Google Cloud Dataflow vs. Heap

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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Scenarios when Heap was well suited: It is when a user claims that he encountered a bug without giving us the details of the error message. Scenarios where it is less appropriate: Its when we try to capture user interaction in our mobile app

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

• Realtime interactions which are captured automatically is a unique feature which no other product offers.
• Low code implementation is a big plus for marketing teams, as there is less dependency on engineering
• Detailed customer behaviours can be studied with captured replay sessions
• Due to all the above points - it helped to improve customer experience

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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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• I think Heap can improve its onboarding; I would love to see some info that is helpful for someone who isn't in Heap every single day.
• Sharing dashboards can be tricky, it feels like a huge learning curve that I already have forgotten how to do.

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It's a great platform. I'm glad that one of our product managers introduced it because it has allowed us to create all kinds of new functionality. We're not only able to create a better product experience from our communications because of Heap, but we're also able to generate all kinds of helpful analysis.

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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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On a scale from 1-10, I find Heap to be incredibly user-friendly and easy to use. I enjoyed the training videos available and was quickly able to pick up how to create events and reports to track user interactions on our product. I would recommend Heap for its usability first and foremost.

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I've never run into any issues with Heap's availability, Heap is always there when I need it. I haven't run into any issues like application errors or unplanned outages during my 2+ years of using Heap. Each and every time I log in to Heap I have a completely functional experience

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Heap doesn't affect page load times considerably nor has a large impact [on] our overall score, as far as page loading times inside of the tool its pretty reliable to retrieve data as much as "instant" that it can be the delay seems to be on data getting tracked into the servers to be read but it's not significant.

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Heap support has allowed us to troubleshoot and test a lot of different items. Their support team is always helpful and friendly, even when we come to them with the most complicated questions. I think this greatly improves the value proposition of the product because their support team is knowledgable and friendly.

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The implementation was smooth and easy. The Heap team helped us with implementation and it went great! Within a few weeks, we were fully up and running and utilizing the platform to its full capability. This is an additional thing that has made this platform so great and we couldn't recommend it enough.

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Google Cloud Dataproc Cloud Datafusion

Heap offers a ton of functionality on a single platform.It also has an smart data science layer to offers suggestions for next steps in the analysis, allowing us to explore alternative paths we may not think to take. The low-code option for updating data is appealing, and there is a lot of automation with minimal engineering effort.

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The most challenging part of using Heap in a growing organization is the naming and structure in which reports and dashboards are organized. I work within the marketing department and our Heap leader internally works within the IT/Product department, which makes it challenging because we often don't speak the same language, so the learning curve has been steep without any specific use-case examples to leverage online.

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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

• It has helped us make decisions on what products to scope for our MVP based on the usage data.
• It's allowed us to measure success with our beta product.
• It's helped us view our conversion rates in an easy and usable manner.

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