We compared Databricks and Dremio based on our user's reviews in several parameters.
Databricks excels in seamless integration, advanced analytics, and collaborative capabilities, with positive feedback on customer service and pricing. In contrast, Dremio is praised for query performance, data virtualization, and scalability, with excellent customer service and cost-effective pricing. Areas for improvement in Databricks include data visualization and pricing flexibility, while Dremio users note issues with performance on complex queries, documentation, and support response times.
Features: Databricks excels in seamless integration, collaborative capabilities, and advanced analytics. In contrast, Dremio stands out for its impressive query performance, data virtualization, user-friendly interface, strong security features, and scalability for large datasets.
Pricing and ROI: Databricks and Dremio have received positive user feedback regarding pricing, setup cost, and licensing. Users found both products to have reasonable and competitive pricing. The setup cost for Databricks was reported to be straightforward, while Dremio's setup process was easy and without significant costs. Both products offer flexible licensing options to meet different user needs. Overall, users had a positive experience with pricing, setup cost, and licensing of both Databricks and Dremio., Users have reported positive outcomes and returns on investment when utilizing both Databricks and Dremio. However, Databricks is praised for its significant impact on increasing efficiency, productivity, and data analysis capabilities, while Dremio is favored for providing favorable returns on investment.
Room for Improvement: Databricks could improve its data visualization capabilities, monitoring and debugging tools, integration with external sources, documentation for beginners, and pricing flexibility. Dremio needs to enhance its user interface, performance with complex queries, documentation, embedding into other applications, and support availability.
Deployment and customer support: In terms of the duration required to establish a new tech solution, user reviews for Databricks and Dremio differ. Databricks reviews mention varying durations for deployment and setup, while Dremio reviews indicate different timeframes for these processes, emphasizing the importance of context., Databricks' customer service is praised for its efficiency, helpfulness, and promptness. The support team is proactive and maintains excellent communication. Dremio's customer service is highly praised for its promptness, efficiency, and resourcefulness. Users appreciate their top-notch and reliable support.
The summary above is based on 53 interviews we conducted recently with Databricks and Dremio users. To access the review's full transcripts, download our report.
"I work in the data science field and I found Databricks to be very useful."
"It is a cost-effective solution."
"There are good features for turning off clusters."
"Databricks helps crunch petabytes of data in a very short period of time."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"Databricks' most valuable feature is the data transformation through PySpark."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"The solution offers a free community version."
"We primarily use Dremio to create a data framework and a data queue."
"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."
"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"Dremio allows querying the files I have on my block storage or object storage."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."
"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"It's not easy to use, and they need a better UI."
"Databricks has a lack of debuggers, and it would be good to see more components."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."
"It shows errors sometimes."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."
"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Dremio is ranked 10th in Data Science Platforms with 6 reviews. Databricks is rated 8.2, while Dremio is rated 8.6. 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 Dremio writes "It enables you to manage changes more effectively than any other platform". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio and Azure Stream Analytics, whereas Dremio is most compared with Snowflake, Starburst Enterprise, Amazon Redshift, Microsoft Azure Synapse Analytics and Microsoft Power BI. See our Databricks vs. Dremio report.
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