Apache Kafka Reviews

Name: Apache Kafka
Brand: Apache
Rating: 4.1 (87 reviews)

4.1 out of 5

87 reviews
95% willing to recommend

1,102 followers

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 70% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 30% 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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Amit Laddha

Vice President (Information and Product Management) at Tradebulls Securities (P) Limited

If you are using the latest version of Apache Kafka in which the use of Zookeeper is not required, then it uses the KRaft protocol, which is built into Kafka broker. Since the use of Zookeeper is no longer required, I think the setup phase of dissolution is better than its previous versions. I rate the initial setup of the product somewhere between seven to eight out of ten. Apache Kafka's initial setup is very straightforward. The solution is deployed on an on-premises model. Apache Kafka was deployed in our company within a couple of days. Three people were involved in the deployment process of Apache Kafka.

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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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Apache Kafka mindshare

As of May 2025, the mindshare of Apache Kafka in the Streaming Analytics category stands at 2.8%, up from 1.9% 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	May 20, 2025	Download
Product	Reviews, tips, and advice from real users	May 20, 2025	Download
Comparison	Apache Kafka vs Databricks	May 20, 2025	Download
Comparison	Apache Kafka vs Amazon Kinesis	May 20, 2025	Download
Comparison	Apache Kafka vs Azure Stream Analytics	May 20, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	14.6%	96%	90 interviews Add to research
Confluent	4.1	8.2%	95%	23 interviews Add to research

Valuable Features

Apache Kafka's most valuable features include replication, partitioning, scalability, and integration with Apache Spark. It supports high availability, message retention, and robust performance. Users appreciate high throughput, consumer offset management, versatility, ease of deployment, and the ability to process messages at varying speeds. Kafka's clustering and sharding capabilities enhance scalability and fault tolerance, making it an effective choice for distributed messaging, real-time streaming, and event-driven architectures. Its open-source nature and robust community support add significant value.

"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."
"Apache Kafka is very fast and stable."

Room for Improvement

Apache Kafka could enhance its user-friendliness with a graphical interface for easier configuration and monitoring. Users express challenges with ZooKeeper dependency, documentation clarity, and Kafka's complex library. There's a need for improved integration, stability, and easier setup. Many struggle with scalability, requiring expert knowledge to manage deployments and configurations efficiently. Memory issues arise with high partition counts, and latency or data loss sometimes affects performance. Enhanced security, duplicate message handling, and cost efficiency are also desired.

"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."
"Config management can be better. We are always trying to find the best configs, which is a challenge."

ROI

Implementations of Apache Kafka generally lead to substantial returns, offering benefits like quick insights and decision-making improvements. Its open-source nature means no licensing fees, resulting in cost savings. Customizations are possible at low costs. Although some perceive longer ROI timelines, many see significant productivity gains when used correctly. High traffic management is streamlined, providing a buffer for data processing. Internal know-how often increases because of the tool’s ease of use.

Pricing

Enterprise buyers evaluating Apache Kafka can benefit from its open-source nature, which allows for free usage. However, costs arise with advanced support and cloud-based solutions, like those from Confluent, which offer tiered pricing and services. On-premise solutions may have significant expenses, with annual fees for comprehensive support and deployment. Managed services have pay-as-you-go pricing, making them cost-effective for varying needs. Overall, opting for cloud solutions may simplify initial setups compared to on-premises alternatives.

"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

Apache Kafka is predominantly used for big data processing, integrating various systems, real-time data streaming, and connecting microservices. Companies leverage it for event-driven architecture, message queuing, and analytics in numerous sectors including finance, logistics, healthcare, and security. It supports high message throughput, synchronizes applications, facilitates IoT data handling, and enables fraud detection. Businesses use it for both cloud and on-premise deployments, often in combination with tools like Apache Spark and Elasticsearch.

Service and Support

Support for Apache Kafka varies. Many users rely on the active open-source community for help, finding community-driven alternatives effective. Confluent offers professional assistance, highly valued by some, though deployment and administrative guidance could improve. Some users find licensed support from companies like Cloudera beneficial, while others experience limitations with language-specific support. Documentation, though extensive, sometimes lacks in detail for specific scenarios. Paid customers receive better service, though satisfaction levels differ.

Deployment

Many find Apache Kafka's initial setup straightforward, especially with Docker or Confluent support. Users appreciate the documentation, which aids in a smooth installation. Some encounter challenges when implementing across data centers or configuring for complex production environments. Experience and familiarity ease the process, while beginners may face difficulty without examples. Preparing for large-scale deployments requires careful planning, and setting up components like ZooKeeper adds complexity for some organizations.

Scalability

Apache Kafka exhibits strong scaling capabilities, with many finding it easy to add hardware or brokers for increased demands. A few challenges arise with balancing nodes and configurations, but horizontal scaling is a noted advantage. Users utilize Kafka across varied industries, handling significant data seamlessly. While some adjustments are required for optimal results, most experience efficient load management. In cloud environments, it offers even greater scaling flexibility.

Stability

Apache Kafka is considered stable by many entities, with some encountering occasional issues due to evolving APIs and configuration complexities. Most note its reliability and effective performance, even under high data loads. Some highlight that while Kafka is stable, external factors around it may affect performance. Stability ratings generally remain high, with a few pointing out latency spikes and resource management challenges, suggesting fine-tuning is essential for optimal results.

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

30%

Computer Software Company

12%

Manufacturing Company

Retailer

Government

Comms Service Provider

Educational Organization

Insurance Company

Energy/Utilities Company

Real Estate/Law Firm

University

Healthcare Company

Media Company

Construction Company

Hospitality Company

Wholesaler/Distributor

Transportation Company

Outsourcing Company

Legal Firm

Non Profit

Performing Arts

Aerospace/Defense Firm

Pharma/Biotech Company

Logistics Company

Recreational Facilities/Services Company

Engineering Company

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.