Apache Kafka

Apache Kafka Certification

Apache Kafka Tutorial

Apache Kafka is a distributed stream processing system that provides high scaling, the ability to handle big data, and a unified API. It’s the most popular messaging platform used in Big Data environments believing it is easier to build programs than manage clusters of nodes. Kafka is currently utilized by companies such as eBay, LinkedIn, Intel, and Capital One. The main goal of Kafka is to achieve high performance and scalability. One important concept behind Kafka is its ability to distribute messages across multiple machines for fault tolerance.

The first version of Kafka was released in June 2011. It has seen several major releases. Developers from LinkedIn started to create the project after they recognized a problem with how Apache Hadoop was being used since Hadoop is all about batch processing and doesn’t fit well with real-time streaming data.

Free Apache Kafka Online Practice Test

Intro to Apache Kafka

Apache Kafka is a distributed streaming platform that can process billions of events daily. The platform is fast and scalable, making it a perfect solution for high-volume service applications. It also has low latency and good throughput because of its design. The platform uses a publish-subscribe messaging model rather than the typical request-response model. It is designed to distribute data across many machines in a fault-tolerant way. By doing so, it can process thousands of messages per second (MPS), with the ability to scale up or down as needed.

Kafka is a modern, distributed, high throughput publish-subscribe system for building a real-time data pipeline and streaming applications.  Its origins are in LinkedIn, which was used internally to handle real-time updates to its website and mobile stack.  It is an open-source project developed by Apache Software Foundation that started around 2011. The platform is written in Java and deployed as a service with its storage runtimes.  It has concepts of batch and user-defined data partitioning.

Confluent Kafka vs Apache Kafka

Confluent Kafka operates quite well, and its performance remains consistent even under huge workloads. Kafka is a fast, scalable, and durable message queue with support for handling millions of messages per second and consuming millions of records from those messages. However, Apache Kafka still beats Confluent Kafka on flexibility. Apache Kafka has more streaming options to provide richer data analysis capabilities by allowing the processing of the data in motion. Apache Kafka also has better integration with third-party tools like Hadoop and Spark due to the project’s open-source nature. Confluent Kafka, on the other hand, offers better operational scalability.

Apache Kafka License

The Apache Kafka license is very permissive and can be used for free or commercial purposes. It is also a contributor’s license with no copyright assignment required. As such, only the original author of the code needs to be legally responsible for that part of the code, and no other party has any rights to any changes made on top of it. The Apache Kafka license permits modification, distribution, and use in software that implements Apache Kafka.

Apache Camel vs Kafka

Apache Camel and Apache Kafka are both popular data stream processing frameworks. Still, they have some key differences that you should be aware of before deciding which to use. The first difference is in how they handle data. Apache Kafka was designed to process large amounts of small, discrete messages sent over time by many producers. Apache Camel is a message broker that can be configured to work with various integration frameworks and programming languages.

Apache Kafka also supplies a higher level of decoupling between producers and consumers, which means that producers can write messages to the Kafka cluster without being concerned about the consumer. This can improve performance by freeing up resources used in managing the consumer. Apache Camel, on the other hand, features a more dynamic approach to processing messages where producers are not required to know anything about the consumer. However, if you need a more static type of messaging where every producer is required to talk to every consumer, then Apache Kafka is your best bet.

Setting Up Apache Kafka on Windows

Setting the Apache Kafka on Windows requires a little more work but is still simple. Here are the steps to install and configure Apache Kafka on Windows:

  1. Check that the JAVA 8 SDK is installed on your PC. 
  2. Apache Kafka Binaries should be downloaded and installed. Download the binaries from the Apache Kafka official download page.
  3. Create a “data” folder and Kafka or Zookeeper folders inside it.
  4. Update the path to the Zookeeper data location in the configuration file. Then, update the path of the Apache Kafka log file in the configuration file.
  5. Start Zookeeper from the command prompt and verify that it launched properly.
  6. Finally, launch Apache Kafka from the command prompt. Run kafka-server-start. bat command using kafka config/server.properties configuration file.
Apache Kafka Certification

Apache Kafka Series - Kafka connect hands-on learning

Kafka connect hands-on learning is a course and series of interactive online tutorials where you can learn how to use Kafka and Kafka Connect. Tutorials are designed to teach core concepts in the most efficient way possible, with the goal of giving users hands-on experience with the technology they’re learning while also providing additional resources for further learning. This course can be used to introduce new users to the basics of Kafka Connect, or as a refresher for more advanced users. This course will teach you about Kafka Connect, its architecture, and how to deploy an Apache Kafka Connector in both standalone and distributed modes.

Apache Kafka Books Ranked

Plenty of free resources are accessible online, but sometimes doing things the old-fashioned way is preferable. Choosing the right book is just part of the process: eventually, you’ll want to transition your knowledge into real-world experience. But first things first: which Kafka book should you start? Below we rank our favorite picks for tackling Apache Kafka from scratch.

  1. The Complete Beginner’s Guide to Apache Kafka, by Packt Publishing
  2. Kafka: The Definitive Guide, by O’Reilly Media
  3. Kafka for Deep Learning, by Packt Publishing 
  4. Mastering Apache Kafka with Azure Cloud Services and Stream Analytics, by Microsoft Press 
  5. Kafka Streams – Real-time Stream Processing by Prashant Kumar Pandey

Apache Kafka vs Apache Spark

Kafka is used for the storage and distribution of messages which are processed as they come in from the producer, while Spark is used to process the messages by performing computations on them. One of these two applications can be more efficient depending on what you want to do with your data and how you define this task. Kafka is more focused on logging data, while Spark handles much more than that. Kafka can handle several tasks, and it’s easier to handle. Spark is faster in terms of processing speed because it has one task to perform and focuses on that. This allows the user to use other tools with Spark while they have to use Kafka with other tools as well.

Kafka is more focused on fault tolerance, while Spark has many faults and problems. The default fault tolerance settings for Kafka are better than that for Spark. In the case of Apache Kafka, there is no recovery option, and you have a single point failure where the whole system stops. This kind of error does not occur with Apache Spark as it also allows for auto-recovery settings for its partitions with backups and other options, making it more reliable.

Apache Kafka vs Tibco EMS

Apache Kafka is a distributed processing system for high volume, low latency data storage and retrieval. It uses a log-structured architecture to provide fault tolerance and scalability. Apache Kafka is the leading open-source platform for companies looking to implement real-time stream processing solutions in production, powering some of the world’s most popular services. Tibco EMS is an enterprise messaging solution that combines traditional point-to-point communications with web-based platforms requiring open interfaces such as document management or content management systems.


                             Apache Kafka


                                      Tibco EMS

  • Apache Kafka can manage many I/Os (writes) with only three or four low-cost servers.
  • It grows quite effectively over big workloads and can handle large-scale deployments.
  • The same Kafka configuration may be used as a message bus, storage system, or log aggregator, making it simple to manage as a single system feeding many applications.
  • Tibco EMS runs well and fulfills our strict corporate communications backbone performance criteria.
  • Tibco EMS scales well, which is another one of our strict criteria.
  • Tibco offers excellent support for the EMS product and always works to enhance it. This is significant since we do not want to employ anything that does not keep up with technological developments.

Apache Kafka Jobs

The job trend for Kafka is still on the incline, and as of early 2017, the market predicts a 15% growth rate by 2025. This makes Kafka poised to be a sought-after skill set in this marketplace. Kafka job-related searches on Google have also increased year-over-year by 350%. The salaries for Kafka-skilled engineers range from $110,000-$185,000. The skill level of these engineers is at the senior to architect level. Many Apache Kafka job offers specify desired qualifications such as knowledge of Java, Hadoop, and Python. However, knowledge of all three is unnecessary but gives engineers a competitive edge.

Apache Kafka Question and Answers

Thousands of enterprises rely on Apache Kafka, an open-source distributed event streaming platform, for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

Kafka is most commonly used to create real-time streaming data pipelines and applications that react to changing data streams. It mixes communications, storage, and stream processing to enable historical and real-time data storage and analysis.

Kafka is an open-source database.

Kafka is a piece of open source software that allows you to store, read, and analyze streaming data.

Confluent is an Apache Kafka-based data streaming platform: a full-scale streaming platform capable of publishing-and-subscribe and data storage and processing within the stream. Confluent is a more comprehensive Apache Kafka distribution.

We may distribute data/load over multiple nodes in the Kafka Cluster, and it is naturally scalable, available, and fault-tolerant. Kafka works as a cluster of one or more nodes that can live in separate Datacenters. Data is stored in Kafka as a stream of continuous records that can be processed in various ways.

  • Download and install the Java 8 SDK. 
  • Get Apache Kafka Binaries and Install Them
  • : Make a Zookeeper and Apache Kafka data folder. 
  • Modify the configuration’s default value 
  • Begin working as a Zookeeper. 
  • Begin using Apache Kafka.
  • Download and install Java. 
  • Set up Apache Kafka.
  • Install Apache Kafka Cluster Manager (CMAK). 
  • Go to the CMAK Web Interface.

Apache Kafka is a free and open source messaging system. It’s compatible with Apache 2.0.

Thousands of enterprises rely on Apache Kafka, an open-source distributed event streaming platform, for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

The Apache Kafka Tutorial explains the fundamentals and advanced features of Apache Kafka. This tutorial is appropriate for both beginners and experts. Apache Kafka is a free and open-source stream-processing software framework for storing real-time data.

It’s written in Scala and Java and is part of the Apache Software Foundation’s open-source project.

Apache Kafka is a high-throughput, high-availability, and low-latency open-source message broker. Apache Kafka can be utilized independently or in conjunction with Confluent’s technologies. Confluent Kafka adds to Apache Kafka by providing additional technologies.

Kafka was created at LinkedIn and then released as an open-source project in early 2011. Kafka was co-created by Jay Kreps, Neha Narkhede, and Jun Rao.

Thousands of businesses, including more than 60% of the Fortune 100, use Kafka. Box, Goldman Sachs, Target, Cisco, Intuit, and others are among them. As a trusted platform for empowering and developing businesses, Kafka enables firms to upgrade their data strategy with event streaming architecture.

  • Update your CentOS 7 installation. 
  • Download and install the OpenJDK Runtime 
  • Get Apache Kafka.
  • Start up Apache Kafka and put it to the test.
  • Download and install Java. 
  • Install Apache Kafka.
  • Setup Kafka Systemd Unit Files   
  • Launch the Kafka Server 
  • In Kafka, make a topic.
  • Send Kafka Messages
  • Using Kafka Consumer 

In the Activities toolbar, click the Ubuntu Software icon to launch the Ubuntu Software manager, where you can search for, install, and uninstall the software from your machine. Look for the application you wish to uninstall in the list of applications and click the Remove button next.

Apache Kafka is the event log technology of choice for Amadeus microservice-based streaming applications, and it is utilized for both real-time and batch data processing.

Kafka is frequently used for operational data monitoring. It entails compiling statistics from scattered apps into centralized operational data feeds.

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