Google Cloud Dataflow vs. WebEngage

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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WebEngage can easily reduce turnaround time for product and marketing teams to execute marketing campaigns for specific user segments as well as remove any dependency on internal tech teams for the development and maintenance of automation journeys. Further, A/B tests can be executed to measure the performance of message variants in real-time directly from the panel. But WebEngage lacks a robust Analytics platform. While basic metrics such as open rate and click-through rate for messages being sent can be considered for reporting, attribution is still not the strongest suit in case the marketing team relies on multiple Ad networks for acquisition. Thus, as per experience, it's better to use the automation suite offered by WebEngage with a different data analytics platform.

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

• Ease in creating user journeys.
• Easy implementation of APIs.
• Good reporting dashboard and scheduling features.
• Segmentation of the users is easy.

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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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• Relays
• Onsite should have more options of templates.
• Backend support for better reachability of the channels.
• Cohort studies should be more customizable.

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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

WebEngage's dashboard is relatively more intuitive and easy to understand. Further in terms of cost as well it is value to money as it delivers excellent ROI. The after-sales support is also better in terms of responsiveness and helps in case of issues with the execution of campaigns. The account managers also ideate and help in the optimization and creation of new user journeys.

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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

• Helped in lowering cost of marketing.
• Driving higher volume of Direct Sales.
• Increasing conversion rate on the website.
• Increasing lead to sale ratio.

Incentivized

Independently Invited by TrustRadius

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