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.
"The solution is an impressive tool for data migration and integration."
"It is a cost-effective solution."
"I work in the data science field and I found Databricks to be very useful."
"Databricks has helped us have a good presence in data."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"Databricks helps crunch petabytes of data in a very short period of time."
"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"It is a scalable solution."
"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."
"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 support team is good and it's easy to use."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable."
"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."
"In the next release, I would like to see more optimization features."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"Google Cloud Dataflow should include a little cost optimization."
"The solution's setup process could be more accessible."
"They should do a market survey and then make improvements."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"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."
"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."
"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."
"The deployment time could also be reduced."
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 Data Science Studio, Microsoft Azure Machine Learning Studio 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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