Compare the Top Real-Time Data Streaming Tools for Linux as of June 2025

What are Real-Time Data Streaming Tools for Linux?

Real-time data streaming tools enable organizations, big data and machine learning professionals, and data scientists to stream data in real time, and build data models when new data is created or ingested. Compare and read user reviews of the best Real-Time Data Streaming tools for Linux currently available using the table below. This list is updated regularly.

  • 1
    SQLstream

    SQLstream

    Guavus, a Thales company

    SQLstream ranks #1 for IoT stream processing & analytics (ABI Research). Used by Verizon, Walmart, Cisco, & Amazon, our technology powers applications across data centers, the cloud, & the edge. Thanks to sub-ms latency, SQLstream enables live dashboards, time-critical alerts, & real-time action. Smart cities can optimize traffic light timing or reroute ambulances & fire trucks. Security systems can shut down hackers & fraudsters right away. AI / ML models, trained by streaming sensor data, can predict equipment failures. With lightning performance, up to 13M rows / sec / CPU core, companies have drastically reduced their footprint & cost. Our efficient, in-memory processing permits operations at the edge that are otherwise impossible. Acquire, prepare, analyze, & act on data in any format from any source. Create pipelines in minutes not months with StreamLab, our interactive, low-code GUI dev environment. Export SQL scripts & deploy with the flexibility of Kubernetes.
  • 2
    Memgraph

    Memgraph

    Memgraph

    Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, and criminal risk assessment), 360-degree data and network exploration (Identity and Access Management (IAM), Master Data Management (MDM), Bill of Materials (BOM)), and logistics and network optimization.
  • 3
    Redpanda

    Redpanda

    Redpanda Data

    Breakthrough data streaming capabilities that let you deliver customer experiences never before possible. Kafka API and ecosystem are compatible. Redpanda BulletPredictable low latencies with zero data loss. Redpanda BulletUpto 10x faster than Kafka. Redpanda BulletEnterprise-grade support and hotfixes. Redpanda BulletAutomated backups to S3/GCS. Redpanda Bullet100% freedom from routine Kafka operations. Redpanda BulletSupport for AWS and GCP. Redpanda was designed from the ground up to be easily installed to get streaming up and running quickly. After you see its power, put Redpanda to the test in production. Use the more advanced Redpanda features. We manage provisioning, monitoring, and upgrades. Without any access to your cloud credentials. Sensitive data never leaves your environment. Provisioned, operated, and maintained for you. Configurable instance types. Expand cluster as your needs grow.
  • 4
    Arroyo

    Arroyo

    Arroyo

    Scale from zero to millions of events per second. Arroyo ships as a single, compact binary. Run locally on MacOS or Linux for development, and deploy to production with Docker or Kubernetes. Arroyo is a new kind of stream processing engine, built from the ground up to make real-time easier than batch. Arroyo was designed from the start so that anyone with SQL experience can build reliable, efficient, and correct streaming pipelines. Data scientists and engineers can build end-to-end real-time applications, models, and dashboards, without a separate team of streaming experts. Transform, filter, aggregate, and join data streams by writing SQL, with sub-second results. Your streaming pipelines shouldn't page someone just because Kubernetes decided to reschedule your pods. Arroyo is built to run in modern, elastic cloud environments, from simple container runtimes like Fargate to large, distributed deployments on the Kubernetes logo Kubernetes.
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