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| <h1>Apache<span style="vertical-align: super; font-size: 0.5em;">®</span> Druid</h1> |
| <p class="lead">Druid is a high performance, real-time analytics database that delivers sub-second queries on streaming and batch data at scale and under load.</p> |
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| <h2> |
| Overview |
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| <h5>Sub-second queries at any scale</h5> |
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| Execute OLAP queries in milliseconds on high-cardinality and high-dimensional data sets with billions to trillions of rows without pre-defining or caching queries in advance. |
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| <h5>High concurrency at the lowest cost </h5> |
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| Build real-time analytics applications that supports 100s to 100,000s queries per second at consistent performance with a highly efficient architecture that uses less infrastructure than other databases. |
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| <h5>Real-time and historical insights</h5> |
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| Unlock streaming data potential through Druid's native integration with Apache Kafka and Amazon Kinesis as it supports query-on-arrival at millions of events per second, low latency ingestion, and guaranteed consistency. |
| </p> |
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| <h2> |
| Key Druid Features |
| </h2> |
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| <h5 class="card-title">Interactive Query Engine</h5> |
| </div> |
| <div class="card-body pt-0"> |
| <p>Druid utilizes scatter/gather for high speed queries with data preloaded into memory or local storage to avoid data movement and network latency</p> |
| </div> |
| </div> |
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| <h5 class="card-title">Tiering & QoS</h5> |
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| <div class="card-body pt-0"> |
| <p>Configurable tiering with quality of service enables the ideal price-performance for mixed workloads, guarantees priority, and avoids resource contention</p> |
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| <h5 class="card-title">Optimized Data Format</h5> |
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| <div class="card-body pt-0"> |
| <p>Ingested data is automatically columnarized, time indexed, dictionary encoded, bitmap indexed, and type-aware compressed</p> |
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| <h5 class="card-title">Elastic Architecture</h5> |
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| <div class="card-body pt-0"> |
| <p>Loosely coupled components for ingestion, queries and orchestration combined with a deep storage layer enable easy & quick scale-up & scale-out</p> |
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| <h5 class="card-title">True Stream Ingestion</h5> |
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| <div class="card-body pt-0"> |
| <p>A connector-free integration with streaming platforms enables query-on-arrival, high scalability, low latency, and guaranteed consistency</p> |
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| <h5 class="card-title">Non-stop Reliability</h5> |
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| <p>Automatic data services including continuous backup, automated recovery, and multi-node replication ensure high availability and durability</p> |
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| Learn more |
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| <h5>Powered By</h5> |
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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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| <h5>FAQ</h5> |
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| Learn about some of the <a href='faq'>most common questions about Druid</a>. |
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| <h5>Quickstart</h5> |
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| <a href="/docs/latest/tutorials/quickstart">Get started with Druid</a> in minutes. Load your own data and query it. |
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| <h5>Get Help</h5> |
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| Get help from a <a href='/community/'>wide network of community members</a> about using Druid. |
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| <h5>Podcast</h5> |
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| Hear from the Druid community on <a href="https://podcasts.apple.com/us/podcast/tales-at-scale/id1655951714">Apple</a>, <a href="https://open.spotify.com/show/6KAKYLJvCVegsFfKvbfDnt">Spotify</a>, and <a href="https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5saWJzeW4uY29tLzQ0ODE3OS9yc3M">Google</a>. |
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| Featured Content |
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| <p> |
| <a href="https://blog.hellmar-becker.de/2023/03/07/selective-bulk-upserts-in-apache-druid/"> |
| <span class="title">Selective Bulk Upserts in Apache Druid</span><br> |
| <span class="text-muted">Hellmar Becker</span><br> |
| <span class="text-muted">Mar 7 2023</span> |
| </a> |
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| <a href="https://imply.io/blog/real-time-analytics-database-uses-partitioning-and-pruning-to-achieve-its-legendary-performance/"> |
| <span class="title">Primary and secondary partitioning</span><br> |
| <span class="text-muted">Sergio Ferragut</span><br> |
| <span class="text-muted">Jan 27 2023</span> |
| </a> |
| </p> |
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| <a href="https://devops.com/stream-big-think-bigger-analyze-streaming-data-at-scale/"> |
| <span class="title">Using Apache Druid for analyzing streaming data</span><br> |
| <span class="text-muted">Julia Brouillette</span><br> |
| <span class="text-muted">Jan 27 2023</span> |
| </a> |
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| <p> |
| <a href="https://www.youtube.com/watch?v=Bozxc3vP1PA"> |
| <span class="title">Why Confluent analyzes Kafka streams with Druid</span><br> |
| <span class="text-muted">Matt Armstrong</span><br> |
| <span class="text-muted">Dec 15 2022</span> |
| </a> |
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| <a href="https://imply.io/blog/native-support-for-semi-structured-data-in-apache-druid/"> |
| <span class="title">Support for nested JSON columns in Druid</span><br> |
| <span class="text-muted">Karthik Kasibhatla</span><br> |
| <span class="text-muted">Dec 14 2022</span> |
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| Latest releases |
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| <a href="https://github.com/apache/druid/releases/tag/druid-25.0.0"> |
| <span class="title">Apache Druid 25.0.0 Released</span><br> |
| <span class="text-muted">Jan 4 2023</span> |
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| <a href="https://github.com/apache/druid/releases/tag/druid-24.0.2"> |
| <span class="title">Apache Druid 24.0.2 Released</span><br> |
| <span class="text-muted">Dec 21 2022</span> |
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| <a href="https://github.com/apache/druid/releases/tag/druid-24.0.1"> |
| <span class="title">Apache Druid 24.0.1 Released</span><br> |
| <span class="text-muted">Nov 21 2022</span> |
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