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 easy to use and has a quick start-up time due to being on the cloud."
"We have the ability to scale, collaborate and do machine learning."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"It's great technology."
"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."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."
"It's very simple to use Databricks Apache Spark."
"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 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."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"The solution allows us to program in any language we desire."
"The service is relatively cheap compared to other batch-processing engines."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"I would like it if Databricks made it easier to set up a project."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
"The product cannot be integrated with a popular coding IDE."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"The product should provide more advanced features in future releases."
"The integration of data could be a bit better."
"The solution's setup process could be more accessible."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The technical support has slight room for improvement."
"They should do a market survey and then make improvements."
"Google Cloud Dataflow should include a little cost optimization."
"The authentication part of the product is an area of concern where improvements are required."
"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."
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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