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Apache Kafka is a widely-used platform that has proven to be invaluable in various industries and applications. It is relied upon by …
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What is Apache Kafka?
Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical…
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What is Apache Kafka?
Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Apache Kafka Technical Details
| Operating Systems | Unspecified |
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| Mobile Application | No |
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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.
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.
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.
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August 23, 2023
Apache Kafka - Default Choice For Large Scale Messaging
Score 8 out of 10
Vetted Review
Verified User
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.
August 21, 2023
Apache Kafka - FTW
We use Apache Kafka as message broker between our two client facing applications. We used ActiveMQ before but it had shortfalls of high availability and clustering. Kafka solved it on both fronts and gives a good business continuity.
- High availability
- performance
- Admin user interface
- zookeeper logs could be better
- monitoring
January 23, 2022
Kafka: Best Streaming Platform on the Market
Apache Kafka is the most powerful and scalable streaming framework on the market. We have used Apache Kafka as a part of many real-time analytics solutions. It has a great performance [and is] easy to integrate with big data technologies like Spark. Due to its distributed nature, Apache Kafka is capable of operating very quickly and can handle millions of messages every second.
- Real time streaming
- Performance
- Scalability
- Management tools
Kafka is being used for sending log information in real time and there[fore] can monitor apps and send these events to feed other apps. It's the core for send[ing] and receiv[ing] messages due to quantity of messages per second. Helps us to scale and manage the common errors in this type of problem.
- Scalable
- Fast
- Performance
- Open source
- Performance security
- Monitoring
- Configuration
April 09, 2021
Apache Kafka: Where messaging meets storage
Apache Kafka is used by our company as the "next generation" of messaging/data-streaming pipeline solutions, to replace our old legacy JMS-based messaging solution and enable the modern streaming API based applications. When it is used for messaging purposes, we shift the responsibility of data replay from the message source (publisher application) to the message destination (consumer application). This flexibility resolved the legacy issue of sources replaying the messages but impacting all subscribers to the same topic. When Kafka is used as the streaming pipeline, it is integrated seamlessly with the Spark/Spring Stream-based analytic solutions, as it is also a kind of distributed storage.
- Undoubtedly, Kafka's high throughput and low latency feature are the highlights.
- Kafka can scale horizontally very well.
- The CLI and configuration details need to be worked out more in-depth. The naming convention of configuration is not so good and causing a lot of confusion. Sometimes there are too many configuration parameters to tune--requires the adopter to understand a lot of tricks like NFS entrapment, for example.
- Lack of a good monitoring solution so far
March 23, 2020
Apache Kafka open source stream processing software
We used it for event logging. It was used for application log collection. Was used with exception tracking and with core microservices of the web application. It helped us reduce cost and simplified operational monitoring.
- It handles large amount of data simultaneously. Makes application scalable.
- It is able to handle real time data pipeline.
- Resistant to node failure within the cluster.
- Does not have complete set of monitoring tools.
- It does not support wild card topic selection.
- Brokers and consumer pattern reduces the performance.
November 07, 2019
Apache Kafka for large scale message ingestion
Apache Kafka is used as a stream/message ingestion engine for all the customer-facing apps including some internal streams company-wide. It is used to ingest close to 2-5 million small (few bytes) messages per second that are then used for internal analytics and decision making in realtime and feed analytics backend (Tibco Spotfire).
- 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).
- The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.
- Apache Kafka does take some initial setup and deployment time especially if you haven't bought support from Confluent.
- It is not a full solution so for an analytics use case, you will still need something like Tibco.
- It does not have a SQL based query engine out-of-the-box so building/using analytics on top can be a lot of work. It would be great to have something already baked into Kafka out-of-the-box.
March 01, 2018
Apache Kafka, the F1 of messaging
Apache Kafka is becoming the new standard for messaging at our organization. Originally we limited the use to big data environments and projects but as the technology is becoming more mature we think it will eventually replace classical messaging software.
- High volume/performance throughput environments
- Low latency projects
- Multiple consumers for the same data, reprocessing, long-lasting information
- Still a bit inmature, some clients have required recoding in the last few versions
- New feaures coming very fast, several upgrades a year may be required
- Not many commercial companies provide support