Google Cloud Dataflow vs. Microsoft Azure IoT Central

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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Microsoft Azure IoT Central has native integrations with Visual Studio (like pretty much all Microsoft products) making it pretty simple to publish apps to Azure App Service, where you can host all sorts of different applications. It does a really good job of storing large amounts of data (for instance, collected from IoT devices with various sensors) and it gets stored to multiple DBs. Azure IoT Central could improve by adding more customizations, as this would make troubleshooting the products easier and make future costs smaller and easier to predict.

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

• Documentation
• Support
• Good and approachable GUI
• Many types of subscription configurations

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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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• central application
• smartphone app

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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

Before deciding to go with Microsoft Azure IoT Central, we considered Google, Datadog, and AWS alternatives. The reason we went with Azure was that the client was heavily invested in Microsoft technologies and Azure plays really well with other Microsoft products. The competitive products might be a little bit easier to set up, with more flexible pricing, but, the integrations with other Microsoft services were the deciding factor for our use case.

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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

• positive: connected huge number of machines as IoT devices and passed them through entire IoT pipeline until storage in a fairly short time
• negative: we spent some time figuring out some "less-used" services because they aren't documented well

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