Google Cloud Dataflow and Amazon MSK compete in data processing and streaming solutions. Google Cloud Dataflow is noted for high scalability and ease of use, while Amazon MSK is recognized for robust real-time streaming features and reliability.
Features: Google Cloud Dataflow integrates smoothly with Google services, offers excellent scalability, and supports complex data processing. Amazon MSK provides real-time Kafka streaming, integrates easily with AWS services, and ensures strong security.
Room for Improvement: Google Cloud Dataflow could improve its real-time streaming capabilities and offer more granular control settings. Amazon MSK could enhance user-friendliness for non-AWS users and provide more straightforward deployment options. Both could benefit from additional third-party integrations.
Ease of Deployment and Customer Service: Google Cloud Dataflow is known for easy deployment within the Google Cloud ecosystem and responsive customer support. Amazon MSK offers detailed configuration options and a reliable support network, focusing on more controlled setups.
Pricing and ROI: Google Cloud Dataflow's transparent pricing allows flexible cost management, yielding strong ROI. Amazon MSK, while having higher initial costs, offers substantial ROI via efficient real-time data streaming. Dataflow's cost-effectiveness contrasts with MSK's investment in real-time streaming capabilities.
Amazon's support is excellent.
The fact that no interaction is needed shows their great support since I don't face issues.
Google's support team is good at resolving issues, especially with large data.
Whenever we have issues, we can consult with Google.
The functionality for scaling comes out of the box and is very effective.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The job we built has not failed once over six to seven months.
The automatic scaling feature helps maintain stability.
The increase in cloud costs by 50% to 60% does not justify the savings.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
Dealing with a huge volume of data causes failure due to array size.
Once we started using Kafka, our cloud costs rose by 50% to 60%.
It is part of a package received from Google, and they are not charging us too high.
The scalability and usability are quite remarkable.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
We then perform data cleansing, including deduplications, schema standardizations, and filtering of invalid records.
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.
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