Akamai Technologies, headquartered in Boston, offers Akamai Connected Cloud, a content delivery network (CDN) with a variety of services used to guarantee application, API, and media delivery.
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Apache Kafka
Score8.9 out of 10
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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.
Akamai is a well-established brand name with a great product. It meets and many time exceeds the needs for global content delivery and security management layer at the edge. Fine grain security tuning seems cumbersome as the definition of the app keeps changing. It also requires an investment of time for the setup which doesn't necessarily make sense for smaller scale scenarios. Some products offered by AWS, Azure or the Google Cloud can be more tightly integrated with cloud provider offerings thus easier to configure.
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Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.
Content offloading - once rules are set up within Akamai you don't need to even think about how many images or large JavaScript or CSS files (for example) are being served from your own estate, Akamai takes care of it all via rules that are quick, easy and flexible.
Page caching - for pages that don't change very often Akamai allows you to set up rules, quickly and easily, to serve up your page content for you, which takes even more load away from your origin servers.
Quick rollback - the Akamai system allows easy testing of rules and changes via a staging system, and also offers a quick rollback option, which is perfect for the rare occasions when something has been set up incorrectly.
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Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).
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The interface in their control centre could be a bit more user-friendly with some of the settings in places that you wouldn't expect them to be. The search offsets this problem to an extent but it's still sometimes slower than you'd like to find what you are looking for.
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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
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Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Their support documents are excellent and provide a lot of useful information for all their services. The only reason I didn't give them a 10 is that the time it takes for them to respond to an issue could be slightly faster.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
We've used -- and considered -- a whole number of CDN offerings. The good news is that almost all of the CDN products on the market are terrific, and will pay for themselves via increased customer satisfaction and lower monthly hosting bills. For some cases, we're still more likely to suggest CloudFlare (which has a free tier) or an integrated offering from a cloud provider, like Cloudfront.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond to other needs.
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Akamai RoI improved over a period of times since it comes with heavy costs, however global deliveries in over 100 counties and global web platforms usage helps improve RoI
With the complex UI challenges, ROI could be impacted negatively when you have to invest in operations and time.
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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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
