Introduction to Apache Kafka
Apache Kafka is a fast, scalable, fault-tolerant publish-subscribe messaging system which enables communication between producers and consumers using message based topics. It designs a platform for high-end new generation distributed applications.
The ability to give higher throughput, reliability and replication has made this technology replace the conventional message brokers such as JMS, AMQP, etc.
|Apache Kafka||Apache Flume|
|General-purpose tool for multiple producers and consumers.||Special-purpose tool for specific applications.|
|Replicates the events using ingest pipelines.||Does not replicate the events.|
Why should we use Apache Kafka cluster?
One of the biggest challenges with big data is processing and analyzing it. Also for a system to process impeccably it should be able to grasp and make the data available to the users. It is when Apache Kafka has proved its utility. It provides numerous benefits like:
- Tracking web activities by storing/sending the events for real-time processes
- Alerting and reporting the operational metrics
- Transforming data into standard format
- Continuous processing of streaming data to the topics
Due to its wide application, this technology is giving a tough competition to some of the most popular applications such as ActiveMQ, RabbitMQ, AWS, etc.
A brief history of Apache Kafka
LinkedIn was facing the issue of low latency ingestion of huge amount of data from the website into a lambda architecture which could be able to process real-time events. Since none of the solutions were available to deal with this drawback, Kafka was developed in the year 2010 as a solution to this problem.
Though there were technologies available for batch processing, but the deployment details of those technologies were shared with the downstream users. Moreover those technologies were not suitable for real-time processing.
Subsequently it was made public in the year 2011.
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Apache Kafka architecture
Kafka is usually integrated with Apache Storm, Apache HBase and Apache Spark in order to process real-time and streaming data. It is capable of delivering massive message streams to Hadoop cluster regardless of the industry or use-case. Its process flow can be better understood if we take a close look into its ecosystem:
Kafka is deployed as a cluster implemented on one or more servers. The cluster is capable of storing ‘topics’ which consists streams of ‘records’. Every record holds three details, a key, a value, a timestamp. Brokers are the abstractions which manage the persistence and replication of message.
Basically it has four core APIs :
- Producer API -This API permits the applications to publish stream of records to one or more topics.
- Consumer API -The Consumer API lets the application to subscribe to one or more topics and process the produced stream of records.
- Streams API – This API takes the input from one or more topics and produces the output to one or more topics by converting the input streams to the output ones.
- Connector API – This API is responsible for producing and executing reusable producers and consumers which are able to link topics to the existing applications.
Scope of Apache Kafka
LinkedIn has deployed one of the biggest clusters of Kafka and has reported saying “Back in 2011, it was ingesting more than 1 billion events a day. Recently, it has reported ingestion rates of 1 trillion messages a day.”
An analysis by Redmonk reveals a surprising fact saying “Kafka is increasingly in demand for usage in servicing workloads like IoT, among others.”
“The partnership with popular streaming systems like Spark has resulted in the consistent growth of active users on the Kafka users mailing list, which is just over 260% since July 2014.”– Fintan Ryan, Redmonk Analyst.
This powerful technology has created a lot of buzz since its emergence due to its special features that distinguishes it from other similar tools. Its ability to provide a unique design makes it suitable for various software architectural challenges.
Some of the tech leaders who have implemented it are-
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Who is the right audience for Apache Kafka?
Apache Kafka is best suited course for aspirants willing to make their career as Big Data Analysts, Big Data Hadoop Developers, Architects, Testing Professionals, Project Managers, Messaging and Queuing System Professionals.
However a thorough knowledge about Java, Scala, Distributed Messaging System and Linux is recommended.
How Apache Kafka will help you in career growth?
The demand for Kafka is rising at such a pace that it is outperforming Apache Spark in terms of relative employer demand.
- “The average salary for Kafka professional is 122,000 USD per annum. This is 112% higher than the average salaries of other jobs.”- Indeed.com
- The salary trend also indicates a steady and zooming growth from early 2015 that is still on the rise. -Indeed.comFrom the aforementioned facts and figures we can definitely assess the extent to which tech giants are craving for Kafka professionals.One thing is clear that Kafka has created a solid impact on the leading market players and is anticipated to grow in near future. Thus mastering it will definitely give you a sure-shot success.Though there are many technologies in market which address the similar issues, but it has created a niche for itself by delivering high-end services to the companies wanting to process streaming data in real-time. The range of qualities it offers is broad and hence it is being widely accepted by the major technology leaders. The growing popularity of this technology has created a huge demand for its professionals offering high-paying jobs to the right candidates. Hence having a sound knowledge of Kafka will help you explore better career opportunities in future.Grab coveted big data jobs by learning these Top Apache Kafka Interview Questions!