IBM Astra Streaming vs. Google Cloud Dataflow

DataStax Enterprise is a state-of-the-art data management platform that manages our own data across web, mobile, and IoT applications. It is a hybrid cloud-based solution that enables us to meet the availability and performance requirements of web, IoT and mobile platforms.

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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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• Easy to operate and save man hours
• Easy installation and configuration
• Mature and scalable data warehouse, well supported

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

• Their solution is expensive compared to other resolutions, however you get what you pay for.

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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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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

• In my personal opinion, I can mention that this product has enormous positive points because it has worked really well for us in our organization in a very positive way, for which we ourselves feel quite comfortable with this product.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• 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