Apache Flink vs. GeoShield Real-Time vs. Tealium Customer Data Hub

In well-suited scenarios, I would recommend using Apache Flink when you need to perform real-time analytics on streaming data, such as monitoring user activities, analyzing IoT device data, or processing financial transactions in real-time. It is also a good choice in scenarios where fault tolerance and consistency are crucial. I would not recommend it for simple batch processing pipelines or for teams that aren't experienced, as it might be overkill, and the steep learning curve may not justify the investment.

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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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• Low latency Stream Processing, enabling real-time analytics
• Scalability, due its great parallel capabilities
• Stateful Processing, providing several built-in fault tolerance systems
• Flexibility, supporting both batch and stream processing

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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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• Python/SQL API, since both are relatively new, still misses a few features in comparison with the Java/Scala option
• Steep Learning Curve, it's documentation could be improved to something more user-friendly, and it could also discuss more theoretical concepts than just coding
• Community smaller than other frameworks

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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.

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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 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 is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and fault tolerance, Apache Flink takes the lead. However, Spark has a larger community and a decidedly lower learning curve.

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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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• Allowed for real-time data recovery, adding significant value to the busines
• Enabled us to create new internal tools that we couldn't find in the market, becoming a strategic asset for the business
• Enhanced the overall technical capability of the team

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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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