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.
"Automation with Databricks is very easy when using the API."
"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."
"Databricks is a scalable solution. It is the largest advantage of the solution."
"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"The most valuable feature is the ability to use SQL directly with Databricks."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"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."
"The solution allows us to program in any language we desire."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"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 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 most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The support team is good and it's 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."
"CI/CD needs additional leverage and support."
"Costs can quickly add up if you don't plan for it."
"The initial setup is difficult."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"It would be great if Databricks could integrate all the cloud platforms."
"In the future, I would like to see Data Lake support. That is something that I'm looking forward to."
"They should do a market survey and then make improvements."
"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."
"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."
"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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The solution's setup process could be more accessible."
"The authentication part of the product is an area of concern where improvements are required."
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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