Google Cloud Dataflow vs. IBM Decision Manager Open Edition

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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Red Hat Decision Manager is well suited for developing a microservices-based application architecture where reusable components / APIs can be used across different end-user applications. It however may not be well suited for use cases where business users need low code / no-code environment to maintain business rules themselves without any developer / IT team support

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

• Business rule management for filtering the candidate
• Automate the possible decision making which reduces the time and increase the productivity
• Whenever business rule changes based on the client requirement.

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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 should be easy to use because it needs more knowledge
• You should be trained to your partner well
• Well Documentations are needed

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Red Hat Decision Manager can be scaled and adapted to meet the automation needs of the organization as it grows by reusing the components and business rules data which provides a high ROI on the renewal costs.

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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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Implementation phase is very difficult .Of course ,you need to know bunch of things about cloud,container,automatization

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The support for Red Hat decision Manager can be improved to be more omnichannel with better response times for the customers.

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For high volume and / or complex set of legacy business rules, plan for adequate time for data migration activities.

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Google Cloud Dataproc Cloud Datafusion

If you have a low budget to invest probably JBoss BRMS is the option for you, if your budget is not restricted you'll probably like to consider Bizagi

• 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

• Quickly write business rules
• Computers can take decision
• Complex event processing

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