Google Cloud Dataflow vs. Oracle Warehouse Builder

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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The best place for Oracle Warehouse Builder is at the business IT level. It's not suited for business-level users. They are easy confused. One way to reduce the confusion for the developers is to set up the workspaces based on the requirements that are discovered in design sessions. Once this is complete, the implementation of Oracle Warehouse Builder can take flight and be successful.

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

• Easy transformation.
• Easy implementation from Oracle to Oracle systems.
• Ease of usage and easy to learn.
• Starting component of metadata management.

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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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• What I noticed is that sometimes OWB doesn't generate the best SQL in the package especially when there are a high number of source tables in the ETL. It would be nice if ETL developers were allowed to update the generated packages in the database directly.
• Another thing - moving OWB ETLs from one database to another one could be easier - for example it would be nice to just copy the generated packages from one database to the other one without doing the deployment of these ETLs through OWB.

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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

Ab>initio, IBM Datastage 8.0

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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 improved understanding of ETL functions. Data is consistent. The speed is pretty good.

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