Spark Streaming
Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability.
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Red Hat OpenShift Streams
Red Hat® OpenShift® Streams for Apache Kafka is a managed cloud service that provides a streamlined developer experience for building, deploying, and scaling new cloud-native applications or modernizing existing systems. Red Hat OpenShift Streams for Apache Kafka makes it easy to create, discover, and connect to real-time data streams no matter where they are deployed. Streams are a key component for delivering event-driven and data analytics applications. The combination of seamless operations across distributed microservices, large data transfer volumes, and managed operations allows teams to focus on team strengths, speed up time to value, and lower operational costs. OpenShift Streams for Apache Kafka includes a Kafka ecosystem and is part of a family of cloud services—and the Red Hat OpenShift product family—which helps you build a wide range of data-driven solutions.
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Spring Cloud Data Flow
Microservice-based streaming and batch data processing for Cloud Foundry and Kubernetes. Spring Cloud Data Flow provides tools to create complex topologies for streaming and batch data pipelines. The data pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a range of data processing use cases, from ETL to import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server uses Spring Cloud Deployer, to deploy data pipelines made of Spring Cloud Stream or Spring Cloud Task applications onto modern platforms such as Cloud Foundry and Kubernetes. A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation. Custom stream and task applications, targeting different middleware or data services, can be built using the familiar Spring Boot style programming model.
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Elecard Boro
Elecard Boro is a professional software solution for real-time video stream health monitoring and Quality of Service (QoS) & Quality of Experience (QoE) tracking across distributed networks. Designed for broadcasters, OTT providers, and IPTV operators, Boro utilizes distributed software probes to instantly isolate quality degradation across the entire delivery chain.
How it works:
Probes analyze UDP, RTP, HTTP, HLS, DASH, SRT, and RTMP streams up to UHD. Multi-point measurements are aggregated on a centralized server, providing instant alerts for ETSI TR 101 290 errors via Email, SNMP, Webhook, PagerDuty, and Telegram.
Key Features:
• Rapid Start: Deploy probes on any hardware in 10–30 minutes
• Deep Diagnostics: Tracks 50+ QoS/QoE parameters, SCTE-35 ad cues, and captures PCAP streams
• Smart Dashboards: Visualizes service states and captures stream thumbnails
• Easy Integration: Access via an intuitive web interface and integrate using WebHook, SNMP, and ControlAPI
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