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.
"It's easy to increase performance as required."
"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."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"Automation with Databricks is very easy when using the API."
"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."
"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."
"The ease of use and its accessibility are valuable."
"The initial setup phase of Databricks was good."
"It is a scalable solution."
"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."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The service is relatively cheap compared to other batch-processing engines."
"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 product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
"The connectivity with various BI tools could be improved, specifically the performance and real time integration."
"There should be better integration with other platforms."
"The initial setup is difficult."
"Costs can quickly add up if you don't plan for it."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"The product cannot be integrated with a popular coding IDE."
"There is room for improvement in visualization."
"The technical support has slight room for improvement."
"The deployment time could also be reduced."
"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."
"Google Cloud Dataflow should include a little cost optimization."
"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 authentication part of the product is an area of concern where improvements are required."
"They should do a market survey and then make improvements."
Databricks is ranked 1st 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.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.