Apache Kafka

Apache Kafka is dead, just 100 years after Franz Kafka! Kafka's books lived on. Long Live the Kafka API! The existing Apache Kafka had to die. It’s a good thing, for several reasons. 1. Kafka isn’t cloud-native. Managing brokers and queues has been a headache for most messaging systems, and Kafka has been one of the best examples. Part of the problem has been tightly coupled storage and compute. Kora is Confluent’s replacement, but they’re not alone. RedPanda and Warpstream are others. 2. The innovation is happening around, not within Kafka. There’s a great ecosystem around the Kafka API, and it’s driving new innovation. The next generations of real-time data integration, streaming analytics, and all kinds of stream-based apps are all evolving. Beyond working on modernizing Kafka, the Apache Kafka project needs to invest in supporting all this innovation. 3. Kafka doesn’t understand data, or data integration. Kafka has always focused on messaging and stream processing, not data management or integration. New technologies have always gotten built on top of messaging to integrate processes and data, or process events. This has happened with TIBCO Rendezvous, JMS, Kafka, even (async) APIs. In the case of real-time data integration and data sharing, something needs to manage data across sources and destinations. You need to manage and integrate data schema from sources to destinations with transformations and workflows. It needs to be visual, flexible, and fast to change. That’s not Kafka. Kafka has queues and a schema registry. You code. But the Kafka API is allowing the next generation of tools to enter the market faster because you can access so many tools, and so much data, via support for the Kafka API. So what should happen to Kafka and around it over the next few years? 1. Kafka will become cloud-native. Either Confluent contributes a cloud-native version, one or more Kafka API compatible technologies take off and create multiple market segments, or both. It’s probably both. 2. New messaging protocols will emerge. Kafka is for data streaming. We need more. There’s NATS and a host of other messaging technologies for IoT. There’s Gazette for exactly-once delivery with built-in stream-store-replay that supports data integration, analytics, and data sharing better. We still need more for transactional messaging. And something will always exist outside the firewall. Most of these technologies use the Kafka API in some form. 3. Data schema and workflows will hide messaging namespaces. I say this with the utmost respect: message namespaces have no business value. If managed directly they will slow down change. They must be driven by higher-level tooling. This is how Estuary Flow works. You can connect to Flow and receive data using the Kafka API. Is Kafka dead? Going cloud-native? What do you think will happen to Kafka? Apache Kafka #ApacheKafka Confluent Redpanda Data WarpStream Estuary

The Ultimate Kafka 101 You Cannot Miss Kafka is super-popular but can be overwhelming in the beginning. Here are 8 simple steps that can help you understand the fundamentals of Kafka. 1 - What is Kafka? Kafka is a distributed event store and a streaming platform. It began as an internal project at LinkedIn and now powers some of the largest data pipelines in the world in orgs like Netflix, Uber, etc. 2 - Kafka Messages Message is the basic unit of data in Kafka. It’s like a record in a table consisting of headers, key, and value. 3 - Kafka Topics and Partitions Every message goes to a particular Topic. Think of the topic as a folder on your computer. Topics also have multiple partitions. 4 - Advantages of Kafka Kafka can handle multiple producers and consumers, while providing disk-based data retention and high scalability. 5 - Kafka Producer Producers in Kafka create new messages, batch them, and send them to a Kafka topic. They also take care of balancing messages across different partitions. 6 - Kafka Consumer Kafka consumers work together as a consumer group to read messages from the broker. 7 - Kafka Cluster A Kafka cluster consists of several brokers where each partition is replicated across multiple brokers to ensure high availability and redundancy. 8 - Use Cases of Kafka Kafka can be used for log analysis, data streaming, change data capture, and system monitoring. Over to you: What else would you add to get a better understanding of Kafka? – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq #systemdesign #coding #interviewtips .

#ApacheKafka 3.8 is here, and features 17 new KIPs: ● 13 related to Kafka Core and clients ● 3 for Kafka Streams ● 1 for Kafka Connect In this new video, Danica Fine provides a summary of the latest updates and new features in this Apache Kafka release! Watch it here ➡️ https://lnkd.in/eTbDZi5Q

𝗧𝗼𝗽 𝟱 𝗞𝗮𝗳𝗸𝗮 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: Apache Kafka is an open source distributed streaming platform designed for building real-time data pipelines and streaming applications. Kafka's high throughput, scalability, fault tolerance, and durability make it ideal for real-time data applications. Let's look at some of the most popular use cases. 𝗗𝗮𝘁𝗮 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 Kafka excels in real-time data streaming, enabling the rapid processing and analysis of data as it's generated. This comes particularly handy for situations where you need to get data quickly, such as tracking user behavior, IoT sensor outputs, and real-time analytics. 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗾𝘂𝗲𝘂𝗶𝗻𝗴 Kafka enhances message queuing by decoupling data producers from consumers, ensuring resilient, scalable asynchronous communication in distributed systems.  Its consumer group mechanism optimally balances loads across services, facilitating efficient message distribution without bottlenecks. This makes Kafka ideal for complex microservices architectures, supporting high-throughput and fault-tolerant data processing. 𝗟𝗼𝗴 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Centralized processing of logs from several sources is made possible by using Kafka for log aggregation and real-time analysis. Providing operational intelligence, keeping an eye on security, and verifying compliance all depend on this capability. Kafka enables enterprises to quickly analyze and act on log data, supporting proactive decision-making and system optimization. 𝗖𝗵𝗮𝗻𝗴𝗲 𝗱𝗮𝘁𝗮 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 (𝗖𝗗𝗖) Kafka transforms CDC by streaming database updates in real time, allowing for seamless data integration and analytics. Modern data architectures often require CDC via a tool like Kafka since it keeps the system in sync while preserving source performance. It ensures consistency and facilitates event-driven applications with current data. 𝗘𝘃𝗲𝗻𝘁 𝘀𝗼𝘂𝗿𝗰𝗶𝗻𝗴 Kafka is perfect for event sourcing, as it records application state changes as a sequence of events. Auditability, replaying events, and simplicity of complex system construction are only a few benefits of this method. System security and resilience are increased by its distributed log system, which provides a stable, scalable event-sourcing platform. Kafka adeptly supports a broad spectrum of data handling and analytics tasks, demonstrating its versatility in addressing complex challenges with scalable, real-time solutions. ~~ 📩 If you found this helpful, I write an email every Thursday to help you level up your engineering and system design skills. Sign up here: https://lnkd.in/giQj3Z44 A big thank you to our partner Kickresume who keeps our content free to the community. 📄 Build a winning resume (without spending all day). Kickresume has templates used by people hired at Google, Microsoft & more (industry-specific & ATS-friendly). Use their AI to refine and elevate your resume in minutes. Check them out: https://lnkd.in/gwWFjyik