We compared Databricks and Google Cloud Dataflow based on our user's reviews in several parameters.
Databricks excels in collaborative features, customer service, and pricing, with a focus on data insights. Google Cloud Dataflow stands out for scalability, real-time processing, ease of use, and ROI, with a focus on data transformation. Areas for improvement in Databricks include data visualization and pricing flexibility, while Google Cloud Dataflow could enhance integration, documentation, and error handling.
Features: Databricks stands out with its seamless integration with various platforms, collaborative capabilities, and advanced analytics. On the other hand, Google Cloud Dataflow offers scalability, easy setup, real-time processing, data transformation, and seamless integration with other Google Cloud services.
Pricing and ROI: The setup cost for Databricks product is reported to be straightforward and hassle-free, while Google Cloud Dataflow offers a relatively low setup cost. This makes it easy and affordable for users to get started with the service., Databricks users report increased efficiency, productivity, and data analysis capabilities. Google Cloud Dataflow users mention improved scalability, reduced costs, and flexibility provided by the platform.
Room for Improvement: Databricks has room for improvement in data visualization, monitoring, external integration, documentation, and flexible pricing. Google Cloud Dataflow needs better integration, documentation, error handling, pipeline customization, and improved performance for large-scale data processing.
Deployment and customer support: The user feedback indicates that the duration required for establishing a new tech solution varies for both Databricks and Google Cloud Dataflow. Some users mention spending three months on deployment and an additional week on setup for both products, while others report a week for both stages., Customers have praised the customer service and support offered by both Databricks and Google Cloud Dataflow. However, Databricks is highlighted for its efficient and effective support team, while Google Cloud Dataflow is commended for its availability of extensive resources for self-guidance.
The summary above is based on 56 interviews we conducted recently with Databricks and Google Cloud Dataflow users. To access the review's full transcripts, download our report.
"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."
"It's easy to increase performance as required."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"It can send out large data amounts."
"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"It is a scalable solution."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"The support team is good and it's easy to use."
"The service is relatively cheap compared to other batch-processing engines."
"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"Can be improved by including drag-and-drop features."
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
"The integration and query capabilities can be improved."
"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."
"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."
"I would like it if Databricks made it easier to set up a project."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"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."
"They should do a market survey and then make improvements."
"The technical support has slight room for improvement."
"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."
"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."
"The solution's setup process could be more accessible."
"Google Cloud Dataflow should include a little cost optimization."
Databricks is ranked 2nd in Streaming Analytics with 78 reviews while Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews. Databricks is rated 8.2, while Google Cloud Dataflow is rated 7.8. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Power BI, whereas Google Cloud Dataflow is most compared with Apache NiFi, Amazon MSK, Amazon Kinesis, Spring Cloud Data Flow and Apache Flink. See our Databricks vs. Google Cloud Dataflow report.
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