Apache Kafka vs. SharePlex

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).

Incentivized

SharePlex is best suited for replicating relatively small but steady streams of data, instead of huge amounts of data surge. Keep in mind that it relies on database logs (redo logs in Oracle) to replicate data in the form of "messages", and therefore it is mandatory to put the database in forced logging mode - that may be a prohibiting factor in some situations.

Incentivized

• 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.

Incentivized

• Replicate data for Dev/QA
• Easy setup/upgrade
• Support is easily reachable

Incentivized

• 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.

Incentivized

• Documentation: There are some areas where the documentation is weak or dated. In particular, running SharePlex on RAC is a more involved process than the implementation guides would have you believe. When reaching out for assistance, the answer from Dell (a few years ago) was to engage their consulting team.
• Built in scripts/agents: SharePlex comes with a handful of monitoring and management scripts that can be run as cron jobs. Unfortunately, these scripts are primitive and rigid, requiring the user to hard-code values into the script body and limiting their usefulness when you're running multiple replication streams. Some of the methods and dependencies are heavy. We ended up writing custom scripts that do what we need but it would be helpful if there were greater functionality and flexibility out of the box.
• Compared to other tools, there is not as wide (or at least as active) a user presence. If you're having difficulties you may have to engage a consultant since it's less likely that you'll find an answer in a forum or blog post.

Incentivized

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.

We depend on it to supply data to our various OLTP and Data Warehouse environments

Incentivized

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

Incentivized

It is very usable once you get over the initial setup.

Incentivized

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.

Incentivized

Support is very responsive. They take ownership of the problems and see them through to the finish. When a bug is found they work towards developing testing and making the fix available to us to solve the problem.

Incentivized

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.

SharePlex was a clear winner in comparison to its competitors. It beats GoldenGate on price, providing equivalent features and performance at a fraction of the price and without the need to license costly Oracle features at the database level. DB Visit was a strong contender for us as well and it is an excellent product I would also recommend, but it did not have quite enough of a user and community presence and penetration for our management to feel comfortable with.

Incentivized

• 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.

Incentivized

• Data migration allows the company to make solid business changes, more so on new business plans.
• Further, system flexibility supports multiple business plans, hence, efficient and reliable.
• Finally, real-time decisions are arrived at using SharePlex, a procedure that ensures there is transparency in the entire operations.

Incentivized