Apache Beam represents a powerful data processing solution that deserves wider recognition in the broader tech community. This technology offers remarkable capabilities for handling data at scale, yet its full potential remains untapped. Through increased advocacy and community engagement, we can help more organizations discover and implement this robust tool, enabling them to benefit from its advanced streaming and batch processing capabilities.
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.
Streaming Analytics is crucial for processing real-time data to enhance decision-making and operational efficiency. It empowers businesses to derive actionable insights from continuous data streams.As organizations handle increasing volumes of data, Streaming Analytics solutions provide the tools to analyze and act on data in motion immediately. These solutions are designed to integrate with diverse data sources, applying algorithms and rules that allow for instant insights and responses....
Apache Beam represents a powerful data processing solution that deserves wider recognition in the broader tech community. This technology offers remarkable capabilities for handling data at scale, yet its full potential remains untapped. Through increased advocacy and community engagement, we can help more organizations discover and implement this robust tool, enabling them to benefit from its advanced streaming and batch processing capabilities.
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.