Brij kishore Pandey’s Post

Unlock the Power of Event Streaming with Apache Kafka! 🚀 I recently dove into the world of Apache Kafka, and I'm excited to share my experience with you. Apache Kafka is an open-source distributed event streaming platform that's transforming the way thousands of companies handle high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Kafka combines three key capabilities that allow you to implement your event streaming use cases end-to-end with a single battle-tested solution: 1. Publish (write) and subscribe to (read) streams of events, including continuous import/export of data from other systems. 2. Store streams of events durably and reliably for as long as you need. 3. Process streams of events as they occur or retrospectively. All of this functionality is provided in a distributed, highly scalable, elastic, fault-tolerant, and secure manner. Kafka can be deployed on bare-metal hardware, virtual machines, containers, on-premises, or in the cloud. You can choose to self-manage your Kafka environments or use fully managed services offered by various vendors. If you're looking to revolutionize your data pipelines, streaming analytics, or data integration, I highly recommend exploring the power of Apache Kafka.  Special thanks to Pau Labarta Bajo who inspired me to learn Kafka and Piyush Garg from whom I learned the concept of Kafka.

Message Queue(MQ) Systems-2: Installing and Using Apache Kafka as an Event Streaming Platform. From where we left off, we continue learning MQ Systems with “Apache Kafka”. We examined “Amazon SQS (Simple Queue Service)” in our previous article. In this section, we will learn detailed information about Apache Kafka, then, we’ll start, connect, and use a Kafka environment, and see its usage in practice in a hands-on example. We’ll start the “demo application”, and observe the “output of the demo application” is a continuous event stream of updates and is processed by Kafka. In the next article, we will examine RabbitMQ in the same way. Topics we will cover: 1. What is Apache Kafka? 1. 1. Event Streaming 1. 2. Kafka Environment 1. 3. Terminology 2. Hands-on-1: Starting, Connecting, and Using a Kafka Environment 2. 1. Downloading The Latest Kafka Release and Starting a Kafka Environment 2. 2. Creating a Topic To Store Our Events 2. 3. Writing Some Events Into The Topic 2. 4. Reading The Events 2. 5. Import/Export Our Data As Streams Of Events With Kafka Connect 3. Hands-on-2: Processing Our Events With Kafka Streams 3. 1. Preparing an “input topic” and Start the Kafka Producer 3. 2. Starting the Wordcount Application 3. 3. Checking Our Events With Kafka Streams 4. Terminating The Kafka Environment 5. As a result 6. Next post: “Message Queue(MQ) Systems-3: Installing and Using RabbitMQ” 7. References I frequently share articles about Cloud and DevOps tools and resources. Follow me on Medium so you don’t miss future articles, cmakkaya.medium.com Share this article with friends in your network and help them to upskill. The Article Link: https://lnkd.in/dyG_TQx5

𝐀𝐩𝐚𝐜𝐡𝐞 𝐊𝐚𝐟𝐤𝐚 Apache Kafka is an open-source distributed streaming system used for stream processing, real-time data pipelines, and data integration at scale. Originally created to handle real-time data feeds at LinkedIn in 2011, Kafka quickly evolved from a messaging queue to a full-fledged event streaming platform, capable of handling over one million messages per second, or trillions of messages per day. Founded by the original creators of Apache Kafka, Confluent provides the most comprehensive Kafka tutorials, training, services, and support. Confluent also offers fully managed, cloud-native data streaming services built for any cloud environment, ensuring scalability and reliability for modern data infrastructure needs. Why Kafka? Kafka has numerous advantages. Today, Kafka is used by over 80% of the Fortune 100 across virtually every industry, for countless use cases big and small. It is the de facto technology developers and architects use to build the newest generation of scalable, real-time data streaming applications. While these can be achieved with a range of technologies available in the market, below are the main reasons Kafka is so popular. High Throughput High Scalability Low Latency Permanent Storage High Availability

𝐀𝐩𝐚𝐜𝐡𝐞 𝐊𝐚𝐟𝐤𝐚 Apache Kafka is an open-source distributed streaming system used for stream processing, real-time data pipelines, and data integration at scale. Originally created to handle real-time data feeds at LinkedIn in 2011, Kafka quickly evolved from a messaging queue to a full-fledged event streaming platform, capable of handling over one million messages per second, or trillions of messages per day. Founded by the original creators of Apache Kafka, Confluent provides the most comprehensive Kafka tutorials, training, services, and support. Confluent also offers fully managed, cloud-native data streaming services built for any cloud environment, ensuring scalability and reliability for modern data infrastructure needs. Why Kafka? Kafka has numerous advantages. Today, Kafka is used by over 80% of the Fortune 100 across virtually every industry, for countless use cases big and small. It is the de facto technology developers and architects use to build the newest generation of scalable, real-time data streaming applications. While these can be achieved with a range of technologies available in the market, below are the main reasons Kafka is so popular. High Throughput High Scalability Low Latency Permanent Storage High Availability

Apache Kafka is an open-source event streaming platform designed for handling large volumes of real-time data. Developed by LinkedIn and now under the Apache Software Foundation, Kafka is known for high throughput, fault tolerance, and scalability, making it ideal for real-time data pipelines and streaming applications. In Kafka, messages are data records with a structure comprising an optional key, a value (the payload), a timestamp, and headers for metadata. Kafka organizes these messages within topics—logical channels for specific data streams, which are further divided into partitions. Partitions are essential to Kafka's scalability and fault tolerance, enabling data distribution across servers and parallel processing, as each consumer can read from different partitions. Kafka Producers send messages to topics, using strategies like round-robin or key-based partitioning. The Producer's acknowledgment settings define the level of message durability. Kafka Consumers read from topics, often as part of a consumer group, where each group member processes different partitions. Consumers track message offsets, allowing reliable, ordered data processing. Kafka Brokers are servers managing data within the cluster, handling storage, partitioning, and producer/consumer requests. Brokers in a Kafka cluster provide scalability and redundancy, with data replication across brokers ensuring durability. Kafka is widely used for real-time analytics, event-driven architectures, log aggregation, data pipelines, and monitoring, making it a cornerstone technology for modern data-intensive applications. Ref: https://lnkd.in/gPsbEYVC

Apache Kafka is an open-source distributed streaming system used for stream processing, real-time data pipelines, and data integration at scale. Publish / Subscribe Model Before discussing the specifics of Apache Kafka, it is important for us to understand the concept of publish/subscribe messaging and why it is important. Publish/subscribe messaging is a pattern that is characterized by the sender (publisher) of a piece of data (message) not specifically directing it to a receiver. Instead, the publisher classifies the message somehow, and that receiver (subscriber) subscribes to receive certain classes of messages. Pub/sub systems often have a broker, a central point where messages arpublished, to facilitate this. Apache Kafka is a publish-subscribe messaging system. A messaging system let you send messages between processes, applications, and servers. Broadly Speaking, Apache Kafka is a software where topics (A topic might be a category) can be defined and further processed. Applications may connect to this system and transfer a message onto the topic. A message can include any kind of information ,from any event on your Personal blog or can be a very simple text message that would trigger any other event. A Kafka cluster usually consists of one or more servers (called as kafka brokers), which are running Kafka over them. Producers are processes that publish data (push messages over trigger) into Kafka topics within the specified broker. A consumer of topics pulls messages off a Kafka topic. Kafka Topic : A Topic basically is a category or a feed name to which messages are stored and published during operations. Messages are mostly byte arrays that can store any object in any format. Yes , That’s the best thing about kafka. Any object can be stored as byte array. Also, as we discussed before, all Kafka messages are organized into topics. If you wish to send a message you send it to a specific topic and if you wish to read a message you read it from a specific topic. Consumers and consumer groups : Consumers can always read messages starting from a specific offset and are allowed to read from any offset point they choose in between. This allows consumers to join the cluster at any point in time.This makes functioning and working really smooth. Partitions allow you to parallelise a topic by splitting the data in a particular topic across multiple brokers.

Apache Kafka is a powerful open-source distributed event streaming platform that offers high-throughput, fault-tolerant, and real-time data processing. At its core, Kafka is a messaging system that enables publishing, subscribing, storing, and processing streams of records in real-time. Kafka's architecture is built around the concept of topics, which provide an organized and stored structure for data. Producers are responsible for publishing records to topics, while consumers subscribe to these topics to process the records. This decoupling of producers and consumers allows them to operate at their own pace, enabling scalable and efficient data processing with multiple producers and consumers interacting with Kafka concurrently. Kafka is known for its fault-tolerance and high availability, replicating data across multiple brokers (servers) within a Kafka cluster to ensure data durability and fault tolerance. Kafka can seamlessly recover without losing any data, making it suitable for mission-critical applications. Kafka's scalability is another key feature as it can handle increasing data loads without sacrificing performance. Additionally, Kafka supports partitioning, which distributes data across multiple brokers, enhancing scalability and parallel processing. In summary, Apache Kafka is an excellent tool for building real-time data pipelines, stream processing applications, and event-driven architectures. Its combination of high throughput, fault tolerance, scalability, and real-time processing capabilities makes it a popular choice for organizations dealing with large volumes of data and demanding performance requirements.

Apache Kafka is a distributed streaming platform that was originally developed by LinkedIn and later open-sourced. It's designed to handle high-throughput, fault-tolerant, and scalable data streaming. Let's break down the key concepts: 1. What is Kafka?    - A distributed streaming platform    - Used for building real-time data pipelines and streaming applications    - Can publish, subscribe to, store, and process streams of records 2. Core Concepts:    a) Topics:    - Categories or feed names to which records are published    - Similar to a table in a database, but without all the constraints    b) Producers:       - Applications that publish (write) records to Kafka topics    c) Consumers:       - Applications that subscribe to (read) topics and process the records    d) Brokers:       - Servers that store the published records    e) Clusters:       - Kafka is run as a cluster of one or more servers (brokers) 3. How It Works:    - Producers send records to Kafka topics    - Consumers read these records from the topics    - Records are stored in partitions within topics for scalability    - Each partition is an ordered, immutable sequence of records 4. Key Features:    - High throughput: Can handle millions of messages per second    - Fault-tolerant: Replicates data across multiple nodes    - Scalable: Easy to add or remove nodes without downtime    - Persistent storage: Stores streams of records durably    - Real-time processing: Processes streams of records as they occur 5. Use Cases:    - Messaging systems    - Activity tracking    - Gathering metrics from various sources    - Application logs processing    - Stream processing    - Event sourcing 6. Benefits:    - Decoupling of data streams and systems    - Multiple consumer support    - Retention of data, allowing for replay and batch processing    - Ordering guarantees of records This is a basic introduction to Kafka. Would you like me to elaborate on any specific aspect or provide a simple example of how Kafka might be used in practice?

Apache Kafka is a distributed streaming platform that was originally developed by LinkedIn and later open-sourced. It's designed to handle high-throughput, fault-tolerant, and scalable data streaming. Let's break down the key concepts: 1. What is Kafka? - A distributed streaming platform - Used for building real-time data pipelines and streaming applications - Can publish, subscribe to, store, and process streams of records 2. Core Concepts: a) Topics: - Categories or feed names to which records are published - Similar to a table in a database, but without all the constraints b) Producers: - Applications that publish (write) records to Kafka topics c) Consumers: - Applications that subscribe to (read) topics and process the records d) Brokers: - Servers that store the published records e) Clusters: - Kafka is run as a cluster of one or more servers (brokers) 3. How It Works: - Producers send records to Kafka topics - Consumers read these records from the topics - Records are stored in partitions within topics for scalability - Each partition is an ordered, immutable sequence of records 4. Key Features: - High throughput: Can handle millions of messages per second - Fault-tolerant: Replicates data across multiple nodes - Scalable: Easy to add or remove nodes without downtime - Persistent storage: Stores streams of records durably - Real-time processing: Processes streams of records as they occur 5. Use Cases: - Messaging systems - Activity tracking - Gathering metrics from various sources - Application logs processing - Stream processing - Event sourcing 6. Benefits: - Decoupling of data streams and systems - Multiple consumer support - Retention of data, allowing for replay and batch processing - Ordering guarantees of records This is a basic introduction to Kafka. Would you like me to elaborate on any specific aspect or provide a simple example of how Kafka might be used in practice?