The authentication part of the product is an area of concern where improvements are required. For some common users, the solution's authentication part is difficult to use. The scalability of the product is an area of concern where improvements are required. In the future, the product should be made available at a cheaper rate.
When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs.
Currently, not all error logs are available to users and this could make debugging failed jobs very difficult. The startup time of Dataflow jobs could also be reduced, and some features available in Java SDK can be included in the Python SDK.
Head of Data and Analytics at a tech services company with 201-500 employees
Real User
2022-06-28T15:57:58Z
Jun 28, 2022
Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job.
What is Streaming Analytics? Streaming analytics, also known as event stream processing (ESP), refers to the analyzing and processing of large volumes of data through the use of continuous queries. Traditionally, data is moved in batches. While batch processing may be an efficient method for handling huge pools of data, it is not suitable for time-sensitive, “in-motion” data that could otherwise be streamed, since that data can expire by the time it is processed. By using streaming...
The authentication part of the product is an area of concern where improvements are required. For some common users, the solution's authentication part is difficult to use. The scalability of the product is an area of concern where improvements are required. In the future, the product should be made available at a cheaper rate.
They should do a market survey and then make improvements.
Google Cloud Dataflow should include a little cost optimization.
There are certain challenges regarding the Google Cloud Composer which can be improved.
The solution's setup process could be more accessible.
I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool.
When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs.
The technical support has slight room for improvement.
Currently, not all error logs are available to users and this could make debugging failed jobs very difficult. The startup time of Dataflow jobs could also be reduced, and some features available in Java SDK can be included in the Python SDK.
Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job.