Best Real-Time Data Streaming Tools for Linux of 2025

Apache Kafka® is a robust, open-source platform designed for distributed streaming. It allows for the scaling of production clusters to accommodate up to a thousand brokers, handling trillions of messages daily and managing petabytes of data across hundreds of thousands of partitions. The system provides the flexibility to seamlessly expand or reduce storage and processing capabilities. It can efficiently stretch clusters over various availability zones or link distinct clusters across different geographical regions. Users can process streams of events through a variety of operations such as joins, aggregations, filters, and transformations, with support for event-time and exactly-once processing guarantees. Kafka features a Connect interface that readily integrates with numerous event sources and sinks, including technologies like Postgres, JMS, Elasticsearch, and AWS S3, among many others. Additionally, it supports reading, writing, and processing event streams using a wide range of programming languages, making it accessible for diverse development needs. This versatility and scalability ensure that Kafka remains a leading choice for organizations looking to harness real-time data streams effectively.

In the field of IoT stream processing and analytics, SQLstream ranks #1 according to ABI Research. Used by Verizon, Walmart, Cisco, and Amazon, our technology powers applications on premises, in the cloud, and at the edge. SQLstream enables time-critical alerts, live dashboards, and real-time action with sub-millisecond latency. Smart cities can reroute ambulances and fire trucks or optimize traffic light timing based on real-time conditions. Security systems can detect hackers and fraudsters, shutting them down right away. AI / ML models, trained with streaming sensor data, can predict equipment failures. Thanks to SQLstream's lightning performance -- up to 13 million rows / second / CPU core -- companies have drastically reduced their footprint and cost. Our efficient, in-memory processing allows operations at the edge that would otherwise be impossible. Acquire, prepare, analyze, and act on data in any format from any source. Create pipelines in minutes not months with StreamLab, our interactive, low-code, GUI dev environment. Edit scripts instantly and view instantaneous results without compiling. Deploy with native Kubernetes support. Easy installation includes Docker, AWS, Azure, Linux, VMWare, and more

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. Memgraph's vibrant open-source community brings together over 150,000 developers in more than 100 countries to exchange ideas and optimize the next generation of in-memory data-driven applications across GenAI/ LLMs and real-time analytics performed with streaming data.

Apache Doris serves as an advanced data warehouse tailored for real-time analytics, providing exceptionally rapid insights into large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion, achieving this within a second, along with a storage engine capable of real-time updates, appends, and pre-aggregations. The platform is optimized for handling high-concurrency and high-throughput queries thanks to its columnar storage engine, MPP architecture, cost-based query optimizer, and vectorized execution engine. Moreover, it supports federated querying across various data lakes like Hive, Iceberg, and Hudi, as well as traditional databases such as MySQL and PostgreSQL. Doris also accommodates complex data types, including Array, Map, and JSON, and features a variant data type that allows for automatic inference of JSON data types. Additionally, it employs advanced indexing techniques like NGram bloomfilter and inverted index to enhance text search capabilities. With its distributed architecture, Doris enables linear scalability, incorporates workload isolation, and implements tiered storage to optimize resource management effectively. Furthermore, it is designed to support both shared-nothing clusters and the separation of storage and compute resources, making it a versatile solution for diverse analytical needs.

Introducing revolutionary data streaming features that enable unparalleled customer experiences. The Kafka API and its ecosystem are fully compatible with Redpanda, which boasts predictable low latencies and ensures zero data loss. Redpanda is designed to outperform Kafka by up to ten times, offering enterprise-level support and timely hotfixes. It also includes automated backups to S3 or GCS, providing a complete escape from the routine operations associated with Kafka. Additionally, it supports both AWS and GCP environments, making it a versatile choice for various cloud platforms. Built from the ground up for ease of installation, Redpanda allows for rapid deployment of streaming services. Once you witness its incredible capabilities, you can confidently utilize its advanced features in a production setting. We take care of provisioning, monitoring, and upgrades without requiring access to your cloud credentials, ensuring that sensitive data remains within your environment. Your streaming infrastructure will be provisioned, operated, and maintained seamlessly, with customizable instance types available to suit your specific needs. As your requirements evolve, expanding your cluster is straightforward and efficient, allowing for sustainable growth.

Insigna - The complete Platform for Real-time Analytics and Data Management. Insigna offers integration, automated processing, transformation, data preparation and real-time analytics to derive and deliver intelligence to various stakeholders. Insigna enables connectivity with the most popular network communication protocols, data stores, enterprise applications, and cloud platforms. Coupled with a rich set of out-of-the-box data transformation capabilities, enterprises greatly benefit from the opportunities offered by operations data generated in real-time.

Scale from zero to millions of events every second with Arroyo, which is delivered as a single, streamlined binary. It can be run locally on either MacOS or Linux for development purposes and easily deployed to production using Docker or Kubernetes. Arroyo represents a revolutionary approach to stream processing, specifically designed to simplify real-time operations compared to traditional batch processing. From its inception, Arroyo has been crafted so that anyone familiar with SQL can create dependable, efficient, and accurate streaming pipelines. This empowers data scientists and engineers to develop comprehensive real-time applications, models, and dashboards without needing a dedicated team of streaming specialists. Users can perform transformations, filtering, aggregation, and joining of data streams simply by writing SQL, achieving results in under a second. Furthermore, your streaming pipelines shouldn’t trigger alerts just because Kubernetes opted to reschedule your pods. With the capability to operate in contemporary, elastic cloud environments, Arroyo is suitable for everything from basic container runtimes like Fargate to extensive, distributed systems managed by Kubernetes. This versatility makes Arroyo an ideal choice for organizations looking to optimize their streaming data processes.