{"id":55676,"date":"2025-12-31T04:33:58","date_gmt":"2025-12-31T04:33:58","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=55676"},"modified":"2026-02-21T08:44:36","modified_gmt":"2026-02-21T08:44:36","slug":"top-10-stream-processing-frameworks-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/top-10-stream-processing-frameworks-features-pros-cons-comparison\/","title":{"rendered":"Top 10 Stream Processing Frameworks: Features, Pros, Cons & Comparison"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Introduction<\/h2>\n\n\n\n

Stream Processing Frameworks are software platforms designed to process, analyze, and react to continuous streams of data in real time<\/strong>. Unlike traditional batch processing systems that work on stored datasets, stream processing tools handle data as it arrives<\/strong>, enabling instant insights, decisions, and actions.<\/p>\n\n\n\n

In today\u2019s digital world, data is generated continuously from applications, sensors, user interactions, financial transactions, logs, and connected devices. Businesses rely on stream processing frameworks to detect anomalies, power real-time dashboards, trigger alerts, personalize user experiences, and automate operational workflows<\/strong>. Without stream processing, organizations risk delayed insights, missed opportunities, and slower response times.<\/p>\n\n\n\n

Why Stream Processing Frameworks Are Important<\/h3>\n\n\n\n
    \n
  • Enable real-time analytics and decision-making<\/strong><\/li>\n\n\n\n
  • Support high-throughput, low-latency data pipelines<\/strong><\/li>\n\n\n\n
  • Improve operational visibility and responsiveness<\/strong><\/li>\n\n\n\n
  • Power modern use cases like fraud detection, IoT analytics, and live monitoring<\/li>\n<\/ul>\n\n\n\n

    Key Real-World Use Cases<\/h3>\n\n\n\n
      \n
    • Real-time fraud detection in banking and payments<\/li>\n\n\n\n
    • Monitoring application logs and system metrics<\/li>\n\n\n\n
    • Live personalization and recommendation engines<\/li>\n\n\n\n
    • IoT sensor data processing<\/li>\n\n\n\n
    • Event-driven microservices and automation<\/li>\n<\/ul>\n\n\n\n

      What to Look for When Choosing a Stream Processing Framework<\/h3>\n\n\n\n

      When evaluating stream processing tools, consider:<\/p>\n\n\n\n

        \n
      • Latency and performance<\/strong> under heavy data loads<\/li>\n\n\n\n
      • State management and fault tolerance<\/strong><\/li>\n\n\n\n
      • Ease of development and learning curve<\/strong><\/li>\n\n\n\n
      • Integration with data sources and sinks<\/strong><\/li>\n\n\n\n
      • Scalability and deployment flexibility<\/strong><\/li>\n\n\n\n
      • Security, governance, and compliance support<\/strong><\/li>\n<\/ul>\n\n\n\n

        Best for:<\/strong>
        Stream Processing Frameworks are ideal for data engineers, backend developers, platform engineers, DevOps teams, and data architects<\/strong> working in startups, SMBs, and large enterprises across industries such as finance, e-commerce, telecom, healthcare, logistics, and IoT<\/strong>.<\/p>\n\n\n\n

        Not ideal for:<\/strong>
        These tools may be unnecessary for teams that only run periodic batch analytics<\/strong>, have small static datasets<\/strong>, or lack the technical resources to manage distributed systems. In such cases, simpler batch processing or managed analytics solutions may be more appropriate.<\/p>\n\n\n\n

        \n\n\n\n

        Top 10 Stream Processing Frameworks Tools<\/h2>\n\n\n\n
        \n\n\n\n

        1 \u2014 Apache Kafka Streams<\/h3>\n\n\n\n

        Short description:<\/strong>
        Apache Kafka Streams is a client library for building real-time stream processing applications directly on top of Kafka. It is designed for developers who want lightweight, embedded stream processing without managing a separate cluster.<\/p>\n\n\n\n

        Key features:<\/strong><\/p>\n\n\n\n

          \n
        • Native integration with Apache Kafka<\/li>\n\n\n\n
        • Stateful and stateless stream processing<\/li>\n\n\n\n
        • Exactly-once processing semantics<\/li>\n\n\n\n
        • Built-in fault tolerance via Kafka<\/li>\n\n\n\n
        • Windowing and stream joins<\/li>\n\n\n\n
        • Embedded deployment model<\/li>\n<\/ul>\n\n\n\n

          Pros:<\/strong><\/p>\n\n\n\n

            \n
          • Simple architecture with no separate cluster<\/li>\n\n\n\n
          • Strong reliability and consistency guarantees<\/li>\n<\/ul>\n\n\n\n

            Cons:<\/strong><\/p>\n\n\n\n

              \n
            • Limited to Kafka-based ecosystems<\/li>\n\n\n\n
            • Less suitable for very complex analytics pipelines<\/li>\n<\/ul>\n\n\n\n

              Security & compliance:<\/strong>
              Supports encryption in transit, authentication, and authorization through Kafka security features.<\/p>\n\n\n\n

              Support & community:<\/strong>
              Strong open-source community, extensive documentation, and enterprise support via Kafka vendors.<\/p>\n\n\n\n

              \n\n\n\n

              2 \u2014 Apache Flink<\/h3>\n\n\n\n

              Short description:<\/strong>
              Apache Flink is a high-performance stream processing framework designed for low-latency, stateful computations<\/strong> at massive scale. It is widely used for advanced real-time analytics.<\/p>\n\n\n\n

              Key features:<\/strong><\/p>\n\n\n\n

                \n
              • True streaming (not micro-batching)<\/li>\n\n\n\n
              • Advanced state management with checkpoints<\/li>\n\n\n\n
              • Event-time processing and watermarks<\/li>\n\n\n\n
              • Exactly-once guarantees<\/li>\n\n\n\n
              • Batch and stream processing in one engine<\/li>\n\n\n\n
              • Rich APIs for Java, Scala, and Python<\/li>\n<\/ul>\n\n\n\n

                Pros:<\/strong><\/p>\n\n\n\n

                  \n
                • Excellent performance and low latency<\/li>\n\n\n\n
                • Highly reliable for mission-critical workloads<\/li>\n<\/ul>\n\n\n\n

                  Cons:<\/strong><\/p>\n\n\n\n

                    \n
                  • Steeper learning curve<\/li>\n\n\n\n
                  • Operational complexity for beginners<\/li>\n<\/ul>\n\n\n\n

                    Security & compliance:<\/strong>
                    Supports authentication, encryption, and role-based access depending on deployment.<\/p>\n\n\n\n

                    Support & community:<\/strong>
                    Large global community, detailed documentation, and strong enterprise adoption.<\/p>\n\n\n\n

                    \n\n\n\n

                    3 \u2014 Apache Spark Structured Streaming<\/h3>\n\n\n\n

                    Short description:<\/strong>
                    Apache Spark Structured Streaming extends Spark\u2019s batch engine to handle streaming workloads using a micro-batch model<\/strong>, making it accessible to existing Spark users.<\/p>\n\n\n\n

                    Key features:<\/strong><\/p>\n\n\n\n

                      \n
                    • Unified batch and stream processing<\/li>\n\n\n\n
                    • SQL and DataFrame-based APIs<\/li>\n\n\n\n
                    • Fault-tolerant processing<\/li>\n\n\n\n
                    • Integration with major data platforms<\/li>\n\n\n\n
                    • Scalable across clusters<\/li>\n\n\n\n
                    • Rich ecosystem support<\/li>\n<\/ul>\n\n\n\n

                      Pros:<\/strong><\/p>\n\n\n\n

                        \n
                      • Easy adoption for Spark users<\/li>\n\n\n\n
                      • Strong ecosystem and tooling<\/li>\n<\/ul>\n\n\n\n

                        Cons:<\/strong><\/p>\n\n\n\n

                          \n
                        • Higher latency compared to true streaming engines<\/li>\n\n\n\n
                        • Resource-intensive for small workloads<\/li>\n<\/ul>\n\n\n\n

                          Security & compliance:<\/strong>
                          Enterprise-grade security through Spark and platform integrations.<\/p>\n\n\n\n

                          Support & community:<\/strong>
                          Massive community, strong documentation, and enterprise backing.<\/p>\n\n\n\n

                          \n\n\n\n

                          4 \u2014 Apache Storm<\/h3>\n\n\n\n

                          Short description:<\/strong>
                          Apache Storm is one of the earliest distributed stream processing frameworks, designed for real-time computation with very low latency<\/strong>.<\/p>\n\n\n\n

                          Key features:<\/strong><\/p>\n\n\n\n

                            \n
                          • True real-time processing<\/li>\n\n\n\n
                          • Simple processing topology model<\/li>\n\n\n\n
                          • Horizontal scalability<\/li>\n\n\n\n
                          • Fault-tolerant design<\/li>\n\n\n\n
                          • Language-agnostic support<\/li>\n<\/ul>\n\n\n\n

                            Pros:<\/strong><\/p>\n\n\n\n

                              \n
                            • Extremely low latency<\/li>\n\n\n\n
                            • Proven in production environments<\/li>\n<\/ul>\n\n\n\n

                              Cons:<\/strong><\/p>\n\n\n\n

                                \n
                              • Limited modern features<\/li>\n\n\n\n
                              • Smaller ecosystem compared to newer tools<\/li>\n<\/ul>\n\n\n\n

                                Security & compliance:<\/strong>
                                Basic security features depending on deployment.<\/p>\n\n\n\n

                                Support & community:<\/strong>
                                Active but smaller community compared to Flink or Spark.<\/p>\n\n\n\n

                                \n\n\n\n

                                5 \u2014 Apache Samza<\/h3>\n\n\n\n

                                Short description:<\/strong>
                                Apache Samza is a stream processing framework tightly integrated with Kafka and designed for stateful, scalable stream processing<\/strong>.<\/p>\n\n\n\n

                                Key features:<\/strong><\/p>\n\n\n\n

                                  \n
                                • Kafka-native design<\/li>\n\n\n\n
                                • Strong state management<\/li>\n\n\n\n
                                • Fault tolerance via checkpoints<\/li>\n\n\n\n
                                • Container-based deployment<\/li>\n\n\n\n
                                • Simple processing model<\/li>\n<\/ul>\n\n\n\n

                                  Pros:<\/strong><\/p>\n\n\n\n

                                    \n
                                  • Reliable state handling<\/li>\n\n\n\n
                                  • Good fit for Kafka-heavy architectures<\/li>\n<\/ul>\n\n\n\n

                                    Cons:<\/strong><\/p>\n\n\n\n

                                      \n
                                    • Limited flexibility outside Kafka<\/li>\n\n\n\n
                                    • Smaller ecosystem<\/li>\n<\/ul>\n\n\n\n

                                      Security & compliance:<\/strong>
                                      Inherits Kafka security features.<\/p>\n\n\n\n

                                      Support & community:<\/strong>
                                      Moderate community and stable documentation.<\/p>\n\n\n\n

                                      \n\n\n\n

                                      6 \u2014 Google Cloud Dataflow<\/h3>\n\n\n\n

                                      Short description:<\/strong>
                                      Google Cloud Dataflow is a managed stream and batch processing service based on the Apache Beam programming model.<\/p>\n\n\n\n

                                      Key features:<\/strong><\/p>\n\n\n\n

                                        \n
                                      • Fully managed infrastructure<\/li>\n\n\n\n
                                      • Unified batch and streaming pipelines<\/li>\n\n\n\n
                                      • Auto-scaling and fault tolerance<\/li>\n\n\n\n
                                      • Strong integration with cloud services<\/li>\n\n\n\n
                                      • Event-time processing<\/li>\n<\/ul>\n\n\n\n

                                        Pros:<\/strong><\/p>\n\n\n\n

                                          \n
                                        • Minimal operational overhead<\/li>\n\n\n\n
                                        • Excellent scalability<\/li>\n<\/ul>\n\n\n\n

                                          Cons:<\/strong><\/p>\n\n\n\n

                                            \n
                                          • Vendor lock-in<\/li>\n\n\n\n
                                          • Costs can increase with scale<\/li>\n<\/ul>\n\n\n\n

                                            Security & compliance:<\/strong>
                                            Enterprise-grade cloud security and compliance options.<\/p>\n\n\n\n

                                            Support & community:<\/strong>
                                            Strong documentation and enterprise cloud support.<\/p>\n\n\n\n

                                            \n\n\n\n

                                            7\u2014 Azure Stream Analytics<\/h3>\n\n\n\n

                                            Short description:<\/strong>
                                            Azure Stream Analytics is a managed real-time analytics service optimized for IoT and event-driven applications<\/strong> on Azure.<\/p>\n\n\n\n

                                            Key features:<\/strong><\/p>\n\n\n\n

                                              \n
                                            • SQL-like query language<\/li>\n\n\n\n
                                            • Native Azure integrations<\/li>\n\n\n\n
                                            • Built-in windowing<\/li>\n\n\n\n
                                            • Real-time dashboards<\/li>\n\n\n\n
                                            • Managed scalability<\/li>\n<\/ul>\n\n\n\n

                                              Pros:<\/strong><\/p>\n\n\n\n

                                                \n
                                              • Easy to use for SQL users<\/li>\n\n\n\n
                                              • Strong IoT support<\/li>\n<\/ul>\n\n\n\n

                                                Cons:<\/strong><\/p>\n\n\n\n

                                                  \n
                                                • Limited flexibility for complex logic<\/li>\n\n\n\n
                                                • Azure-centric<\/li>\n<\/ul>\n\n\n\n

                                                  Security & compliance:<\/strong>
                                                  Strong enterprise and regulatory compliance support.<\/p>\n\n\n\n

                                                  Support & community:<\/strong>
                                                  Good documentation and enterprise-grade support.<\/p>\n\n\n\n

                                                  \n\n\n\n

                                                  8 \u2014 Amazon Kinesis Data Analytics<\/h3>\n\n\n\n

                                                  Short description:<\/strong>
                                                  Amazon Kinesis Data Analytics enables real-time stream processing using SQL or Apache Flink on AWS-managed infrastructure.<\/p>\n\n\n\n

                                                  Key features:<\/strong><\/p>\n\n\n\n

                                                    \n
                                                  • Managed Flink environments<\/li>\n\n\n\n
                                                  • SQL-based stream processing<\/li>\n\n\n\n
                                                  • Native AWS integrations<\/li>\n\n\n\n
                                                  • Auto-scaling<\/li>\n\n\n\n
                                                  • Fault tolerance<\/li>\n<\/ul>\n\n\n\n

                                                    Pros:<\/strong><\/p>\n\n\n\n

                                                      \n
                                                    • Easy integration with AWS ecosystem<\/li>\n\n\n\n
                                                    • Managed scalability<\/li>\n<\/ul>\n\n\n\n

                                                      Cons:<\/strong><\/p>\n\n\n\n

                                                        \n
                                                      • AWS lock-in<\/li>\n\n\n\n
                                                      • Cost management complexity<\/li>\n<\/ul>\n\n\n\n

                                                        Security & compliance:<\/strong>
                                                        Comprehensive cloud security and compliance controls.<\/p>\n\n\n\n

                                                        Support & community:<\/strong>
                                                        Strong enterprise support and growing community.<\/p>\n\n\n\n

                                                        \n\n\n\n

                                                        9 \u2014 Apache Beam<\/h3>\n\n\n\n

                                                        Short description:<\/strong>
                                                        Apache Beam is a unified programming model for defining batch and streaming pipelines that can run on multiple execution engines.<\/p>\n\n\n\n

                                                        Key features:<\/strong><\/p>\n\n\n\n

                                                          \n
                                                        • Portable pipeline definitions<\/li>\n\n\n\n
                                                        • Support for batch and streaming<\/li>\n\n\n\n
                                                        • Windowing and event-time processing<\/li>\n\n\n\n
                                                        • Multiple language SDKs<\/li>\n\n\n\n
                                                        • Engine-agnostic design<\/li>\n<\/ul>\n\n\n\n

                                                          Pros:<\/strong><\/p>\n\n\n\n

                                                            \n
                                                          • Flexibility across execution engines<\/li>\n\n\n\n
                                                          • Consistent pipeline model<\/li>\n<\/ul>\n\n\n\n

                                                            Cons:<\/strong><\/p>\n\n\n\n

                                                              \n
                                                            • Requires underlying runner<\/li>\n\n\n\n
                                                            • Debugging can be complex<\/li>\n<\/ul>\n\n\n\n

                                                              Security & compliance:<\/strong>
                                                              Depends on execution engine.<\/p>\n\n\n\n

                                                              Support & community:<\/strong>
                                                              Active community and strong documentation.<\/p>\n\n\n\n

                                                              \n\n\n\n

                                                              10 \u2014 Hazelcast Jet<\/h3>\n\n\n\n

                                                              Short description:<\/strong>
                                                              Hazelcast Jet is a distributed stream processing engine optimized for in-memory computation and low-latency processing<\/strong>.<\/p>\n\n\n\n

                                                              Key features:<\/strong><\/p>\n\n\n\n

                                                                \n
                                                              • In-memory data processing<\/li>\n\n\n\n
                                                              • Low-latency pipelines<\/li>\n\n\n\n
                                                              • Stateful and stateless processing<\/li>\n\n\n\n
                                                              • Easy cluster setup<\/li>\n\n\n\n
                                                              • Fault tolerance<\/li>\n<\/ul>\n\n\n\n

                                                                Pros:<\/strong><\/p>\n\n\n\n

                                                                  \n
                                                                • High performance<\/li>\n\n\n\n
                                                                • Simple architecture<\/li>\n<\/ul>\n\n\n\n

                                                                  Cons:<\/strong><\/p>\n\n\n\n

                                                                    \n
                                                                  • Smaller ecosystem<\/li>\n\n\n\n
                                                                  • Less mature than older frameworks<\/li>\n<\/ul>\n\n\n\n

                                                                    Security & compliance:<\/strong>
                                                                    Basic enterprise security features.<\/p>\n\n\n\n

                                                                    Support & community:<\/strong>
                                                                    Growing community and commercial support options.<\/p>\n\n\n\n

                                                                    \n\n\n\n

                                                                    Comparison Table<\/h2>\n\n\n\n
                                                                    Tool Name<\/th>Best For<\/th>Platform(s) Supported<\/th>Standout Feature<\/th>Rating<\/th><\/tr><\/thead>
                                                                    Apache Kafka Streams<\/td>Kafka-centric apps<\/td>JVM-based<\/td>Embedded stream processing<\/td>N\/A<\/td><\/tr>
                                                                    Apache Flink<\/td>Low-latency analytics<\/td>Multi-platform<\/td>True streaming engine<\/td>N\/A<\/td><\/tr>
                                                                    Spark Structured Streaming<\/td>Spark users<\/td>Multi-platform<\/td>Unified batch + stream<\/td>N\/A<\/td><\/tr>
                                                                    Apache Storm<\/td>Ultra-low latency<\/td>Multi-platform<\/td>Real-time topology model<\/td>N\/A<\/td><\/tr>
                                                                    Apache Samza<\/td>Stateful Kafka pipelines<\/td>JVM-based<\/td>Strong state management<\/td>N\/A<\/td><\/tr>
                                                                    Google Cloud Dataflow<\/td>Managed pipelines<\/td>Cloud<\/td>Fully managed Beam runner<\/td>N\/A<\/td><\/tr>
                                                                    Azure Stream Analytics<\/td>IoT analytics<\/td>Cloud<\/td>SQL-based streaming<\/td>N\/A<\/td><\/tr>
                                                                    Amazon Kinesis Analytics<\/td>AWS workloads<\/td>Cloud<\/td>Managed Flink<\/td>N\/A<\/td><\/tr>
                                                                    Apache Beam<\/td>Portable pipelines<\/td>Multi-platform<\/td>Engine-agnostic design<\/td>N\/A<\/td><\/tr>
                                                                    Hazelcast Jet<\/td>In-memory processing<\/td>Multi-platform<\/td>Low-latency execution<\/td>N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                    \n\n\n\n

                                                                    Evaluation & Scoring of Stream Processing Frameworks<\/h2>\n\n\n\n
                                                                    Criteria<\/th>Weight<\/th>Kafka Streams<\/th>Flink<\/th>Spark<\/th>Beam<\/th><\/tr><\/thead>
                                                                    Core features<\/td>25%<\/td>High<\/td>Very High<\/td>High<\/td>High<\/td><\/tr>
                                                                    Ease of use<\/td>15%<\/td>High<\/td>Medium<\/td>High<\/td>Medium<\/td><\/tr>
                                                                    Integrations & ecosystem<\/td>15%<\/td>High<\/td>High<\/td>Very High<\/td>High<\/td><\/tr>
                                                                    Security & compliance<\/td>10%<\/td>Medium<\/td>High<\/td>High<\/td>Medium<\/td><\/tr>
                                                                    Performance & reliability<\/td>10%<\/td>High<\/td>Very High<\/td>High<\/td>High<\/td><\/tr>
                                                                    Support & community<\/td>10%<\/td>High<\/td>High<\/td>Very High<\/td>High<\/td><\/tr>
                                                                    Price \/ value<\/td>15%<\/td>High<\/td>High<\/td>Medium<\/td>High<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                    \n\n\n\n

                                                                    Which Stream Processing Frameworks Tool Is Right for You?<\/h2>\n\n\n\n
                                                                      \n
                                                                    • Solo users & startups:<\/strong> Kafka Streams, Hazelcast Jet<\/li>\n\n\n\n
                                                                    • SMBs:<\/strong> Spark Structured Streaming, Apache Samza<\/li>\n\n\n\n
                                                                    • Mid-market:<\/strong> Apache Flink, Apache Beam<\/li>\n\n\n\n
                                                                    • Enterprise:<\/strong> Managed cloud solutions or Apache Flink<\/li>\n<\/ul>\n\n\n\n

                                                                      Budget-conscious teams<\/strong> should favor open-source tools, while premium solutions<\/strong> suit organizations prioritizing operational simplicity.<\/p>\n\n\n\n

                                                                      Choose feature depth<\/strong> if you need advanced analytics, or ease of use<\/strong> if development speed matters most. Always align your choice with integration, scalability, and compliance needs<\/strong>.<\/p>\n\n\n\n

                                                                      \n\n\n\n

                                                                      Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n
                                                                        \n
                                                                      1. What is stream processing?<\/strong>
                                                                        It is the real-time processing of continuous data streams as events occur.<\/li>\n\n\n\n
                                                                      2. How is it different from batch processing?<\/strong>
                                                                        Batch processes stored data periodically, while stream processing handles data instantly.<\/li>\n\n\n\n
                                                                      3. Do I need Kafka for stream processing?<\/strong>
                                                                        Not always, but many frameworks integrate tightly with Kafka.<\/li>\n\n\n\n
                                                                      4. Which framework is best for low latency?<\/strong>
                                                                        Apache Flink and Apache Storm are strong low-latency options.<\/li>\n\n\n\n
                                                                      5. Are managed cloud tools better?<\/strong>
                                                                        They reduce operational overhead but may increase costs and lock-in.<\/li>\n\n\n\n
                                                                      6. Is stream processing hard to learn?<\/strong>
                                                                        It can be complex, especially stateful processing and event-time handling.<\/li>\n\n\n\n
                                                                      7. Can I use SQL for stream processing?<\/strong>
                                                                        Yes, some tools offer SQL-based streaming interfaces.<\/li>\n\n\n\n
                                                                      8. Is stream processing scalable?<\/strong>
                                                                        Yes, most frameworks scale horizontally across clusters.<\/li>\n\n\n\n
                                                                      9. What are common mistakes?<\/strong>
                                                                        Ignoring state management, underestimating latency needs, and poor monitoring.<\/li>\n\n\n\n
                                                                      10. Can I mix batch and streaming?<\/strong>
                                                                        Many modern frameworks support unified batch and streaming pipelines.<\/li>\n<\/ol>\n\n\n\n
                                                                        \n\n\n\n

                                                                        Conclusion<\/h2>\n\n\n\n

                                                                        Stream Processing Frameworks are essential for organizations that rely on real-time data, fast decisions, and scalable event-driven architectures<\/strong>. From open-source engines like Apache Flink and Kafka Streams to managed cloud services, each tool offers unique strengths.<\/p>\n\n\n\n

                                                                        There is no single \u201cbest\u201d framework for everyone. The right choice depends on use case complexity, team expertise, budget, ecosystem compatibility, and performance requirements<\/strong>. By clearly understanding your needs and evaluating tools carefully, you can build reliable, future-ready real-time data systems that deliver continuous value.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                        Introduction Stream Processing Frameworks are software platforms designed to process, analyze, and react to continuous streams of data in real time. Unlike traditional batch processing systems that… <\/p>\n","protected":false},"author":58,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[11138],"tags":[15069,15066,15072,15070,15068,15077,15067,15073,15074,15064,15076,15065,15071,15063,15075],"class_list":["post-55676","post","type-post","status-publish","format-standard","hentry","category-best-tools","tag-apache-flink-streaming","tag-big-data-streaming-tools","tag-cloud-stream-processing-tools","tag-data-streaming-frameworks","tag-distributed-stream-processing","tag-event-driven-data-architecture","tag-event-stream-processing","tag-kafka-stream-processing","tag-low-latency-data-processing","tag-real-time-analytics-platforms-2","tag-real-time-data-pipelines","tag-real-time-data-processing-2","tag-spark-structured-streaming","tag-stream-processing-frameworks","tag-streaming-analytics-software"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/55676","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=55676"}],"version-history":[{"count":2,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/55676\/revisions"}],"predecessor-version":[{"id":60264,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/55676\/revisions\/60264"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=55676"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=55676"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=55676"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}