What is Kafka?
To understand Kafka, it is better to understand what ‘Stream processing’ technology is. ‘Stream processing is a technology using which a user can query a continuous data stream in a micro timeframe to better understand underlying conditions responsible.
A real-time scenario – imagine if your temperature sensor sends data which you can query and receive an alert after a freezing point is received. This data query can be done in microseconds.
according to Wiki, it is open source data processing software. It was developed by LinkedIn and later donated to Apache software.
Its growth is exploding exponentially. Let’s see some facts and stats to better underline our thought. It enjoys the prime preference by more than one-third of the Fortune 500 across the globe. This distribution is shared by travel business companies, telecom giants, banks and several others. LinkedIn, Microsoft and Netflix process four-comma messages a day with Kafka (nearly equals to 1,000,000,000,000).
It is used for real-time streams of data, to collect big data, or to do real-time analysis (or both). Kafka is used with in-memory microservices to provide durability and it can be used to feed events to CEP (complex event streaming systems) and IoT/IFTTT-style automation systems.
How does Kafka work so easy?
Driven by simplicity would be the right way to define the performance. It is easy to figure out how Kafka works with such ease from its set up and use. This increased performance in behavior is dedicated to its stability, its provision to reliable durability, with its flexible inbuilt capability to publish or subscribe or queue maintenance. This is very crucial to have if you need to deal with N – numbers of clients group, if you have to show a robust replication in the market, aimed to provide your customers a consistent approach (i.e. Kafka topic partition). One crucial behavior of Kafka that set it apart from its competitors is its compatibility to systems with data streams – its process and enables these systems in order to aggregate, transform and load other stores for convenience working. “All the above-mentioned facts would not be possible if Kafka was slow”. Its exceptional performance makes this possible.
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With further addition to ease of Kafka working we have to go to “OS Level”. Let’s us find how things work for Kafka at OS level –
- It relies on OS kernels for moving the data more quickly and works on the principle of zero copy.
- It allows data records to batch into chunks which can be seen from file-system (a.k.a Kafka topic log) to consumers.
- The facility to batch data gives an efficient data compression with I/O latency reduction.
- It has the ability to scale horizontally via sharding. It can shard a title log into hundreds of partition to thousands. This allows it to handle the massive workload easily.
What can you do with Kafka?
If your company plays with huge sets of data on a regular basis you need Kafka. There is a long list of companies using it.
- LinkedIn uses to track data and operational metrics.
- Twitter to provide stream processing infrastructures.
There is a long list of companies from Uber to Spotify and Goldman Sachs to Cisco.
- High Throughput: It can easily handle a large volume of data when generating at high velocity is an exceptional advantage in favor of Kafka. This application lacks huge hardware. With the capacity to support message throughput at a frequency of thousands of messages per second.
- Low Latency: Low latency handling this high volume message generation.
- Fault-tolerance: This feature is very useful, it has an inherent capability to be restricted by node built into a cluster.
- Durable: it is very durable in its operation and is so why many MNC’s are preferring to use Kafka. Talking of durability in operations, the messages cannot get lost in the long term.
There is no special requirement for being a Kafka professional. But we have underlined some streams and professionals –
- Developers who willingly want to make a career in Big Data stream and want to accelerate there career.
- Testing professional have a good scope in Kafka in terms of Queuing and Messaging systems
- Architects – since everything needs some framework and this framework can be updated from time to time. Big Data architects would find Kafka as a good career investment.
- Project Manager is needed if the above professional is there for better management of the resources. So, higher positions are also available for the management professionals in the field of Kafka.
Why use Kafka?
For the purpose of data tracking and manipulating them as per the business need, Kafka is preferred worldwide. It gives the possibility to stream data in real time with real-time analytics. It is fast, scalable, and durable and designed as fault tolerance. There are multiple use cases present over the web where you can see why JMS, RabbitMQ, and AMQP are not even considered to work with as the need is to operate huge volume and responsiveness.
It has high throughput, reliable setup with replication characteristics which makes it a preferable choice to work on IoT sensors.
Compatibility is another reason to use it and made it acceptable worldwide. It can be easily configured to work with the below-listed application. This combination is very vital for many companies to grow business and survive (as it saves time and money).
- Spark Streaming
- Spark for real-time ingestion, processing, and analysis of data.
- It is used to feeding Hadoop BigData
It is doing great all across the globe. Well, we are not saying this rather stats. Let’s have a look –
Salary Stats for Kafka Professionals – PayScale
- Software Engineer – $109,825
- Data Engineer – $109,580
- Developers – $81,182
- Senior Data Engineer – $ 127, 836
At present Kafka has become the de-facto standard when it comes to real-time data analytics with the highest precision in microseconds. We have presented our insights in terms of data and details in support of Kafka technologies. There are several big companies that are harnessing data on a daily basis, in doing this they need professionals to harness these huge data sets. With Kafka, one can be assured to lead their career in a BigData analytics
This has been a guide to What is Kafka. Here we discussed the working, scope, career growth and advantages of Kafka. You can also go through our other suggested articles to learn more –