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.
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SAP Integration Suite
Score 8.5 out of 10
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SAP Integration Suite is an integration platform-as-a-service (iPaaS) that helps quickly integrate on-premises and cloud-based processes, services, applications, events, and data. It is used to accelerate innovation, automate more processes, and realize a faster time to value.
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Apache Kafka
SAP Integration Suite
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Apache Kafka
SAP Integration Suite
Free Trial
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Yes
Free/Freemium Version
No
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Premium Consulting/Integration Services
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Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Access to free tier services does not expire while there is an active Pay-As-You-Go or CPEA account with SAP. Once a free tier service limit has been reached users have the option to update from a free to a paid service plan in the same account.
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Apache Kafka
SAP Integration Suite
Features
Apache Kafka
SAP Integration Suite
Cloud Data Integration
Comparison of Cloud Data Integration features of Product A and Product B
For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).
The case of the SAP Integration Suite shows outstanding capability in handling huge data, and especially when handling corporate portfolio data with multiple owners. But we have noticed that in the cases of moving data from the older banking system, it is necessary to further refine the system. It does an excellent job of the current credit scoring.
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.
Broad Connectivity: SAP Integration Suite excels in connecting diverse systems, including SAP and non-SAP applications, databases, and third-party services.
Pre-built Integrations and Templates:The suite provides a library of pre-built integrations and templates for common business scenarios.
The Kafka Tool is a community-made Java application that looks and feels from the past century.
Logging can be confusing. This certainly shows when we have to do troubleshooting.
Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.
Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.
SAP Integration Suite is very helpful to us in many ways to manage purchase procedures, stocks, and data of vendors and suppliers. Also, it helps to manage data for service providers. SAP Integration Suite has the tool to conduct training and evaluation. Unique features like the cloud can provide access from any place and by any device is very helpful.
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
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.
Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.
We used to have a in house application in Camel and Karaf as a middleware, and after we migrate to S4HANNA we decide to give a try and move to the cloud with SAP Integration Suite, knowing the capabilities and the needs that we have in the company and the projects that were running on premise in our middleware.
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.
