Popular Apache Kafka Use Cases and Implementation Checklist
Are you interested in adopting Apache Kafka but unsure if your organization needs it? This article is for you.
Here we will discuss;
- What is Kafka, and its benefits
- What are the popular Apache Kafka use cases?
- What is the go to list for Kafka Implementation?
Firstly, one need to understand that data generation has grown and multiplied many folds owing to the overall dominance of digital age. Businesses that want to thrive and remain relevant in the modern world and in the future need to learn and understand how to manage massive volumes of data via scalable, strong and flexible platforms.
Apache Kafka is the tool to achieve it – with ultra scalability, strength and flexibility.
Apache Kafka Business Benefits
Today Apache Kafka is the second largest and most active visited Apache project – a project under use by 100,000 organizations worldwide.
Similar to other messaging platforms – Apache Kafka works by facilitating asynchronous data exchanges between applications, processes and servers. Plus, Kafka has the capability to process trillions of event each day. All real time big data solutions stand to benefit from Kafka, with its specialized system targeting desired level of performance.
What Kafka does is help ingest and swiftly move massive data amounts reliably – plus it is a flexible tool for sustaining communication in between loosely connected elements of the IT systems.
Apache Kafka Technological Benefits
Making Architects and software developers prefer Kafka as it comes with a set of software – primarily making it an attractive option in data integration. Kafka has come out to become the obvious choice for handling distributed processing.
Why? – Kafka offers the holistic ecosystem complete with tools for effectively handling data flow, fast and timely.
Kafka has become a state–of–the–art streaming platform that handles sending messages from one end to the other, making Kafka the perfect tool for handling big data. Below we will look at some real-life Apache Kafka use cases:
1. Kafka in Messaging
Kafka is the go to choice for a more conventional broker – it works perfectly as a substitute.
The message brokers are put to use for various purposes including to safeguarding of unprocessed messages, and decouple handling from the data producers, and more.
Apache Kafka offers;
- Improved imitation
- Built-in partitioning
- Error tolerance
In comparison to most of the traditional messaging systems – Kafka outshines as a worthy solution for the large scale message handling platforms and applications.
2. Kafka Metrics
Kafka is primarily put to use for operating the monitoring data. Moreover, it works to account of combining statistics from the distributed applications for generating the integrated feeds of the operative data.
3. Log Aggregation
Kafka has versatile uses and it can be put to use across the organizations for capturing logs from the multiple varying services; making all of these logs available in a standard format for several consumers.
4. Stream Processing
Some of the mainstream frameworks retrieve and read data from subject, process it and then write the processed data to a new topic, writing where this data is available for applications and users alike. In a stream processing framework, Kafka’s robust stability is very beneficial.
Kafka comes with multiple scalable benefits; however, there are some instances where Kafka should not be used;
Where Not To Use Kafka?
While Kafka and its scope make it somewhat of a Swiss army knife of the big data applications – it does come with certain limitations. These limitations include its complexity, plus there are scenarios where it is just not appropriate.
1. Small Data
Kafka is one of the big guns and it is designed to handle massive volumes of data – therefore it will be overkill if you only need to process a small amount of messages each day (several thousands). If this is the requirement of your organization or platform, it is ideal to use the conventional message queues like the RabbitMQ for the processing smaller datasets.
2. Streaming ETL
Even so Kakfa has the stream API, it is a pain to do data transformations on the go.
The process shall need you to create complex pipelines of interactions in between the producers and consumers then also maintaining the complete system.
Overall, this needs quite a substantial effort and work, adding more to the complexity. Here it is ideal to avoid Kafka as the processing engine for the ETL jobs, or the more so, where real-time processing is required. There are third party tools that can be used with Kafka for harvesting the additional robust capabilities – for instance, for optimizing tables for the real-time analytics.
Having said all of this, how do you know if you should or should not implement Kafka in your organization? Check out the;
Checklist for Apache Kafka Implementation
When you are contemplating whether you should implement Kafka or not, ask yourself the following questions;
- Does my business processes need message ordering?
- Do you want to be able to configure the retention of data?
- Are you looking to scale consumers linearly?
- Do you require multiple processes reading same data independently?
- Will it make sense for multiple consumers to consume the same data?
- Are you looking to integrate message broker with other services? (databases)
This is indeed a lot of information however if you need more – visit us at Memphis where we have more information available on Apache Kafka.
