Google Cloud Dataflow vs. IBM InfoSphere Information Server

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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Information Server is extremely useful to replace manual developments that require a lot of coding effort. It significantly increases the productivity of the initial development and the future maintenance of the processes since it has a visual development environment with self-documentation.

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

• IIS best for ETL ,not ELT , and many and diffrent source systems.
• It also can process big data , unstuctured data
• It is not only DWH , you can use infosphere for analys and see the bigger architecture of your OLTP systems

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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 would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

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• Scale of implementation
• IBM techsupport

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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

DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

• 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

• Productivity of the development of integration processes.
• Better documentation and governance.
• Reduce training costs of various technologies.