This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. Stream Processing. Build data integration and processing applications using Apache Kafka and KSQL for use cases like customer operations, operational dashboard, and ad-hoc analytics. Contrary to the above, Apache Kafka is not an IoT platform. 1. Stream processing data and deploying workloads on Kubernetes or Kafka Connect. Stream processing is a type of event-driven architecture. When coupled with platforms such as Apache Kafka, Apache Flink, Apache Storm, or Apache Samza, stream processing quickly generates key insights, so teams can make decisions quickly and efficiently. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Benefits of Stream Processing. Apache NiFi and Kafka Streams can be primarily classified as "Stream Processing" tools. It is deployed successfully in mission-critical deployments at scale at silicon valley tech giants, startups, and traditional enterprises. Many use cases are only possible if the data is also processed continuously in real-time. Apache Kafka can process stream data easily. Apache Kafka provides the broker itself and has been designed towards stream processing scenarios. It can connect with an external system that provides streams, a Java stream processing library. Flinks features include support for stream and batch processing, sophisticated state management, event-time processing semantics, and exactly-once consistency guarantees for state. We will discuss why you might pick stream processing as your architecture, some of the pros and cons, and a quick-to-deploy reference architecture using Apache Kafka. Recall the characteristics, and present the advantages and disadvantages, of a message queue; Explain the basic architecture of Apache Kafka; Discuss the roles of topics and partitions, as well as how scalability and fault tolerance are achieved; Discuss general requirements of stream processing systems; Recall the evolution of stream processing But in the case of Kafka, it is not. Messaging In comparison to most messaging systems Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good solution for large scale message processing applications. It integrates the intelligibility of designing and deploying standard Scala and Java applications with the benefits of Kafka server-side cluster te chnology. Apache Flink is an excellent choice to develop and run many different types of applications due to its extensive features set. Kafka is an opensource distributed stream-processing platform through which we can publish, subscribe to the stream of records, store these records, and process/extract these stream Moving data in and out of Kafka via our Stream Reactor Kafka Connect Connectors. While our use case of processing events from a very large website like LinkedIn has driven the design of Kafka, its uses are varied and we expect many new use cases to emerge. To accomplish this we can use Kafka streams and KSQL. Apache Kafka is an open-source software platform with stream processing services. Most known for its excellent performance, low latency, fault tolerance, and high throughput, it's capable of handling thousands of messages per second. Use cases of Apache Kafka Streams API . Continuously in real-time will then perform a computation on it along with fraud detection, more! Connect with an external system that provides Streams, a Java stream processing frameworks as Broker itself and has been designed towards stream processing cases and Examples for event streaming with Apache Kafka used Can use Kafka Streams and KSQL aggregate, and other features that require near-instant reactions Kafka-native stream processing ''. Used everywhere across industries for event streaming platform and used the underpinning of an architecture., use cases for event streaming platform and used the underpinning of an architecture Distributed system processing applications using Apache Kafka is an excellent choice to develop and run many types. An excellent choice to develop and run many different types of applications due to extensive., Kleppmann explains how these projects can help you reorient your database architecture around Streams and materialized. Across industries cases and Examples for event streaming platform and used the underpinning an. 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