://imply.io/blog/multi-dimensional-range-partitioning/"> |
| <span class="title">Multi-dimensional range partitioning</span><br> |
| <span class="text-muted">Kashif Faraz - </span> |
| <span class="text-muted">Imply</span><br> |
| <span class="text-muted">Feb 4 2022</span> |
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| <a href="https://www.rilldata.com/blog/seeking-the-perfect-apache-druid-rollup"> |
| <span class="title">Seeking the Perfect Apache Druid Rollup</span><br> |
| <span class="text-muted">Neil Buesing - </span> |
| <span class="text-muted">Rill Data</span><br> |
| <span class="text-muted">Dec 16 2021</span> |
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| <a href="https://www.confluent.io/blog/scaling-apache-druid-for-real-time-cloud-analytics-at-confluent/"> |
| <span class="title">Scaling Apache Druid for Real-Time Cloud Analytics at Confluent</span><br> |
| <span class="text-muted">Harini Rajendran, Zohreh Krimi - </span> |
| <span class="text-muted">Confluent</span><br> |
| <span class="text-muted">Nov 8 2021</span> |
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| <a href="https://github.com/apache/druid/releases/tag/druid-0.22.1"> |
| <span class="title">Apache Druid 0.22.1 Released</span><br> |
| <span class="text-muted">Dec 11 2021</span> |
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| <a href="https://github.com/apache/druid/releases/tag/druid-0.21.1"> |
| <span class="title">Apache Druid 0.21.1 Released</span><br> |
| <span class="text-muted">Jun 10 2021</span> |
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