QuestDB is a high performance, open-source, time-series database
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Updated
Sep 17, 2026 - Java
QuestDB is a high performance, open-source, time-series database
flink learning blog. http://www.54tianzhisheng.cn/ 含 Flink 入门、概念、原理、实战、性能调优、源码解析等内容。涉及 Flink Connector、Metrics、Library、DataStream API、Table API & SQL 等内容的学习案例,还有 Flink 落地应用的大型项目案例(PVUV、日志存储、百亿数据实时去重、监控告警)分享。欢迎大家支持我的专栏《大数据实时计算引擎 Flink 实战与性能优化》
Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
Upserts, Deletes And Incremental Processing on Big Data.
Stream Processing and Complex Event Processing Engine
A microservices-based Streaming and Batch data processing in Cloud Foundry and Kubernetes
Distributed Stream and Batch Processing
Framework for building Event-Driven Microservices
Apache StreamPipes - A self-service (Industrial) IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams.
An extensible Java framework for building event-driven applications that break up XML and non-XML data into chunks for data integration
The database purpose-built for stream processing applications.
Elastic data processing with Apache Pulsar and Apache Flink
Dagger is an easy-to-use, configuration over code, cloud-native framework built on top of Apache Flink for stateful processing of real-time streaming data.
Source code for the Kafka Streams in Action Book
An online code compiler supporting 11 programming languages (Java, Kotlin, Scala, C, C++, C#, Golang, Python, Ruby, Rust and Haskell) for competitive programming and coding interviews.
This is the central repository for all materials related to Kafka Streams : Real-time Stream Processing! Book by Prashant Pandey.
Stream Discovery and Stream Orchestration
Scalable stream processing platform for advanced realtime analytics on top of Kafka and Spark. LogIsland also supports MQTT and Kafka Streams (Flink being in the roadmap). The platform does complex event processing and is suitable for time series analysis. A large set of valuable ready to use processors, data sources and sinks are available.
Simple examle for Spark Streaming over Kafka topic
A high-throughput, consistent ticket reservation system that can process 83000+ reservations per second
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