Apache Kafka is a fast, scalable, and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers due to its better throughput, built-in partitioning for scalability, replication for fault tolerance, and ability to handle large message processing applications. Kafka uses topics to organize streams of messages, partitions to distribute data, and replicas to provide redundancy and prevent data loss. It supports reliable messaging patterns including point-to-point and publish-subscribe.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Apache Kafka 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Apache Kafka 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
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"Ein pragmatischer Ansatz für eine bessere
Zusammenarbeit in der IT" - Vortrag beim Networking Event beim Beratungshaus affinis in Hamburg am 22.2.2013
In this session you will learn:
1. Kafka Overview
2. Need for Kafka
3. Kafka Architecture
4. Kafka Components
5. ZooKeeper Overview
6. Leader Node
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Learn All Aspects Of Apache Kafka step by step, Enhance your skills & Launch Your Career, On-Demand Course
for apache kafka online training visit: https://mindmajix.com/apache-kafka-training
How to use kakfa for storing intermediate data and use it as a pub/sub model with each of the Producer/Consumer/Topic configs deeply and the Internals working of it.
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Kafka Streams is a client library providing organizations with a particularly efficient framework for processing streaming data. It offers a streamlined method for creating applications and microservices that must process data in real-time to be effective. Using the Streams API within Apache Kafka, the solution fundamentally transforms input Kafka topics into output Kafka topics. The benefits are important: Kafka Streams pairs the ease of utilizing standard Java and Scala application code on the client end with the strength of Kafka’s robust server-side cluster architecture.
Kafka is a real-time, fault-tolerant, scalable messaging system.
It is a publish-subscribe system that connects various applications with the help of messages - producers and consumers of information.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
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Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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And...
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Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
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Major cyber events in 2024
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How world-class product teams are winning in the AI era by CEO and Founder, P...
Apache kafka
1. Apache Kafka is a fast, scalable, durable and
distributed messaging system.
2.
We need basic Java programming skills plus access
to:
Apache Kafka 0.9.0
Apache Maven 3.0 or later
Git
Step 1: Download Kafka
Download the Apache Kafka 0.9.0release and un-tar
it.
Prerequisites & Installation
3.
Kafka is designed for distributed high
throughput systems. Kafka tends to work very
well as a replacement for a more traditional
message broker. In comparison to other
messaging systems, Kafka has better throughput,
built-in partitioning, replication and inherent
fault-tolerance, which makes it a good fit for
large-scale message processing applications.
4.
A Messaging System is responsible for transferring
data from one application to another, so the
applications can focus on data, but not worry about
how to share it. Distributed messaging is based on
the concept of reliable message queuing. Messages
are queued asynchronously between client
applications and messaging system. Two types of
messaging patterns are available − one is point to
point and the other is publish-subscribe (pub-sub)
messaging system. Most of the messaging patterns
follow pub-sub.
What is a Messaging
System?
5.
In a point-to-point system, messages are persisted in
a queue. One or more consumers can consume the
messages in the queue, but a particular message can
be consumed by a maximum of one consumer only.
Once a consumer reads a message in the queue, it
disappears from that queue. The typical example of
this system is an Order Processing System, where
each order will be processed by one Order Processor,
but Multiple Order Processors can work as well at
the same time. The following diagram depicts the
structure.
Point to Point Messaging
System
Sender
Message
Queue
Receiver
6. In the publish-subscribe system, messages are persisted
in a topic. Unlike point-to-point system, consumers can
subscribe to one or more topic and consume all the
messages in that topic. In the Publish-Subscribe system,
message producers are called publishers and message
consumers are called subscribers. A real-life example is
Dish TV, which publishes different channels like sports,
movies, music, etc., and anyone can subscribe to their
own set of channels and get them whenever their
subscribed channels are available.
Publish-Subscribe
Messaging System
Sender
Message
Queue
Receiver
Receiver
Receiver
7.
Following are a few benefits of Kafka −
Reliability − Kafka is distributed, partitioned, replicated and
fault tolerance.
Scalability − Kafka messaging system scales easily without
down time..
Durability − Kafka uses Distributed commit log which means
messages persists on disk as fast as possible, hence it is
durable..
Performance − Kafka has high throughput for both publishing
and subscribing messages. It maintains stable performance
even many TB of messages are stored.
Kafka is very fast and guarantees zero downtime and zero data
loss.
Benefits
8.
Kafka is a unified platform for handling all the real-time
data feeds. Kafka supports low latency message delivery
and gives guarantee for fault tolerance in the presence of
machine failures. It has the ability to handle a large
number of diverse consumers. Kafka is very fast,
performs 2 million writes/sec. Kafka persists all data to
the disk, which essentially means that all the writes go to
the page cache of the OS (RAM). This makes it very
efficient to transfer data from page cache to a network
socket.
Need for Kafka
9.
such as topics, brokers, producers and consumers.
Topics: A stream of messages belonging to a particular category is
called a topic. Data is stored in topics
Partition:Topics may have many partitions, so it can handle an
arbitrary amount of data.
Partition offset: Each partitioned message has a unique sequence id
called as offset
Replicas of partition: Replicas are nothing but backups of a partition.
Replicas are never read or write data. They are used to prevent data
loss.
Brokers
Brokers are simple system responsible for maintaining the pub-lished
data. Each broker may have zero or more partitions per topic. Assume,
if there are N partitions in a topic and N number of brokers, each
broker will have one partition.
Kafka main
terminologies
10.
The sample Producer is a classical Java application with a
main() method, this application must:
Initialize and configure a producer
Use the producer to send messages
1- Producer Initialization
Create a producer is quite simple, you just need to create an
instance of the
org.apache.kafka.clients.producer.KafkaProducer class with a
set of properties, this looks like:
producer = new KafkaProducer(properties); In this example,
the configuration is externalized in a property file, with the
following entries:
Producer
11.
Once you have a producer instance you can post
messages to a topic using the ProducerRecord class. The
ProducerRecord class is a key/value pair where:
the key is the topic
the value is the message
As you can guess sending a message to the topic is
straight forward:
... producer.send(new ProducerRecord("fast-messages",
"This is a dummy message")); ...
2- Message posting
12.
Once you are done with the producer use
the producer.close() method that blocks the process
until all the messages are sent to the server. This call is
used in a finally block to guarantee that it is called. A
Kafka producer can also be used in a try with resources
construct.
... }
finally { producer.close();
}
Producer End
13.
The Consumer class, like the producer is a simple
Java class with a main method.
This sample consumer uses the Hdr Histogram
library to record and analyze the messages received
from the fast-messages topic, and Jackson to parse
JSON messages.
Consumer
14.
ZooKeeper is used for managing and coordinating
Kafka broker. ZooKeeper service is mainly used to
notify producer and consumer about the presence of
any new broker in the Kafka system or failure of the
broker in the Kafka system. As per the notification
received by the Zookeeper regarding presence or
failure of the broker then pro-ducer and consumer
takes decision and starts coordinating their task with
some other broker.
Kafka stores basic metadata in Zookeeper such as
information about topics, brokers, consumer offsets
(queue readers) and so on.
ZooKeeper