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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Qlik Replicate
Score 8.4 out of 10
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Qlik Replicate enables organizations to accelerate data real-time replication, ingestion and streaming via change data capture, across a wide range of heterogeneous databases, data warehouses and data lake platforms.
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.
Qlik Replicate works very well with relational data platforms, both on premise and in the cloud, for example Oracle, SQL Server, PostgreSQL, MySQL and others, it also works very well with DB2. If the data source is MongoDB, it is more complicated and currently there is no possibility of sending data to MongoDB.
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).
Replicate is extremely stable and does not generate a lot of alerts/failures/issues that take up time to troubleshoot.
It is very easy to add new source tables to a Replicate task so that we're always in sync with new data available from the CRM.
It's nice that Qlik Replicate also allows you to create a job to stop and then restart your tasks during maintenance windows that occur on both the source and target systems.
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
The base Replicate web GUI is lacking. If you have dozens or more tasks, it's hard to get a sense of how they're performing. Enterprise Manager solves all of these problems but is a separate install.
The support portal is extremely difficult to navigate. It's hard to track down exactly what you're looking for.
It would be helpful to have better documentation and example queries for the tables in the Enterprise Manager analytics database.
Destination databases that don't support common DDL commands behave unpredictably. And the replication of schema changes isn't consistent.
The availability of the replicated data in disparate environments has is now crucial. Replacing a product like Qlik Replicate would require significant time, investments, and work. In addition, Qlik Replicate is reasonably reliable with few failures.
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
We now have greater business flexibility and scalability, and our big data integration projects have a quick rate of growth, which has been profitable for us. Independent of the sources involved, maintaining data consistency between sources is easy. One of my favorite features is the way it lets owners of the source system start and stop processes from updating their system windows.
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.
The issue I've had is that Qlik does an awful job of keeping their customers informed when new versions of the software are available. We found that we were using a version that was no longer supported and could never get help. When it came time to get us upgraded so that we were on a current version, no one knew how to help get us to where we needed to be. We had to purchased professional services time and even then I was basically on my own to get everything built out and set up. Qlik needs to be more proactive with communicating about new releases and how to get your version upgraded in the most secure, safe way possible.
Follow the directions from the Qlik documentation. They are pretty straight forward and easy enough to follow. If you follow these, then you are not likely to have issues on implementation.
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.
Great tool for data replication solution for Oracle/SQLServers/etc. Real easy to get it set up and start realizing business value. Getting the PoC accomplished in a short window. Product costing and easy to start small and scale as needed. It helped cover most of our ask compared to other solutions.
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.
Prior to using Qlik Replicate, we used an ETL solution to copy data from the Oracle ERP system to the Microsoft SQL Server BI system at a 15-minute interval. It was very tedious to maintain. Qlik Replicate is much easier to use and we replicate data near real-time now.
