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.
"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."
"Its lightweight and fast processing are valuable."
"The main features of the solution are efficiency."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily."
"I haven't heard about any major stability issues. At this time I feel like it's stable."
"It is a cost-effective solution."
"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."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"Dremio allows querying the files I have on my block storage or object storage."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
"Doesn't provide a lot of credits or trial options."
"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."
"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 to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."
"Anyone who doesn't know SQL may find the product difficult to work with."
"It shows errors sometimes."
"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."
"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."
"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."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
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 Data Science Studio, 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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