What are the cons of using Kafka?

Disadvantages Of Apache Kafka Do not have complete set of monitoring tools: Apache Kafka does not contain a complete set of monitoring as well as managing tools. Thus, new startups or enterprises fear to work with Kafka. Message tweaking issues: The Kafka broker uses system calls to deliver messages to the consumer.

What are advantages of Kafka?

Kafka is Highly Reliable. Kafka replicates data and is able to support multiple subscribers. Additionally, it automatically balances consumers in the event of failure. That means that it’s more reliable than similar messaging services available.

How does Kafka handle broker failure?

Fault tolerance in Kafka is done by copying the partition data to other brokers which are known as replicas. The leader partitions and replica brokers are kept in separate brokers because if a leader partition goes down, one of the replica partition brokers can serve as the leader.

How does Kafka handle fault tolerance?

With Kafka, you can create multiple types of clusters such as Single Node Single Broker cluster, Single Node Multiple Broker cluster, and Multiple Nodes Multiple Broker cluster. Thus, Kafka achieves fault tolerance by duplicating each partition over a number of servers.

Why Kafka is pull based?

Since Kafka is pull-based, it implements aggressive batching of data. Kafka like many pull based systems implements a long poll (SQS, Kafka both do). A long poll keeps a connection open after a request for a period and waits for a response.

Is Kafka a LIFO?

Kafka supports a publish-subscribe model that handles multiple message streams. These message streams are stored as a first-in-first-out (FIFO) queue in a fault-tolerant manner. Processes can read messages from streams at any time.

What are the disadvantages of using Kafka?

The major problem comes from Kafka storing redundant copies of data. It can affect the performance, but, more importantly, it can significantly increase your storage costs.

What are the advantages of Kafka for data lake implementation?

Basically, these Kafka advantages are making Kafka ideal for our data lake implementation. So, let’s start learning advantages of Kafka in detail: a. High-throughput Without having not so large hardware, Kafka is capable of handling high-velocity and high-volume data. Also, able to support message throughput of thousands of messages per second.

What is Apache Kafka good for?

Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers. It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).

What are the advantages of using Kafka for ETL?

Batch approach: Kafka uses batch-like use cases. It can also work like an ETL tool because of its data persistence capability. Scalability: The quality of Kafka to handle large amount of messages simultaneously make it a scalable software product.