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Alternatives of Kafka 1. Amazon Kinesis
AWS Kinesis or Kinesis Streams is considered to be based on Kafka. Kinesis is known for its super-fast speed, reliability, ease of operation and its cross-platform replication ability. Cross-platform replication is syncing of data between physical and logical data centres. There are also many language-specific clients like Java, Scala, Ruy, JavaScript (Node), etc.
One of Kafka’s major flaws is the need for human intervention to install and maintain the streams. However, Kinesis breaks the streams across shards which are similar to partitions in Kafka, and each shard has a hard limit on the number of transactions and data volume per second. You’ll need to increase the number of shards if you exceed that limit. This is where Kinesis distinguishes where AWS allows users to increase or scale their operations by only paying for what they use, and thus, much of the maintenance is hidden from the users.
However, Kafka is more flexible and can be accustomed to what users need however;, the user needs to manage its own clusters and require DevOps resources to keep it running. On the other hand, Kinesis is sold as a service by Amazon Web Services and doesn’t need a DevOps team to run it.
Kinesis is more useful for functions like Stock Price tickers, Social Network Data (Although LinkedIn is entirely run on Kafka for streaming data), Geospatial data like connecting User users with Drivers and with IoT sensors.
2. RAITMQ
It is used when the application needs to work on various protocols like AMQP, STOMP, MQTT.
Suppose you need a more detailed control on a per-message basis. Meaning where the data volume is less, and the user needs more control over it. However, Kafka has added support for more control recently.
The application needs more flexibility in point to point, Request/Reply messages.
To integrate multiple services/apps with non-trivial routing logic.
If you need more Security over-application as it provides support for plugins and APIs for the same. Also, it has better community support through various community plugins available for almost all possible scenarios.
Some of the companies using RabbitMQ are Reddit, 9gag, Robinhood, Zapier, Myntra and MIT.
3. ActiveMQAlthough both Kafka and ActiveMQ were originally made for different operations completely, they’ve had many features that overlay each other over time. Therefore, they’re being used interchangeably and often used for the same purposes and compared with each other.
Some of the key differences are:
Allowing applications built with different languages and on different operating systems to integrate with each other
Kafka producer doesn’t wait for the broker’s acknowledgements, increasing the overall throughput if the broker can handle messages as fast as the producer sends them. ActiveMQ has to keep and maintain a delivery report of every message at every state. Thus, it has great recovery support where messages can be restored later if a queue fails.
Kafka also has a better storage efficiency as in Kafka; each message has an overhead of 9 bytes against that of 144 bytes in ActiveMQ. This increases the space used by ActiveMQ by 70% more than Kafka.
ActiveMQ also pushes messages to consumers instead of consumers having to poll for new messages by doing a SQL Query which reduces the latency involved in processing new messages.
It also has a great and rousts scheduler which means you can schedule messages to be delivered at a particular time.
Some of the companies using ActiveMQ are Intuit, Awin, Zingat.
4. Apache Spark
Spark is an open-source, distributed general-purpose, unified analytics engine for large-scale distributed data processing and ML. Apart from Core Data Processing, it has libraries for SQL, ML graph computation and Stream Processing. So, it offers much more than Kafka, which only provides stream processing at its core.
Spark processes data streams in real-time as it is generated, making it one of the fastest amongst the lot and thereby increasingly having use cases in fields like Financial Markets. Also, as data streams grow, and it’s growing at an exponential rate, their ML capabilities become more feasible and accurate.
It’s also easy to use and doesn’t require a preset DevOps team as it has easy APIs for operating on large datasets. It includes a collection of over 100 operators for transforming data and data frame APIs for manipulating semi-structured data.
It also has an Interactive Analytics engine, enabling users to have engagingly project data.
However, many people would still prefer Kafka or better Kafka Streams for its relatively simple approach, and thereby Spark is more of a use case for data scientists and developers engaging in ML and Analytics over a simple message broker platform for their application.
5. Apache Storm
The storm is more in line with Spark as it is primarily in Data. It’s an Open source, distributed Realtime computation system for data streams similar to what Hadoop does for a batch of data. It also integrates with the queuing and database technologies.
It is also extremely fast, reliable, and fault-tolerant, processing over a million records per second per node on a modest size cluster.
Because of its similar nature, having varied use cases Storm like Spark is widely used in the Financial, Telecom, Retail, We and Manufacturing sectors.
Conclusion Recommended ArticlesThis is a guide to Kafka Alternatives. Here we discuss the Introduction and Alternatives of Kafka, including Amazon Kinesis, RAITMQ, ActiveMQ, and Apache Spark in detail. You may also look at the following articles to learn more –
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