Sub-second
Apache Druid is a high-performance, real-time analytics database purpose-built for fast, ad hoc exploration of massive datasets.
It combines streaming and historical data ingestion with sub-second query response times, scaling from terabytes to petabytes and supporting thousands of concurrent users.
That’s why global enterprises rely on Druid to power dashboards, anomaly detection, self-service BI, and user-facing analytics applications.
Apache Druid offers powerful solutions for organizations that need fast, scalable, and real-time analytics.
Unlike Lambda-style architectures, Druid reduces complexity and cost while supporting interactive apps, dashboards, and real-time visibility into live data.
Sub-second queries, even with thousands of concurrent users.
Unified support for batch and real-time ingestion.
Adaptable to many industries and use cases.
Backed by the Apache Software Foundation and an engaged developer ecosystem.
Load data from Kafka, Kinesis, HDFS, S3, GCS, or Azure and query it instantly.
Replace batch + streaming silos with a unified platform, lowering infrastructure overhead.
Support self-service BI and customer-facing analytics with sub-second response times, even on massive datasets.
Monitor events, detect anomalies, and act on insights as they happen.
Apache Druid follows a distributed, service-oriented architecture with specialized node types, each responsible for a different function:
This architecture ensures scalability, high concurrency, and fault tolerance, making Druid equally effective for real-time and historical analytics.
Apache Druid helps organizations across industries turn fast-moving data into instant insights:
If your team needs expert support with installation, tuning, troubleshooting, or monitoring Apache Druid — contact us and get help within 24 hours or less.
Druid is the right choice if your workloads include:
It powers real-time analytics for dashboards, monitoring systems, fraud detection, and BI apps.
Druid specializes in sub-second interactive queries, while Presto, Hive, and Spark are optimized for batch or slower analytics.
Yes. Druid ingests seamlessly from Kafka, Kinesis, S3, GCS, Azure, and HDFS.
Ad tech, finance, gaming, SaaS, retail, telecom, and security — especially for real-time dashboards and anomaly detection.
Teams with large-scale, fast-moving data that need instant insights and high concurrency.
Apache Druid is a strong fit if you need high streaming insert rates (Kafka, Kinesis), handle very large data volumes (TB → PB), require thousands of concurrent queries, or need low-latency aggregations on time-series or high-cardinality data (user IDs, URLs, events).
