Google Cloud Dataflow vs. Talend Data Fabric

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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Truly trusted contact center where the effective solution is always guaranteed. It is not a one-off fix to a specific data integration or management problem. It is a permanent and scalable solution to manage all of your data under a unified environment. Easy to use, great performance, used it for our internal data warehouse. Easy to build and connect to our data sources such as Salesforce, Netsuite, and Marketo.

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

• It supports a wide variety of connectors (Systems/endpoints)
• It provides great flexibility for developers as it not only has a lot of predefined ready to use the function but also provides the ability to use complex java code within the platform. Great tool if you have good developers available.

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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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• The print and export functionality are missing in the Community version which frustrates a lot of our clients as well as internal users.
• The learning curve is very steep for all Talend products including DQ.
• Lack of easy to create workflows for jobs that can be repeated.

Lack of report export and publication! Steep learning curve!

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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At this moment the usability of Talend Data Quality is optimal, too bad I cannot say the same in the first three months, it was always a problem due to its steep learning curve, but what matters is being able to use it effectively at this precise moment.

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Talend Data Quality gave us direct help in the learning process and prevented us from taking many more months to adapt and I appreciate this from the heart, I think that thanks to the support we can have very detailed reports that help increase the use of Talend Data Quality in the company.

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Google Cloud Dataproc Cloud Datafusion

The engine with which it works to process a lot of information is striking, the comparison also being the connectors it has for different RDBMS, which other tools do not count as they are GNU licenses or community editions. The friendly and intuitive environment is what catches the eye. that's why I choose Talend over any other tool

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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

• Definitely simplified the development efforts on our end in doing the data integration, saving lots of human hours.
• Small company, so cost could be a problem for these services, but works so far.

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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