Google Cloud Dataflow vs. Sybase Event Stream Processor (discontinued)

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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These complex event processing platforms have the ability to process to multiple streams of data with low latency and ability to scale up well. It provides the ability to analyze streaming data real time and establish/investigate patterns with this streaming data and also deals with high-speed data very well.

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

• Ability to capture data and investigate data for pattern fairly quickly
• Integrates well with Sybase database
• Ability to do data modeling

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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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• Configuring data streams could be made more intuitive to users
• Sometimes debugger tools are very slow

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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

IBM WebSphere business elements which was more complex to set up and also more expensive especially in a large SAP environment, We were also looking to get ramped up with the product with minimal learning curve especially with good documentation available to get started with the product as well as the ability to deploy and maintain with relative ease on an ongoing basis.

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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

• A positive impact on real-time analysis and patterns from streaming data
• Good speed
• Good documentation for setup
• No negative impact

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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