Name: Apache Kafka
Rating: 8.2 (140 reviews)
Author: Apache

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### Message Queuing
Reviewers generally agree that Apache Kafka's message queuing capabilities are a key strength. Users appreciate Kafka's ability to handle large volumes of messages efficiently and reliably, ensuring that data is transmitted in a fault-tolerant manner. The platform's message queuing functionality is particularly praised for its role in facilitating communication between different systems and enabling real-time data processing. Despite some minor challenges in monitoring Kafka deployments and a learning curve in setting up topics, users find that Kafka's message queuing features significantly enhance their asynchronous activities and microservice communication. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-08-15-12-40-41), [2](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [3](https://www.trustradius.com/reviews/apache-kafka-2023-08-16-10-33-21), [4](https://www.trustradius.com/reviews/apache-kafka-2021-10-20-01-51-36), [5](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-09-16-04), [6](https://www.trustradius.com/reviews/apache-kafka-2023-05-13-10-57-27), [7](https://www.trustradius.com/reviews/apache-kafka-2021-12-04-02-58-10), [8](https://www.trustradius.com/reviews/apache-kafka-2021-10-30-10-37-27), [9](https://www.trustradius.com/reviews/apache-kafka-2021-06-25-02-03-15), [10](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-11-53-00), [11](https://www.trustradius.com/reviews/apache-kafka-2020-02-18-11-49-23), [12](https://www.trustradius.com/reviews/apache-kafka-2019-10-04-14-08-41), [13](https://www.trustradius.com/reviews/apache-kafka-2020-03-22-10-29-45), [14](https://www.trustradius.com/reviews/apache-kafka-2019-11-05-11-44-09))

### Data Streaming
Users consistently praise Apache Kafka for its robust data streaming capabilities. Reviewers highlight that Apache Kafka excels in handling large volumes of data in real-time, making it a reliable choice for streaming and processing data efficiently. The platform's ability to scale, integrate with various big data technologies, and provide fault-tolerant architecture for data transit receives positive feedback from users across different industries. Additionally, Apache Kafka's performance, scalability, and ease of integration with other systems contribute to its reputation as a top-tier data streaming solution. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-05-18-03-22-16), [2](https://www.trustradius.com/reviews/apache-kafka-2023-08-16-10-33-21), [3](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-11-53-00), [4](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [5](https://www.trustradius.com/reviews/apache-kafka-2021-07-28-10-04-15), [6](https://www.trustradius.com/reviews/apache-kafka-2021-10-20-01-51-36), [7](https://www.trustradius.com/reviews/apache-kafka-2021-12-04-02-58-10), [8](https://www.trustradius.com/reviews/apache-kafka-2021-06-25-02-03-15), [9](https://www.trustradius.com/reviews/apache-kafka-2020-03-22-10-29-45), [10](https://www.trustradius.com/reviews/apache-kafka-2020-02-18-11-49-23), [11](https://www.trustradius.com/reviews/apache-kafka-2019-11-05-11-44-09))

### Event Processing
Users generally believe that Apache Kafka's event processing capabilities are a standout feature according to the reviews provided. The consensus among reviewers is that Kafka excels in handling event-driven architectures, processing data in real-time, and ensuring fault tolerance in high-volume processing scenarios. Users appreciate Kafka's ability to efficiently queue records, provide easy expansion options for topics, and offer various tools for managing and maintaining queues. Additionally, Kafka's event processing functionality is praised for its scalability, reliability, and performance in processing a large volume of messages. Despite some challenges such as monitoring difficulties and a learning curve in setting up brokers and topics, users find Apache Kafka to be a valuable tool for event processing in various use cases. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-08-15-12-40-41), [2](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [3](https://www.trustradius.com/reviews/apache-kafka-2023-05-18-03-22-16), [4](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-09-16-04), [5](https://www.trustradius.com/reviews/apache-kafka-2021-10-30-10-37-27), [6](https://www.trustradius.com/reviews/apache-kafka-2021-07-28-10-04-15), [7](https://www.trustradius.com/reviews/apache-kafka-2021-10-20-01-51-36), [8](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-11-53-00), [9](https://www.trustradius.com/reviews/apache-kafka-2021-12-04-02-58-10), [10](https://www.trustradius.com/reviews/apache-kafka-2021-06-25-02-03-15), [11](https://www.trustradius.com/reviews/apache-kafka-2020-03-22-10-29-45), [12](https://www.trustradius.com/reviews/apache-kafka-2019-10-04-14-08-41), [13](https://www.trustradius.com/reviews/apache-kafka-2020-02-18-11-49-23), [14](https://www.trustradius.com/reviews/apache-kafka-2019-11-05-11-44-09))

### Consumer Management
Reviewers generally praise Apache Kafka's consumer handling capabilities. Users appreciate the FIFO message delivery system and the Dead Letter Queue feature for error handling. However, some users have raised concerns about the lack of complete monitoring tools and the performance impact of brokers and consumer patterns. Despite these drawbacks, Apache Kafka is widely regarded as a reliable and scalable solution for consumer management in messaging and data-streaming pipelines. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [2](https://www.trustradius.com/reviews/apache-kafka-2023-08-15-12-40-41), [3](https://www.trustradius.com/reviews/apache-kafka-2021-10-20-01-51-36), [4](https://www.trustradius.com/reviews/apache-kafka-2020-03-22-10-29-45), [5](https://www.trustradius.com/reviews/apache-kafka-2020-02-18-11-49-23), [6](https://www.trustradius.com/reviews/apache-kafka-2019-10-04-14-08-41))

### Topic and Partition Handling
Users generally praise Apache Kafka for its robust topic and partition handling capabilities. The partition mechanism, along with the ease of configuration and integration with schema registry, receives positive feedback. However, some users have highlighted concerns such as limitations with a high number of small topics and issues with tenant isolation affecting performance. Despite these drawbacks, the majority of users appreciate Kafka's scalability and reliability in managing topics and partitions efficiently. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [2](https://www.trustradius.com/reviews/apache-kafka-2021-12-04-02-58-10), [3](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-09-16-04), [4](https://www.trustradius.com/reviews/apache-kafka-2019-10-04-14-08-41))

### Data Ingestion and Pipeline Architecture
Reviewers consistently praise Apache Kafka's data ingestion and pipeline architecture capabilities. Users highlight its ability to efficiently handle high data loads and provide fault-tolerant architecture for real-time processing. The platform's feature-rich design enables the ingestion of millions of messages per second, supporting internal analytics and decision-making processes. While some caution against using Kafka as a long-term data storage solution, its strength lies in its robust data ingestion mechanisms and reliable pipeline architecture for stream processing. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-08-15-12-40-41), [2](https://www.trustradius.com/reviews/apache-kafka-2023-05-18-03-22-16), [3](https://www.trustradius.com/reviews/apache-kafka-2021-12-04-02-58-10), [4](https://www.trustradius.com/reviews/apache-kafka-2019-10-04-14-08-41), [5](https://www.trustradius.com/reviews/apache-kafka-2019-11-05-11-44-09))

### Configuration and Deployment
Reviewers generally express mixed opinions regarding Apache Kafka's configuration and deployment capabilities. While some users appreciate its scalability and suitability for both on-premises and cloud-based use cases, others highlight areas for improvement. Common concerns include the complexity of Zookeeper services configuration, the need for enhanced data logging security, and challenges related to monitoring Kafka deployments effectively. Suggestions for enhancement include simplifying the process for local deployment, providing a user-friendly interface for visibility into topics and messages, and streamlining the learning curve for creating brokers and topics. Additionally, users point out the need for improved management features, such as simplifying Zookeeper configuration, enhancing front-end development for property configuration, and integrating with schema registry tools like Avro. Despite these challenges, Apache Kafka's configuration and deployment functionalities are seen as integral components of its overall utility. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-05-18-03-22-16), [2](https://www.trustradius.com/reviews/apache-kafka-2023-08-15-12-40-41), [3](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [4](https://www.trustradius.com/reviews/apache-kafka-2021-12-04-02-58-10), [5](https://www.trustradius.com/reviews/apache-kafka-2021-06-25-02-03-15), [6](https://www.trustradius.com/reviews/apache-kafka-2021-10-20-01-51-36), [7](https://www.trustradius.com/reviews/apache-kafka-2020-02-18-11-49-23), [8](https://www.trustradius.com/reviews/apache-kafka-2019-11-05-11-44-09))

### Fault Tolerance and High Availability
Users consistently praise Apache Kafka for its fault tolerance and high availability features, highlighting its significant impact on operational efficiency and data processing capabilities. The reviews indicate that Kafka's fault tolerance mechanisms, such as handling errors using Dead Letter Queue and providing redundancy in the cluster, have been instrumental in ensuring smooth data flow and minimal disruptions. Additionally, users appreciate Kafka's high availability, which contributes to business continuity and reliable message delivery between applications. The consensus among reviewers is that Apache Kafka excels in providing robust fault tolerance and high availability solutions, enhancing the overall reliability and performance of data processing workflows. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-08-16-10-33-21), [2](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [3](https://www.trustradius.com/reviews/apache-kafka-2023-08-15-12-40-41), [4](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-09-16-04), [5](https://www.trustradius.com/reviews/apache-kafka-2021-12-04-02-58-10))

### Monitoring and Logging
Reviewers have varying opinions on Apache Kafka's monitoring and logging features. Some users have highlighted challenges with monitoring Kafka deployments, suggesting the need for a separate monitoring dashboard for better visibility. Others have mentioned the complexity of CLI monitoring and the learning curve associated with it. Additionally, concerns have been raised about the lack of a built-in logging tool and the need for improved performance security and configuration options. Despite these challenges, users have acknowledged the benefits of simplified logging and reduced monitoring costs when utilizing Apache Kafka for event logging and application log collection. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-kafka-2023-08-16-10-33-21), [2](https://www.trustradius.com/reviews/apache-kafka-2023-08-15-12-40-41), [3](https://www.trustradius.com/reviews/apache-kafka-2023-05-18-03-22-16), [4](https://www.trustradius.com/reviews/apache-kafka-2023-05-22-20-37-18), [5](https://www.trustradius.com/reviews/apache-kafka-2021-10-20-01-51-36), [6](https://www.trustradius.com/reviews/apache-kafka-2021-07-28-10-04-15), [7](https://www.trustradius.com/reviews/apache-kafka-2022-01-06-09-16-04), [8](https://www.trustradius.com/reviews/apache-kafka-2021-06-25-02-03-15), [9](https://www.trustradius.com/reviews/apache-kafka-2020-03-22-10-29-45), [10](https://www.trustradius.com/reviews/apache-kafka-2020-02-18-11-49-23))

Users have recommended using Apache Kafka for various messaging platform requirements. It integrates easily with multiple programming languages, offers stream processing capabilities, distributed data storage, and the ability to handle multiple requests simultaneously.

Another common recommendation is to consider Apache Kafka as a messaging broker due to its extensive feature set and guaranteed delivery of data to consumers. Users find it highly supported and widely used within the community.

Users also recommend Apache Kafka for streaming large amounts of data. They praise its scalability and ease of use, although they mention that manual rebalancing of partitions may be required when adding or deleting nodes. Additionally, users appreciate that Kafka allows connections between multiple producers and consumers with low resource consumption.

Overall, Apache Kafka is regarded as a practical choice for message processing systems, data streaming, and handling large volumes of data due to its stability, scalability, and diverse features.

Difficulty Monitoring Kafka Deployments: Some users have found it difficult to monitor their Kafka deployments and have expressed a desire for a separate monitoring dashboard that would provide them with better visibility into their topics and messages.

Steep Learning Curve for Creating Brokers and Topics: The process of creating brokers and topics in Kafka has been described as having a steep learning curve by some users, who believe that it could be simplified to make it more accessible.

Outdated Web User Interface: The web user interface of Kafka has not been updated in years, leading some users to feel that it lacks a streamlined user experience. They express the need for a more modern interface instead of relying on third-party tools.

Apache Kafka is a widely-used platform that has proven to be invaluable in various industries and applications. It is relied upon by organizations to have real-time communication and keep order information up-to-date. This is particularly useful for organizations that need to process large volumes of data, such as those in the cybersecurity industry. Apache Kafka is also considered the go-to tool for event streaming, generating events and notifying relevant applications for consumption. Additionally, it is used in both first-party and third-party components of applications to address data proliferation and enable efficient notifications.

Another key use case for Apache Kafka is replacing classical messaging software within organizations, becoming the new standard for messaging. This powerful streaming framework plays a crucial role as a queuing mechanism for records in various pipelines, providing a simple yet efficient system for queuing and maintaining records. Moreover, Apache Kafka excels at storing and processing records in dedicated servers, supporting high data loads and offering the ability to replay consumed data. This makes it ideal for buffering incoming records during traffic spikes or in case of data infrastructure failures.

Furthermore, Apache Kafka finds its purpose in driving real-time monitoring by sending log information to feed other applications. Its ability to scale and manage common errors in messaging allows organizations to handle large quantities of messages per second without compromising performance. Another notable use case involves Apache Kafka acting as an efficient stream/message ingestion engine for customer-facing applications, enabling internal analytics and real-time decision-making.

Additionally, Apache Kafka integrates seamlessly with big data technologies like Spark, making it a valuable addition to big data ecosystems. Organizations have successfully replaced legacy messaging solutions with Apache Kafka, thanks to its ability to serve as a messaging and data-streaming pipeline solution. It enables modern streaming API-based applications while ensuring high availability and clustering as a message broker between client-facing applications.

Moreover, Apache Kafka serves as an ingress and egress queue for big data systems, facilitating data storage and retrieval processes. It also acts as a reliable queue for frontend applications to retrieve data and analytics from MapR and HortonWorks. With over five years of being utilized in data pipelines, Apache Kafka has consistently demonstrated excellent performance and reliability.

In summary, Apache Kafka proves to be versatile and essential across various industries and use cases. It facilitates real-time communication, ensures data integrity, enables efficient event streaming, replaces classical messaging software, and supports high scalability and fault tolerance. With its robust capabilities, Apache Kafka continues to be the go-to solution for organizations seeking to streamline their data processing and communication systems.

Fault tolerance and high scalability: Users have consistently praised Apache Kafka for its fault tolerance and high scalability. Many reviewers have stated that Kafka excels in handling large volumes of data and is considered a workhorse in data streaming. 

Ease of administration: Reviewers appreciate Kafka's ease of administration, noting that it offers an abundance of options for managing and maintaining queues. Multiple users have mentioned that the platform allows for easy expansion and configuration of cluster growth, making it straightforward to administer.

Real-time streaming capabilities: Kafka's real-time streaming capabilities are seen as a significant advantage by users. Several reviewers have highlighted the platform's ability to handle real-time data pipelines and its resistance to node failure within the cluster. This feature enables users to process asynchronous data efficiently and ensures continuous availability of the system.

Apache Kafka

A Message Queue is a component that facilitates information exchange between processors, effectively holding messages until they are processed by some component service in a system. They may be used to hold and pass messages in an operating system, within an application, or between computer systems.

Message Queue

Information Technology & Services

Pharmeasy

Senior Engineering Manager

Likelihood to Recommend

Likelihood to Renew

Usability

Support Rating

The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field.

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MySQL

Amazon Simple Queue Service (SQS)

Apache Kafka 2024-12-11 23:26:26

Apache Spark

Amazon Elastic Compute Cloud (EC2)

Amazon Elastic Kubernetes Service (EKS)

Engineer

Apache Kafka 2023-05-18 03:22:16

Prometheus

Grafana

Apache ZooKeeper

Senior Member of Technical Staff

Apache Kafka 2023-05-22 20:37:18

Amazon Aurora

NGINX

Amazon Athena

Apache Airflow

Co-Founder & CTO

Apache Kafka 2023-08-15 12:40:41

Google Cloud SQL

Google Cloud Pub/Sub

Google Kubernetes Engine

Google Cloud Identity

Apache Kafka 2023-05-13 10:57:27

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TrustRadius

Monitoring tools

Naming convention

Use case

Management interface

Bad data

Sql based

Management tools

Admin tool

Not support

Lot of work

Open source

Low latency

High throughput

High volume

Real time

Highly scalable

Capture data

Easy to understand

Easily scalable

Ability to process

Home

Message Queue Software

We use Apache Kafka for asynchronous communication. 
For any processing that we need to do on background, we use Apache Kafka. We also set the configurations in such a way so that we can use it for retrying messages in a topic.
We also use it for data streaming which powers our data platform.

Its extremely fast. It is able to deliver messages very quickly.
It is very reliable, I have not yet seen any cases where messages might have dropped
Using different configurations we can model it any way and cater to large number of business use cases.

If there can be some way of scheduling messages to reappear that would be great.
There should be functionality of decreasing the partitions on the fly so that we can scale down when needed.
Apache Kafka should have better consumer UI view so that we can more details on the consumers attached.

It has reduced overhead of managing a different system for maintaining infra of asynchronus programming.
Due to its simplicity and reliability we don't have to invest much on training engineers on it.

Amazon Simple Queue Service (SQS) and RabbitMQ

Apache Kafka is really the bedrock of all things streaming and data processing. I cannot imagine if there is any other product that does it better. My last 2 companies used it, and my current one does so as well. If you want your data stream to be organized and sent, Apache Kafka has become the tool of choice. I have dabbled in Azure EventHubs as well, if you are into opensource data streaming, Apache Kafka will take you where you need to be for data lakes and the amount of data that is streamed for the cybersecurity industry that my company is in. Without Apache Kafka, there is no way that my company products can handle the volume of data that we process for our customers.

Data streaming is really second to none.
Scaling, done right, Apache Kafka is a workhorse.
Ease of administration - Although you cannot really compare to Azure EventHubs, but that is comparing between Apples and Oranges.

The web UI has not really changed in years. UX has been refreshed, but a more streamlined UX instead of many 3rd party webUX tools, will be most welcome.
Webhooks can still be tricky to troubleshoot at times.
CLI monitoring is a learning curve to get it right.

Well known and known set of tools from setup to admin.
Scalability.
Fit for use in both onprem, and cloud-base use cases.

Being an open-source tool, Apache Kafka is invaluable to my company's product. I cannot imagine how much it is if we are using Amazon Kinesis or Azure EventHubs.
The negative part will be in the event of Apache Kafka failures, the trouble-shooting can really be a pain and bane. But given enough exposure to its inner workings, Apache Kafka still comes out OK.
Having used Apache Kafka for years in this company, I can only say without Apache Kafka, my company would not be cost-efficient and would be much more costlier to sell to customers if we were paying on top of Azure Event hubs or Amazon Kinesis.

Amazon Elastic Kubernetes Service (EKS), Apache Spark, Amazon Elastic Compute Cloud (EC2)

We use Apache Kafka to stream order information across systems. An order may go through certain updates through its lifecycle. These updates need to be communicated to the systems in near real time and we rely on Kafka for this.Our business use case is to take these orders up with the insurance companies for approval and thus the order information need to be up to date. Kafka has been excellent at doing this so far.

Receiving messages from publisher and sending to consumer in FIFO manner
Handling of errors using Dead Letter Queue when message could not be consumed on the consumer end
Fault tolerance

Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
Learning curve around creation of broker and topics could be simplified

High throughput
Low latency
Fault tolerance

We are able to submit orders to the insurance companies with almost 100% accuracy because we receive Kafka updates in almost real time
We are getting notified of error scenarios separately because of our Dead Letter Queue implementation so that we can handle those cases
There is certain engineering effort being spent to maintain Kafka

Publishing messages to other services to inform them of modification in an entity
Publishing messages to Camunda to mark a user task as complete

We use Apache Kafka as an event bus for all our async activities &amp; Micro Service Communication, like sending emails, SMS, and notifications between services and consumers and for event &amp; data processing.

Event driven architectures
Any use case which requires async data processing
Any use case with production and consuming the same data to build business-specific processing

Zookeeper services configuration can be simplified
Data logging needs to be secured
Restarting & overall management needs to be improved

Event-Driven Architecture
Super fast processing capability & data retention
Fault Tolerance & High Volume Processing

Faster Implementation of Business Logics has improved productivity by 30%
Clean Architecture leads to fewer bugs and improved business

Amazon Kinesis, Amazon Simple Queue Service (SQS) and RabbitMQ

Amazon Aurora, Amazon Athena, NGINX, Apache Airflow

Currently consulting and implementing for a bank, we use a cloud-native Kafka solution (Confluent Kafka) for brokering. The solution is well documented, and liked by the developers but lacks certain technical aspects to improve usability and administration.

Private access to a cluster.
Visualisation solutions.

Brokering & message consumption.
Cluster & topic definition.

Apache Kafka

Overview

What is Apache Kafka?