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.
Imply is a cloud-native real-time analytics platform built by the original creators of Apache Druid.
Explore all tools that specialize in perform real-time analytics. This domain focus ensures Imply delivers optimized results for this specific requirement.
Explore all tools that specialize in olap querying. This domain focus ensures Imply delivers optimized results for this specific requirement.
A distributed query execution engine that allows Druid to handle complex joins and heavy batch transformations using SQL.
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.
Sign up for an Imply Polaris account and create a project in your preferred cloud region.
Define a Connection to your data source (e.g., Apache Kafka, Amazon Kinesis, or S3).
Create a Table by defining the schema, including dimensions and primary timestamp.
Configure the Ingestion Job, mapping source fields to Druid columns and setting up data roll-up.
Define 'Granularity' settings to optimize storage and query performance.
Launch the ingestion task and monitor the 'Real-time Segments' being built.
Use the built-in SQL workbench to run exploratory queries on the live data.
Design a Data Cube in Imply Pivot for drag-and-drop visualization.
Configure Alerting rules to trigger notifications when specific metrics exceed thresholds.
Deploy the dashboard or integrate the query API into your production application.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform for its unmatched speed on large datasets and ease of use compared to raw Druid, though some note the cost can scale quickly with high data volume."
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