Apache Spark Streaming vs. Confluent vs. Tealium Customer Data Hub

Apache Spark Streaming is a tool that we are using for almost a year and is excellent in managing batch processing. It is user-friendly. Using it, we can even process our massive data in fractions of seconds. Its pricing is its other plus point. Only its In-memory processing is its demerit as it occupies a large memory.

Incentivized

If you have a need to stream data, real time or segmented structured data then Confluent is a great platform to do so with. You won't run into packet transfer size limitations that other platforms have. Flexibility in on-prem, cloud, and managed cloud offerings makes it very flexible no matter how you choose to implement.

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Well suited: Any company that has a variety of data interspersed across multiple systems and is trying to get a more unified understanding of their customer profile—that enriched customer view of having all these disparate data sources and wanting to bring them together in a relatively seamless manner, and then having that more nuanced understanding of the entirety of the customer and being able to action off of it. Which again, I feel like is a core marketing use case.

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• It is amazing in solving complicated transformative logic.
• It is straightforward to program.
• It is a very quick tool.
• It processes large data within a fraction of seconds.

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• Products work great.
• Training is available.
• Customer support is good.

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• Activation in real time - it's really good and you can literally see the information being activated in all other technologies that my clients use, like TikTok, Facebook, Google, et cetera. At the time that a client arrives on your website, you have instant data to enrich the profiles and activate on this channel. So this is really good and it's something that Tealium give us the control in order to do that.
• The other thing is that you have the power to choose what data you want to collect and what data you do not want to collect. Tealium is particularly very good on doing that.

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• There must be more documentation.
• It is a profoundly complex tool.
• Its in-memory processing consumes massive memory.

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• Cloud based Azure platform features for Confluent lacks behind AWS And GCP

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• Audiences—don't they technically exist in Tealium? They are just streamed—no count, no backfill, etc.
• Working backward to identify issues involves lots of clicking in the UI, going from audience to audience attribute, badge to event attribute, and so on.
• You have to wait for a Real-Time event to see the payload. There is no sample or other option.

Incentivized

I already know that my company has no plans to discontinue use of Tealium. We are heavily reliant on it due to a huge number of product teams and developers we would have to work with to place tags across many pages. Tealium is already there on the pages, and our application/product teams are familiar with how to integrate it. It is just the simplest way to ensure that new data requirements are implemented in a timely manner.

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Once you understand how it works and how to implement things, it is a dream and very user friendly. There is the initial learning curve but there are oceans of helpers available to you to help you bridge that initial gap and get going. Take the time to learn it up front and you will never have any problems. And support is great and available to answer questions

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It's been flawless as far as I am concerned.

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Sometimes publishing can take longer than expected

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The support from the Confluent platform is great and satisfying. We have been working with Confluent for more than a year now. They sent out resident architects to help us set up Confluent cluster on our cloud and help us troubleshoot problems we have encountered. Overall, it has been a great experience working with the Confluent Platform.

Incentivized

The support team often is so quick to respond and so helpful when it comes to working with the needs of my clients and being able to resolve potential shortcomings or technical issues or surrounding the tool. There have been times more recently that I’ve gotten more generic service rather than the tailored experience that I have come to expect.

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Did what we needed to onboard us onto the platform

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Implementation had some bumps in the road and it was new for all of us, but for the most part, it was easier than many other implementations we've done with other technologies.

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Apache Spark Streaming stands above all the huge data transformative tools because of its speed of processing which was quite slow in Presto as it takes a lot of our time in the data processing. Spark, comfortably provides integration with Jupyter like notebook environment. and Spark's combination with Jupyter and Python results in enhancing the speed .

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For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others scale options and cost to run it was more than financially viable as a platform solution to our global operations.

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Tealium Customer Data Hub can do it all in one. Whereas, I think by using multiple Tealium Customer Data Hubs, we can utilize what each tool is really good at. In an ideal world, we'd like to use Tealium Customer Data Hub for everything, but one thing that we struggle with is Audience Segmentation, and we are looking for a one touch solution, without a lot of the work since the data is already there.

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Like anything it could be cheaper but we still feel we get value from it.

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Never had an issue, scales as we use it.

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Very easy to work with and relatively cost effective

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• Cost and time-effective tool for our business.
• We can integrate with Jupyter with many conveniences.
• Its high-speed data processing has proved beneficial for us.

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• It enables us to develop event driven application.
• It increases our ability to handle streaming data.
• It reduces latency of communication.

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• There has been a near immeasurable return on customer data and improvement of our quality for our physical products due to be in tune with the customer. This has changed our way of doing things for the better to gain a better flow and overall workplace experience.
• A negative is that Tealium AudienceStream becomes harder to manuever and use data analytics for when a database has been existing for a fairly large amount of time. It goes from an agile ship to a huge vessel that takes many components to be able to move.

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