7 Advantages and Dis advantages of Apache Kafka | Limitations & Benefits of Apache Kafka

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7 Advantages and Dis advantages of Apache Kafka | Limitations & Benefits of Apache Kafka

7 Advantages and Dis advantages of Apache Kafka | Limitations & Benefits of Apache Kafka
Apache Kafka is a kind of open source streaming platform which is used to handle large volumes of data. It is written using Java and Scala languages. Currently, big internet giants like Twitter, LinkedIn and Airbnb use Kafka. 

The primary role of Apache Kafka is to handle event streaming. This platform is so powerful that it has the capability to handle trillions of events. Apache Kafka is the preferred option for more advanced tasks like quick messaging, tracking of IoT sensor data and service calls. 

Other than that, Kafka is also used for processing of streaming data, real-time ingesting and analysis. Al though Kafka makes big data analysis easy it is still with some drawbacks. By knowing all the pros and cons, you can decide if Apache Kafka will be a best option for your future projects.

In this article, I will be describing about 7 Advantages and Disadvantages of Apache Kafka | Limitations & Benefits of Apache Kafka. From this post, you will know the pros and cons of using Apache Kafka.

Let's get started,

Advantages of Apache Kafka

1. User Friendly

There are more than one customer waiting to handle messages. When there is a need to integrate with multiple customers, creating one integration is sufficient. The integration is made simple even for customers with variety of languages and behaviors. 

2. Reliability

As compared to other messaging services, Kafka is considered to be more reliable. In the event of a machine failure, Kafka provides resistance through means of replicating data. Thus, the consumers are automatically balanced.

3. Durability

Kafka ensures durable messaging service by storing data quickly as possible. The messages are persisted on the disk which is one of the reasons for data not being lost. 

4. Latency

The latency value offered by Kafka is very low ; not more than 10 milliseconds. The messages received by the consumer is consumed instantly. Apache Kafka cannot handle most messages sine the messages are automatically decoupled. 

5. Scalability

Apache Kafka is a scalable solution. It allows you to add additional nodes without facing any downtimes. And also, Kafka posses transparent message handling capabilities. They are able to process even terabytes of data seamless.

6. Real-time Data Control

Handline real-time data pipeline is crucial for every applications. Kafka makes it easy to build real time data pipelines such as storage, processor and analytics. 

7. Buffering Action

Apache Kafka comes with its own set of servers known as Clusters. These clusters make sure that system does not crash when there is a data transfer happening real time. Kafka acts as a buffer by relieving data from source systems and redirecting it to the target systems.


Disadvantages of Apache Kafka

1. Performance

Kafka both receives and transmits data. When the flow of data is compressed or decompressed, the performance if affected. For an example, if the data is decompressed it will eventually drain the node memory. As a result, it affects both throughput and performance.

2. Complexity

As we all know Kafka is an excellent platform for streamlining messages. However in the case of migration projects which transforms data, Apache Kafka gets more complex. Hence, to interact both data producers and consumers you need to create data pipelines.

3. Tool Support

There is always a concern for the startup companies to use Kafka over other options.  Especially, if it remains in the long run. This is because full set of management and monitoring tools are absent in Kafka. 

4. Message Tweaking

Kafka uses system calls before delivering a message. Therefore, the messages are sensitive to modifications. Tweaking messages reduces the performance of Kafka to a greater extent. The performance is not impacted only under the condition of not changing the message. 

5. No Message Paradigm

Many of the messaging paradigms are missing in Kafka. Certain use cases like extending the infrastructure is limited. One such is the point to point queues. 

6. Topic Selection

In instances of addressing certain use cases, you need to go with the approach of selecting the exact topic name. If you replace the characters with a symbol (also known as Wildcard), Kafka does not recognize them. Their algorithms are incapable of matching wildcards. 

7. Data Storage

Apache Kafka is not a recommended option for storing large sets of data. If the data is stored for a long period, the redundant copies of it is also stored. When this happens the app must be ready to compromise its performance. For this reason, only use Kafka if there is a need to store data for a short period. 

There are other storage options available like Relational or Non-Relational databases to consider.

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