Google Cloud Dataflow vs. jKool

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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JKool is a decent tool for retrieving information regarding user engagement and customer feedback for your software products. With these information it's very valuable stepping stone for us to make future updates or produce new products. However, there are some downsides with jKool, two of which are there are too much clutter in the GUI and it takes sometimes to turn how things work.

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

• 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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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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Google Cloud Dataproc Cloud Datafusion

No other product that I used is similar to jKool, it's unique product that have good amount of features that I would be opened to invest more in the future if time permit. It still has a long way to go and the first thing it can do is clean up it user interface to make it better.

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Independently Invited by TrustRadius

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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