Apache Kafka Reviews & Product Details

What I like best about Apache Kafka is its ability to handle high volumes of data in real time, with strong throughput and reliability. It makes it easier to build event-driven architectures and integrate multiple services through asynchronous communication. I also appreciate its scalability, fault tolerance, and ability to retain and replay events, which are useful for building resilient data pipelines and distributed applications. Review collected by and hosted on G2.com.

Apache Kafka can be complex to set up, configure, and maintain, especially in larger production environments. Monitoring consumer lag, managing partitions, tuning performance, and troubleshooting issues can require significant expertise. It also adds operational overhead when self-hosted, and understanding topics, brokers, offsets, and consumer groups can take time for developers who are new to the platform. Better tooling and simpler configuration options could improve the overall developer experience. Review collected by and hosted on G2.com.

it handle high volume of data in real time.

if this application crashes and down becouse some reason then if we use it again after 2-3 hrs also then it just picks up right where it left off when it restarts. Review collected by and hosted on G2.com.

it reqyires serios hardware and it need strong network bandwith.

it is costly . Review collected by and hosted on G2.com.

What I like most is how well it handles real-time data streaming at scale. As a data engineer, we’ve used Kafka to move and process streaming data between different systems, and it’s been very reliable. It’s great for decoupling producers and consumers, so different services can operate independently without breaking the pipeline. Once it’s set up properly, it can handle high volumes of data without much issue. Review collected by and hosted on G2.com.

The setup and maintenance can be quite heavy, especially if you’re managing it yourself. There’s a steep learning curve around topics, partitions, consumer groups, and tuning performance. Debugging issues or lag in consumers can also take time, and it’s not always obvious where the bottleneck is. It’s powerful, but definitely not the simplest tool to work with. Review collected by and hosted on G2.com.

Kafka gives a durable, partitioned commit-log that can handle massive throughput and lets you replay events at will, perfect for building resilient, real-time pipelines and stream processing with minimal latency.

Deployment using docker is easy.

Connector ecosystem is good for sink and source. Review collected by and hosted on G2.com.

Since Kafka is JVM-based, it can feel heavier compared to some of the newer messaging systems written in Rust or C++. Those tend to have lower memory overhead and sometimes better raw performance. Review collected by and hosted on G2.com.

Apache Kafka is the best open source highly scalable Message Queue with flexible configuration and high modularity. Its perfect for smaller Proof of concept to large enterprise grade applications without the need to drastically change the tech stack Review collected by and hosted on G2.com.

I feel the documentation could be improved so that developers who start to work in Apache Kafka would have smoother learning curve. Review collected by and hosted on G2.com.

Apache kafka is one of the best tools that I use almost daily to streamline messages. Queing of data messages makes it easier to read and execute things in order. Review collected by and hosted on G2.com.

Sometimes the sorting functionality doesnt work which could be improved. Review collected by and hosted on G2.com.

High throughput and low latency, scalability ,Durability & Fault Tolerance ,replay ability & Retention Kafka retains messages for a configurable period, allowing consumers to reprocess data or recover from failures without losing information Review collected by and hosted on G2.com.

Operational complexity, High resource consumption, limited Message Queuing Features Review collected by and hosted on G2.com.

Kafka handles large volumes of data really well and is very reliable once set up properly. We use it for real-time data processing between different parts of our system. It's fast, fault-tolerant, and can scale easily when traffic grows. The publish-subscribe model makes it simple to connect producers and consumers across different services. Review collected by and hosted on G2.com.

Initial setup and configuration can be a bit tricky, especially if you’re new to distributed systems. Managing brokers and zookeepers manually can get complex. Also, monitoring and debugging issues sometimes takes extra effort unless you have good tooling in place. Review collected by and hosted on G2.com.

High scalability and performance making it ideal for growing data volumes in enterprises. Review collected by and hosted on G2.com.

It requires dedicated expertise for monitoring, scaling, and maintenance. Smaller teams often find this overwhelming. Review collected by and hosted on G2.com.

High Performance and easy to use as event driven in Microservices Review collected by and hosted on G2.com.

need more supporting AI in applications Review collected by and hosted on G2.com.