Get to know Apache Druid – one of the most powerful DB's in the world.

Get Started with Apache Druid Installation

What is Apache Druid?

Apache Druid is a powerful analytics database that enables real-time, ad hoc exploration of massive amounts of live and historical data at any scale. It has delivered sub-second response times against hundreds of petabytes of data and hundreds of millions of events per second.

258x

faster than Hive

68x

faster than Presto

Why Choose Apache Druid?

Apache Druid offers powerful solutions for organizations needing fast, scalable, and real-time analytics capabilities.

Key Benefits
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1. Interactive Analytics – Delivers immediate results with concurrent user queries
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2. Scalable Kappa Architecture – Seamlessly integrates batch and real-time data processing
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3. Flexibility and Extensibility – Customizable to fit a wide range of use cases
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4. Open-Source with Active Community – Supported by a vibrant community driving continuous innovation

Apache Druid's architecture and features directly address the challenges of scaling analytics for a large number of users. It reduces the complexity and costs associated with traditional Lambda architectures, supports interactive applications with low-latency queries, and provides real-time visibility into streaming data.

Key Features
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1. Scalable Data-Driven Approach

Apache Druid excels in real-time data ingestion and querying capabilities. It supports high concurrency and low-latency queries, making it suitable for a large number of analytics users, both internal and external. This ensures that as more users engage with analytics, Druid can efficiently handle the scale and demand.

2. Simplicity of Design and Cost Reduction

Druid's architecture consolidates both real-time and historical data into one system, eliminating the need to maintain a separate Lambda-like architecture for batch and real-time processing. This simplicity reduces operational overhead and lowers infrastructure and maintenance costs.

3. Support for Interactive Applications

Modern applications require responsiveness and low query latency for interactive data exploration and reporting. Druid's ability to handle sub-second query response times, even with large volumes of data, ensures that interactive applications can provide instant insights to users without significant delays.

4. Real-Time Data Visibility

Druid supports real-time data ingestion, enabling organizations to immediately see what is happening. This capability is crucial for monitoring live events, detecting anomalies, and providing up-to-date insights for operational decision-making.

Industries Using Apache Druid

Apache Druid helps tech teams across industries make sense of fast-moving data. From streaming platforms to financial systems, it’s used to power real-time dashboards, track user behavior, and monitor systems at scale – with sub-second speed.

Used in:

1. Technology & SaaS – Observability, customer-facing analytics, lakehouse integration
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2. Retail & E‑Commerce – Sales performance, merchandising, operational dashboards
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3. Advertising & Media – Real-time campaign metrics, audience forecasting

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4. Gaming & Social Platforms – Player analytics, fraud detection, creator insights
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5. Financial Services – Transaction analytics, billing intelligence, compliance
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6. Telecommunications & IoT – High-ingest device and network telemetry

7. Security & Risk Analytics – Threat detection, anomaly scoring, fraud prevention

Apache Druid Use Cases

Druid is likely a good choice if your use case fits a few of the following:

Input Data

● Insert rates are very high, but updates are less common.
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● You want to load streaming data from sources like Apache Kafka (Druid supports exactly once semantics) or Amazon Kinesis, or batch data from HDFS, flat files, or an object storage like Amazon S3, Google Cloud Storage or Azure Storage.

Scale of Data

● You have very large data volumes, from many terabytes to petabytes.

● You have a large number of concurrent users, such as operational staff across your company, or end customers.

Data Queries

● Most of your queries are aggregation and reporting queries ("group by" queries). You may also have searching and scanning queries.

● You are targeting query latencies of 100ms to a few seconds, such as to power a user-facing application where you want your users to be able to self-service iterative ad hoc queries (i.e. data exploration), perhaps through a visual interface.

Types of Data

● Your data has a time component. Druid includes optimizations and design choices specifically related to time.

● You have high cardinality data columns (e.g. URLs, user IDs) and need fast counting and ranking over them.