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Thursday 18 August 2022 13:31:01 GMT

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𝓚𝓪𝓽𝔂𝓕𝒾𝓁𝒾𝓅𝓅𝓮𝓷𝓴𝓸 :

атмосферно🔥

2025-11-14 19:22:48

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tysm

2025-11-13 04:31:01

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#Apache #Kafka Apache Kafka has emerged as the de facto standard for event streaming and real-time data processing in the industry. Developed at LinkedIn and open-sourced in 2011, Kafka is now used by over 80% of Fortune 100 companies to handle trillions of events per day. 📈🔔 Free Workshop Alert! ✅ Register here - https://brij.guru/ai🔸 Join me for a session to learn how to implement a real-time Product-Led Growth (PLG) strategy using Kafka. 🔸 Discover the power of real-time data integration and analytics for driving user acquisition, expansion, and retention. 🔸 You'll get a comprehensive guide on setting up a data pipeline that enables real-time decision-making and personalized user experiences. At its core, Kafka is a distributed publish-subscribe messaging system designed for high throughput and low latency. Kafka's architecture revolves around three key components:1. Producers that publish data to Kafka topics2. Brokers that store and manage the distribution of data 3. Consumers that subscribe to topics and process the dataToday, Let's talk about Kafka Producer :Thanks for sharing this informative infographic on how Kafka producers work! Let's dive into the key components and processes:🟣 The Kafka Producer is responsible for publishing data to Kafka topics. It takes a ProducerRecord consisting of the topic, partition, timestamp, message key, and message value.🟢 The producer has a buffer memory to batch messages before sending them to the Kafka brokers. This buffering is configured through batch.size and compression.type parameters.🔄 The serializer converts the message key and value to byte arrays before they are sent.🎛️ The partitioner determines which partition within the topic the message is sent to, allowing for distributing messages across brokers.⏩ The producer uses the send() method to publish messages asynchronously. It can also retrieve metadata about the message's topic and partition after sending.🔁 Kafka's replication feature ensures fault tolerance. The infographic shows an example setup with a leader broker and two follower brokers that replicate the data for high availability.🎭 I/O threads handle the network communication between the producer and Kafka brokers, with the number of threads configured based on throughput requirements.❗ If the producer encounters issues like retries, delivery timeouts or buffer full scenarios, it can be alerted to take necessary actions.📊 The producer also records metadata about the message's partition, offset and timestamp, which can be used for tracking and monitoring purposes.Kafka's producer architecture enables efficient, fault-tolerant and scalable publishing of event data. By understanding these components, you can optimize your producers for reliable real-time data streaming.

#Apache #Kafka Apache Kafka has emerged as the de facto standard for event streaming and real-time data processing in the industry. Developed at LinkedIn and open-sourced in 2011, Kafka is now used by over 80% of Fortune 100 companies to handle trillions of events per day. 📈🔔 Free Workshop Alert! ✅ Register here - https://brij.guru/ai🔸 Join me for a session to learn how to implement a real-time Product-Led Growth (PLG) strategy using Kafka. 🔸 Discover the power of real-time data integration and analytics for driving user acquisition, expansion, and retention. 🔸 You'll get a comprehensive guide on setting up a data pipeline that enables real-time decision-making and personalized user experiences. At its core, Kafka is a distributed publish-subscribe messaging system designed for high throughput and low latency. Kafka's architecture revolves around three key components:1. Producers that publish data to Kafka topics2. Brokers that store and manage the distribution of data 3. Consumers that subscribe to topics and process the dataToday, Let's talk about Kafka Producer :Thanks for sharing this informative infographic on how Kafka producers work! Let's dive into the key components and processes:🟣 The Kafka Producer is responsible for publishing data to Kafka topics. It takes a ProducerRecord consisting of the topic, partition, timestamp, message key, and message value.🟢 The producer has a buffer memory to batch messages before sending them to the Kafka brokers. This buffering is configured through batch.size and compression.type parameters.🔄 The serializer converts the message key and value to byte arrays before they are sent.🎛️ The partitioner determines which partition within the topic the message is sent to, allowing for distributing messages across brokers.⏩ The producer uses the send() method to publish messages asynchronously. It can also retrieve metadata about the message's topic and partition after sending.🔁 Kafka's replication feature ensures fault tolerance. The infographic shows an example setup with a leader broker and two follower brokers that replicate the data for high availability.🎭 I/O threads handle the network communication between the producer and Kafka brokers, with the number of threads configured based on throughput requirements.❗ If the producer encounters issues like retries, delivery timeouts or buffer full scenarios, it can be alerted to take necessary actions.📊 The producer also records metadata about the message's partition, offset and timestamp, which can be used for tracking and monitoring purposes.Kafka's producer architecture enables efficient, fault-tolerant and scalable publishing of event data. By understanding these components, you can optimize your producers for reliable real-time data streaming.

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Liên đoàn bóng đá Thái Lan cử phải đoàn cấp cao sang xin lỗi Việt Nam sau sự cố nhầm quốc kỳ. Hành động này thể hiện lời xin lỗi thành tâm hay chỉ mang tính đối phó? #bongda #vietnam #thailand

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