Build real-time analytics on Confluent Cloud in minutes
Turn your data in motion into analytics in motion with Imply: the real-time analytics platform built from Apache Druid.
Trusted by leading organizations
Building on Apache Kafka and Druid
Apache Druid is designed for rapid ingestion and immediate querying of stream data. Whether you’re ingesting thousands or millions of events per second, Druid delivers exactly-once ingestion and subsecond latency for data streams without needing a Kafka connector. When existing databases and legacy analytics stacks fail to meet real-time requirements, Druid is the answer.
Get started with Confluent Cloud and Imply Polaris
Together, Imply Polaris and Confluent Cloud provide a complete, fully-managed, cloud-native data architecture for real-time analytics applications at any scale. Get the full power of Kafka and Druid without the production risk and infrastructure management, while accelerating time to value for real-time analytics use cases.
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Analyzing streaming data with Imply
Imply is purpose-built for stream ingestion. It ingests event-by-event, not a series of batched data files sent sequentially to mimic a stream. This means that Imply supports query-on-arrival. It’s true real-time analytics with no wait for data to be batched and then delivered.
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Event-based ingestion
Unlike systems that rely on periodic batch processing, Druid’s event-based ingestion enables data to be ingested and processed as soon as events occur.
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Query-on-arrival
Druid provides instantaneous access to streaming data, enabling individuals and/or applications to query data as soon as it enters the stream.
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High EPS scalability
Druid handles data streams up to millions of events per second with ease, ideal for highly dynamic data.
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Auto schema discovery
Druid automatically discerns the fields and types of data ingested, updating tables to align with evolving data.
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Guaranteed consistency
Druid guarantees data consistency—preventing duplicates or data loss—through its native indexing service.
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Continuous backup
Druid ensures no data loss of streaming data as it persists data segments to deep storage automatically.
Real-time analytics use cases
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Application Observability
Analyze and capitalize on events such as clicks, telemetry, logs, and metrics from applications—while the data is fresh.
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Security/Fraud Analytics
Investigate anomalies, identify unusual patterns, and prevent or mitigate security attacks in real time.
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Customer-facing Analytics
Build real-time data products that deliver valuable insights into product performance, user behavior, billing, and more.
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Real-time Decisioning
Build real-time workflows for applications that rely on machines to make decisions or predict outcomes automatically.
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Product Analytics
Create a holistic view of user patterns, better understand product weaknesses and strengths, and build a better experience.
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IoT / Telemetry Analytics
Understand usage patterns, predict shifts in customer behavior, automate routine tasks, and design the next generation of products.
Learn more about IoT/telemetry analytics