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

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

A type of open source streaming technology that can manage massive amounts of data is called Apache Kafka. The languages Scala and Java are used in its writing. At the moment, Kafka is used by major internet companies like Airbnb, LinkedIn, and Twitter.

Managing event streaming is Apache Kafka's main responsibility. This platform is capable of handling trillions of events because of its immense power. For more complex jobs, such as tracking IoT sensor data, service calls, and instant messaging, Apache Kafka is the recommended choice.

In addition, Kafka is utilized for real-time data ingestion and analysis, as well as streaming data processing. Even though Kafka simplifies huge data analysis, there are still certain disadvantages. You can determine whether Apache Kafka will be the best choice for your next projects by being aware of all the benefits and drawbacks.

I will be discussing the 7 Advantages and Dis advantages of Apache Kafka | Limitations & Benefits of Apache Kafka in this essay. You now have an understanding of the pros and cons of utilizing Apache Kafka.

Now let's get stared,

Advantages of Apache Kafka

1. User Friendly

Several customers are in line to handle messages. When more than one customer integration is required, only one integration needs to be created. Even consumers with a range of languages and habits can easily integrate.


When it comes to messaging services, Kafka is thought to be more dependable than others. Kafka offers resilience in the case of a machine failure by means of data replication. As a result, the consumers are balanced automatically.

3. Durability

Kafka stores data as soon as feasible to ensure long-lasting messaging service. One of the reasons that data is not lost is that the messages are stored on the disk.

4. Latency

Kafka offers a very low latency value—no more than 10 milliseconds. The consumer immediately consumes the messages that they get. Due to automated decoupling, most messages are too large for Apache Kafka to process.

5. Scalability

One scalable option is Apache Kafka. You can add more nodes without experiencing any outages thanks to it. Furthermore, Kafka is capable of transparent message handling. Even terabytes of data can be processed by them with ease.

6. Real-time Data Control

For all applications, handle real-time data pipelines. Building real-time data pipelines for analytics, processing, and storage is made simple using Kafka.

7. Buffering Action

The Clusters that come with Apache Kafka are a separate group of servers. These clusters ensure that during real-time data transfers, the system does not crash. By removing data from source systems and sending it to target systems, Kafka serves as a buffer.

Disadvantage to Apache Kafka

1. Performance

Kafka may send and receive data. Performance is impacted by compression and decompression of the data flow. For instance, the node memory will eventually run out if the data is decompressed. It consequently has an impact on performance and throughput.

2. Complexity

Kafka, as everyone knows, is a great tool for condensing communications. However, Apache Kafka becomes more complicated when it comes to migration efforts that alter data. Therefore, data pipelines are necessary to facilitate communication between data producers and consumers.

3. Tool Support

Startups are constantly hesitant to choose Kafka over other solutions. particularly if it persists over time. This is a result of Kafka's lack of a complete set of administration and monitoring tools.

4. Message Tweaking

System calls are used by Kafka prior to message delivery. As a result, changes can affect the messaging. Kafka's performance decreases more as messages are tweaked. The mere fact that the message remains unchanged has no bearing on the performance.

5. No Message Paradigm

Kafka lacks a lot of the messaging paradigms. There are restricted use cases, such as expanding the infrastructure. Point-to-point queues are one example of this.

6. Topic Selection

The method of choosing the precise topic name must be used when addressing specific use cases. Kafka does not recognize characters that are substituted with a symbol, sometimes referred to as a Wildcard. Wildcard matches are not compatible with their algorithms.

7. Data Storage

It is not advised to store big data sets with Apache Kafka. Long-term data storage also involves the storage of redundant copies of the data. The application needs to be prepared to sacrifice performance when this occurs. 


Use Kafka exclusively in situations where short-term data storage is required because of this. There are more storage choices to think about, such as relational or non-relational databases.

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