Apache Kafka Reviews

Name: Apache Kafka
Brand: Apache
Rating: 4.1 (88 reviews)

4.1 out of 5

88 reviews
96% willing to recommend

What is Apache Kafka?

Apache Kafka is an open-source distributed streaming platform that serves as a central hub for handling real-time data streams. It allows efficient publishing, subscribing, and processing of data from various sources like applications, servers, and sensors.

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Apache Kafka is the #8 ranked solution in Streaming Analytics tools. PeerSpot users give Apache Kafka an average rating of 8.2 out of 10. Apache Kafka is most commonly compared to Databricks: Apache Kafka vs Databricks. Apache Kafka is popular among the large enterprise segment, accounting for 66% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 26% of all views.

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Featured Apache Kafka reviews

Snehasish Das

Technology Leader at eTCaaS

I worked with Apache Kafka for customers in the financial industry and OTT platforms. They use Kafka particularly for data streaming. Companies offering movie and entertainment as a service, similar to Netflix, use Kafka Apache Kafka offers unique data streaming. It allows the use of data in…

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Eyob Alemu

Technical Director at NIDP

We use Kafka for a stage event-driven process from a process perspective. Our platform is an ID platform, so after registration data is received, it has to be stored from various registration locations. The process includes stages like quality checking, consistency, format, biometric data checking…

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Vishal M Godi

Big Data Teaching Assistant at Center for Cloud Computing and Big Data, PES University

Kafka is used as a streaming platform where multiple producers and consumers exchange high load and high volume of messages asynchronously without affecting each other's performance. It serves as an industry-standard platform for such operations. Kafka is also integrated into data system…

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Apache Kafka mindshare

As of August 2025, the mindshare of Apache Kafka in the Streaming Analytics category stands at 3.5%, up from 2.0% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Streaming Analytics

PeerResearch reports based on Apache Kafka reviews

Type	Title	Date
Category	Streaming Analytics	Aug 1, 2025	Download
Product	Reviews, tips, and advice from real users	Aug 1, 2025	Download
Comparison	Apache Kafka vs Databricks	Aug 1, 2025	Download
Comparison	Apache Kafka vs Amazon Kinesis	Aug 1, 2025	Download
Comparison	Apache Kafka vs Confluent	Aug 1, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	13.5%	96%	91 interviews Add to research
Confluent	4.1	8.3%	95%	23 interviews Add to research

Valuable Features

Valuable features of Apache Kafka include replication, partitioning, and scalability. Users appreciate its high throughput, robust performance, and ease of integration with Apache Spark. Kafka supports large-scale data streaming, with reliability and real-time processing capabilities. It allows for message retention, replay, and clustering while providing efficient message delivery and persistence. Kafka's flexibility, fault tolerance, and strong community support enhance its appeal for event-driven architectures and microservices applications.

"I appreciate that Apache Kafka is fast and secure thanks to implementing it with AWS, allowing me to secure it on a high level."
"Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing."
"Overall, I rate Apache Kafka as nine out of ten for its scalability and stability."

Room for Improvement

Apache Kafka users highlight areas for improvement, including the need for better user interfaces and more comprehensive monitoring tools. Many find the dependency on ZooKeeper problematic and desire enhancements in API stability, configuration, and integration capabilities. Enhanced support for Windows and reductions in complexity, memory usage, and latency issues are frequently mentioned. Users also suggest improvements in documentation, authentication management, and better orchestration for managing large data streams.

"For personal preferences, since we use Managed Kafka in AWS, I would appreciate having some kind of UI integrated into Apache Kafka for connecting to it because using code to connect it is basic, but we can use a UI."
"We haven't seen a return on investment with Apache Kafka. It's used for a specific use case rather than cost reduction."
"In the data sharing space, the performance of Apache Kafka could be improved. The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds."

ROI

Many observed a rapid ROI when Apache Kafka was used for high-value projects, benefiting from open-source cost savings and efficient data handling. It reduced decision-making time from years to months and offered significant savings in analytics, contributing to year-on-year profit increases. Not all experienced similar gains, with some reporting slower ROI primarily for log management projects. Successful implementations required skilled developers, though many still enjoyed substantial investment returns in various applications.

Pricing

Apache Kafka offers a predominantly open-source model, making it free to use, with minimal setup costs for enterprises opting for self-managed options. Some opt for support from providers like Confluent or managed services from AWS, Azure, or Google Cloud, which entail subscription or pay-as-you-go costs. Enterprise versions, such as those integrated with Cloudera, involve significant expenses, potentially exceeding $100,000 annually, depending on data volume, support levels, and specific capabilities.

"I was using the product's free version."
"Apache Kafka is an open-source solution."
"It's quite affordable considering the value it provides."

Popular Use Cases

Entities utilize Apache Kafka primarily for real-time data streaming, data integration, and message queuing across various industries such as finance, healthcare, and IoT. They leverage it for tasks like event monitoring, log aggregation, fraud detection, and data processing from multiple sources. Additionally, organizations implement Kafka to handle high-velocity data, enable microservices communication, and facilitate data enrichment and transformation, often integrating it with systems like Kafka Streams and Elastic Stack.

Service and Support

Apache Kafka's customer service relies heavily on its open-source community, with many users managing support internally or through partners like Confluent. Satisfaction varies; some find community resources sufficient, while others opt for paid support through vendors offering more structured guidance. Despite some gaps in deployment assistance, paid services generally provide quicker response times. Community engagement and third-party offerings significantly assist in overcoming technical hurdles, proving crucial for enterprises utilizing Kafka.

Deployment

Users experience varied complexity with Apache Kafka's initial setup. Some find it straightforward, especially with Docker or pre-configured versions, while others face challenges, especially in production environments. Common difficulties include multi-zone deployments, integrating with ZooKeeper, and large data volumes. Documentation is generally helpful, and experienced teams manage setup efficiently. Clusters and configurations are easy for development but can be complex for production, requiring careful planning and attention to architecture details.

Scalability

Apache Kafka shows remarkable adaptability, handling extensive user loads efficiently. Users note its horizontal scalability allowing expansion by adding nodes. Performance meets demands even with high data throughput, supporting large teams across multiple sectors. Concerns exist about manual efforts in some setups, yet in cloud environments, scaling remains straightforward. Some users appreciate ease of growth without issues from increased data volume, rating Kafka's capability as superior compared to other streaming tools.

Stability

Most users report Apache Kafka as stable, handling large data volumes effectively. Some face occasional configuration challenges with evolving APIs and minor stability issues, but solutions generally perform reliably. Partitioning features improve system stability, and Kafka's strong resilience and consistency are praised. However, certain users mention dependencies on Java increasing complexity. While some have experienced minor glitches, stability ratings commonly fall between seven to nine out of ten, indicating a high level of reliability.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Apache Kafka Buyer's Guide for additional reliable information.

Review data by company size

By reviewers

By visitors reading reviews

Top industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

12%

Manufacturing Company

Retailer

Government

Insurance Company

Educational Organization

Comms Service Provider

Healthcare Company

Energy/Utilities Company

University

Media Company

Real Estate/Law Firm

Performing Arts

Outsourcing Company

Construction Company

Non Profit

Transportation Company

Aerospace/Defense Firm

Hospitality Company

Pharma/Biotech Company

Wholesaler/Distributor

Legal Firm

Recreational Facilities/Services Company

Logistics Company

Consumer Goods Company

Marketing Services Firm

Compare Apache Kafka with alternative products

Learn more about Apache Kafka

Kafka's core benefits include high scalability for big data pipelines, fault tolerance ensuring continuous operation despite node failures, low latency for real-time applications, and decoupling of data producers from consumers.

Key features include topics for organizing data streams, producers for publishing data, consumers for subscribing to data, brokers for managing clusters, and connectors for easy integration with various data sources.

Large organizations use Kafka for real-time analytics, log aggregation, fraud detection, IoT data processing, and facilitating communication between microservices.