Real-time analytics at scale powered by Apache Druid for sub-second, high-concurrency queries.
Imply is a cloud-native real-time analytics platform built by the original creators of Apache Druid. In 2026, it remains the market leader for operational analytics, specializing in high-concurrency workloads where sub-second query responses are required on multi-petabyte datasets. The architecture centers around Imply Polaris, a fully managed Database-as-a-Service (DBaaS) that abstracts the complexity of Druid management while offering integrated data visualization through Imply Pivot. Technically, Imply utilizes a unique storage-compute separation combined with a multi-stage query (MSQ) engine, allowing it to bridge the gap between low-latency streaming ingestion (from Kafka or Kinesis) and high-throughput batch processing. Its 2026 market position is solidified by its 'total observability' suite, which integrates deeply with AI/ML pipelines to provide real-time feedback loops for model performance and automated fraud detection systems. By providing a serverless experience for Druid, Imply enables enterprises to transition from legacy, slow-moving data warehouses to proactive, event-driven decision engines.
A distributed query execution engine that allows Druid to handle complex joins and heavy batch transformations using SQL.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
An integrated visualization layer designed specifically for multidimensional exploratory analysis with zero lag.
Background processes that automatically merge and optimize data segments to improve query speed and reduce storage costs.
Exactly-once ingestion semantics directly from Kafka topics without intermediate connectors.
A feature that summarizes high-cardinality data during ingestion to reduce storage footprint while maintaining analytical utility.
Support for multi-region data replication to ensure high availability and low-latency access for global users.
Uses standard SQL to define complex logical conditions across streaming data for automated response.
Ad networks need to analyze billions of bid requests per second to optimize auction floor prices.
Registry Updated:2/7/2026
Detecting DDoS attacks or lateral movement in high-volume VPC flow logs.
Providing end-users with interactive, real-time dashboards of their own data within an app.