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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 |
|---|---|
| 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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Reviews
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August 23, 2023
Apache Kafka - Default Choice For Large Scale Messaging
Score 8 out of 10
Vetted Review
Verified User
- 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.
August 21, 2023
Apache Kafka - FTW
- Needs at least 3 machines is a negative
- Open source is positive
- good community to get help is a positive
August 20, 2023
A Deep Dive into the Power and Potential of Apache Kafka
- Faster Implementation of Business Logics has improved productivity by 30%
- Clean Architecture leads to fewer bugs and improved business
May 30, 2023
Kafka for tracking changes
Score 8 out of 10
Vetted Review
Verified User
- 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
May 22, 2023
Confluent Kafka for messaging.
- Not yet estimated, the solution is still being developed, but we can already say it lowers time to market.
January 23, 2022
Kafka: Best Streaming Platform on the Market
- Performance at scale to tackle realtime use cases
January 21, 2022
Apache Kafka is awesome! Tricky sometimes, but we love it!
- Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
- Positive: it's scalable so we can develop small and scale for real-world scenarios
- Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.
January 06, 2022
Kafka events; helping your company work with data
- Kafka has made our indexing pipeline at least 3 times more efficient
- Records are accessible instantly when added to Kafka
- Having redundancy in the cluster has greatly helped with keeping indexing working with little effort on Operations teams.
December 06, 2021
Apache Kafka - a must have tool for distributed toolkit
- Opensource support.
- Zero-down time.
- Wide community support.
- Steep learning curve.
November 08, 2021
Kafka is an excellent tool for data integration!
- Easy data integration
August 07, 2021
Apache Kafka for your Data solutions
- Faster deployments
- Scalable solution which improves up time of systems
- Low Dev effort
- Apps with data available faster
- Minimize charge process
- Data replication with lag minor than 5 s
April 09, 2021
Apache Kafka: Where messaging meets storage
- Kafka makes the messaging itself more reliable (as it has the distributed storage by itself and the message doesn't disappear even after it has been consumed).
- Kafka can support a much higher volume use case, without too much extra pressure on the existed hardware.
March 23, 2020
Apache Kafka open source stream processing software
- Simplified logging.
- Reduced monitoring costs.
- Not expensive.
November 07, 2019
Apache Kafka for large scale message ingestion
- Positive impact on ROI since now we can use one large deployment of Apache Kafka that can be used for multiple scenarios ( storage systems, log aggregate, messaging queue ).
- It is open-source so there are no licenses or subscription fees reducing the cost of deployment.
- Data can now be ingested and analyzed in real-time making it easy to fine-tune the customer experience and decision making for internal IT.
November 06, 2019
Battle-tested, de facto option for message broker
- Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
- Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
- Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.
January 30, 2019
Kafka quick queue
- Kafka is free
- Speeds up ingest
- Faster for UI to get data
March 01, 2018
Apache Kafka, the F1 of messaging
- Easier deployment and horizontal scalability
- Messaging cost reduction
- Developments require adaptation and some paradigm shift to interoperate with Kafka