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The world’s leading companies use Hazelcast to modernize applications and take instant action on data in motion to create new revenue streams, mitigate risk, and operate more efficiently. Businesses use Hazelcast’s unified real-time data platform to process streaming data, enrich it with historical context and take instant action with standard or machine learning/AI-driven automation - before it is stored in a database or data lake.

Hazelcast is named in the Gartner Market Guide to Event Stream Processing and a leader in the GigaOm Radar Report for Streaming Data Platforms. To join our community of CXOs, architects, and developers at brands such as Lowe’s, HSBC, JPMorgan Chase, Volvo, New York Life, and others, visit


Instantaneously unifies real-time data streams with context from data at rest to create intelligent applications that transform revenue, cost and risk to achieve competitive advantage

Use Hazelcast for:

  • Stateful data processing such as aggregations, joins, windows and querying directly over streaming data or data at rest

  • Ingesting data through a comprehensive library of connectors for automatic data integration

  • Serving data to microservices using low-latency queries or push updates

  • Messaging and dependency graphs to wire complex apps

  • Distributed coordination of your microservices

  • Replicating data from one region to another or between data centers in the same region

  • Fast access to contextual data via smart caching

Hazelcast features:

  • Stateful and fault-tolerant data processing and querying  over data streams and data at rest using SQL or pipelines

  • A comprehensive library of connectors such as Kafka, Hadoop, S3, RDBMS, JMS and many more

  • Distributed messaging using pub-sub and queues (Queue, Topic)

  • Distributed, partitioned, queryable key-value store with event listeners (Map) - which can also be used to store contextual data for enriching event streams with low latency

  • A production-ready Raft-implementation which allows lineralizable (CP) concurrency primitives (Lock, CountdownLatch, Semaphore) 

  • Tight integration for deploying machine learning models with Python to a data processing pipeline

  • Cloud-native, run everywhere architecture

  • Zero-downtime operations with rolling upgrades

  • At-least-once and exactly-once processing guarantees for stream processing pipelines

  • Data replication between data centers and geographic regions using WAN 

  • Microsecond performance for key-value point lookups and pub-sub

  • Unique data processing architecture results in 99.99% latency of under 10ms for streaming queries with millions of events per second (link to blog post).

  • Client libraries in Java, Python, Node.js, C#, C++ and Go


Presenting the right offer at the right time can boost conversions, but you need real-time capabilities to deliver on this objective.

Customers are more inclined to act on recommendations when their current mindset aligns with the business's offering. These recommendations encompass upselling, cross-selling, personalized ads, and tailored content based on customer behavior. Identifying immediate behaviors and intents through ongoing interactions enhances the likelihood of successful recommendations. See how adding real-time stream processing to promote offers at the right time boosted conversion rates by 400%. 


 In today's landscape, payment processing systems are critical data-driven operations that demand the utmost speed and reliability in their underlying software systems.

Payment processing systems enable businesses to accept payments through credit cards, debit cards, checks, and digital wallets. These systems verify transaction details and then transmit the data for financial processing. The payment company conducts anti-fraud checks before finalizing the transaction, all of which must occur before clearance, even while the customer waits. A global bank rolled out a highly scalable, cross-border payment system with 1000s of payments per seconds


As our digital presence grows more interconnected and exploitable, fraud remains a significant business challenge. Hazelcast offers real-time fraud detection solutions that instantly identify fraud to prevent financial losses.

Fraud detection systems play a pivotal role in providing a comprehensive view of entities, relationships, and concealed patterns within an enterprise ecosystem. They combat an array of financial crimes, spanning payment fraud, credit card fraud, money laundering, and support for criminal activities. Neglecting ongoing enhancement of a fraud detection system can have dire consequences for customers, investors, compliance teams, and a company's financial health. Hazelcast powered real-time fraud detection saving a company $100M

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