Google Cloud Dataflow vs. IBM Streams (discontinued) vs. Tealium Customer Data Hub

It is best in cases where you have batch as well as streaming data. Also in some cases where you have batch data right now and in future you will get streaming data. In those cases Dataflow is very good. Also in cases where most of your infra is on GCP. It might not be good when you already are on AWS or Azure. And also you want in-depth control over security and management. Then you can directly use Apache beam over Dataflow.

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Like the name says, it is good for streaming data and analyzing. It is great to look at tuples at a fast rate, filtering, calling other sources to enrich data, can call APIs, etc. Could do better for ingest use cases, can do better with guaranteed delivery, etc.

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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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• Streaming, Real time work load
• Batch processing
• Auto scaling
• flexible pricing

• IBM Streams is well suited for providing wire-speed real-time end-to-end processing with sub-millisecond latency.
• Streams is amazingly computationally efficient. In other words, you can typically do much more processing with a given amount of hardware than other technologies. In a recent linear-road benchmark Streams based application was able to provide greater capability than the Hadoop-based implementation using 10x less hardware. So even when latency isn't critical, using Streams might still make sense for reducing operational cost.
• Streams comes out of the box with a large and comprehensive set of tested and optimized toolkits. Leveraging these toolkits not only reduces the development time and cost but also helps reduce project risk by eliminating the need for custom code which likely has not seen as much time in test or production.
• In addition to the out of the box toolkits, there is an active developer community contributing additional specialized packages.

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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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• More templates for Bigquery and App Engine. There is only limited options for templates so the things we use can limit.
• I would like native connectors for Excel (XLSX) to reduce the need for custom wrappers in financial pipelines.
• Debugging Google Cloud Dataflow using only logs in Cloud Logging can be overwhelming sometimes, and it’s not always obvious which specific element in the flow caused a failure. IT uses a lot of time.

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• Documentation could be more extensive, with more examples, although overall this is not too bad compared to some of the alternative solutions.
• Seems expensive to use in production.

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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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It really saved a lot of time and it's flexibility really can give you infra which is future-proof for most of the use cases may it be streaming or batch data. And with this you can avoid use of resource-heavy big data offerings.

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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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Google Cloud Dataproc Cloud Datafusion

There are well explained tutorials to get the user started. If you are looking for business application ideas, the user community offers a diversity of applications. It is very easy to launch applications on the cloud and can integrate with other analytic tools available on Watson Studio. It takes away the burden of the technology so that users can focus on business innovations.

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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 saving from managing our own data center for ETL servers
• consumption based pricing
• with auto scaling feature, we were able to expand components to support work load

• Ability to do more with less
• Admins and data analyst can now focus on more thinking tasks
• No negative impacts yet

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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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