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Databricks Logo
30,172 views|19,226 comparisons
Dremio Logo
2,693 views|2,048 comparisons
Comparison Buyer's Guide
Executive Summary
Updated on Mar 6, 2024

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.

To learn more, read our detailed Databricks vs. Dremio Report (Updated: March 2024).
765,234 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The most valuable feature of Databricks is the notebook, data factory, and ease of use.""Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours.""The solution is very easy to use.""The most valuable feature of Databricks is the integration with Microsoft Azure.""We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time.""The load distribution capabilities are good, and you can perform data processing tasks very quickly.""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 covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."

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"Everyone uses Dremio in my company; some use it only for the analytics function.""We primarily use Dremio to create a data framework and a data queue.""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 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.""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."

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Cons
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice.""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.""The integration features could be more interesting, more involved.""It would be very helpful if Databricks could integrate with platforms in addition to Azure.""Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage.""The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau.""Overall it's a good product, however, it doesn't do well against any individual best-of-breed products.""The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."

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"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.""We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily.""I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported.""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.""It shows errors sometimes.""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."

More Dremio Cons →

Pricing and Cost Advice
  • "Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."
  • "I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."
  • "Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
  • "We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."
  • "The pricing depends on the usage itself."
  • "I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."
  • "The price is okay. It's competitive."
  • "Databricks uses a price-per-use model, where you can use as much compute as you need."
  • More Databricks Pricing and Cost Advice →

  • "Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
  • "Dremio is less costly competitively to Snowflake or any other tool."
  • More Dremio Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with… more »
    Top Answer:We researched AWS SageMaker, but in the end, we chose Databricks Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It… more »
    Top Answer:Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their… more »
    Top Answer:Dremio allows querying the files I have on my block storage or object storage.
    Top Answer:Every tool has a value based on its visualization, and the pricing is worth its value.
    Top Answer:Dremio's interface is good, but it has a few limitations. I cannot do a lot of things with ANSI SQL or basic SQL. I cannot use the recursive common table expression (CTE) in Dremio because the support… more »
    Ranking
    1st
    Views
    30,172
    Comparisons
    19,226
    Reviews
    47
    Average Words per Review
    446
    Rating
    8.2
    8th
    Views
    2,693
    Comparisons
    2,048
    Reviews
    6
    Average Words per Review
    530
    Rating
    8.7
    Comparisons
    Also Known As
    Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
    Learn More
    Dremio
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    Overview

    Databricks is an industry-leading data analytics platform which is a one-stop product for all data requirements. Databricks is made by the creators of Apache Spark, Delta Lake, ML Flow, and Koalas. It builds on these technologies to deliver a true lakehouse data architecture, making it a robust platform that is reliable, scalable, and fast. Databricks speeds up innovations by synthesizing storage, engineering, business operations, security, and data science.

    Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This enables users to easily manage a colossal amount of data and to continuously train and deploy machine learning models for AI applications. The platform handles all analytic deployments, ranging from ETL to models training and deployment.

    Databricks deciphers the complexities of processing data to empower data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads. The program takes advantage of AI’s cost-effectivity, flexibility, and cloud storage.

    Databricks Key Features

    Some of Databricks key features include:

    • Cloud-native: Works well on any prominent cloud provider.
    • Data storage: Stores a broad range of data, including structured, unstructured, and streaming.
    • Self-governance: Built-in governance and security controls.
    • Flexibility: Flexible for small-scale jobs as well as running large-scale jobs like Big Data processing because it’s built from Spark and is specifically optimized for Cloud environments.
    • Data science tools: Production-ready data tooling, from engineering to BI, AI, and ML.
    • Familiar languages: While Databricks is Spark-based, it allows commonly used programming languages like R, SQL, Scala, and Python to be used.
    • Team sharing workspaces: Creates an environment that provides interactive workspaces for collaboration, which allow multiple members to collaborate for data model creation, machine learning, and data extraction.
    • Data source: Performs limitless Big Data analytics by connecting to Cloud providers AWS, Azure, and Google, as well as on-premises SQL servers, JSON and CSV.

    Reviews from Real Users

    Databricks stands out from its competitors for several reasons. Two striking features are its collaborative ability and its ability to streamline multiple programming languages.

    PeerSpot users take note of the advantages of these features. A Chief Research Officer in consumer goods writes, “We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.”

    A business intelligence coordinator in construction notes, “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.”

    An Associate Manager who works in consultancy mentions, “The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view. The ability to migrate from one environment to another is useful.”

    Dremio is a data lake query engine tool that creates PDSs and VDSs on top of S3 buckets. It is used for managing simple ad-hoc queries and as a greater layer for ad-hoc queries. The most valuable features of Dremio include its ability to sit on top of any data storage, generate refresh reflections and create visuals, manage changes effectively through data lineage and data providence capabilities, use open-source, and address the problem of data transfer when working with large datasets. The use cases are broad, allowing for high-performance queries from a data lake.

    Sample Customers
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    UBS, TransUnion, Quantium, Daimler, OVH
    Top Industries
    REVIEWERS
    Computer Software Company25%
    Financial Services Firm16%
    Retailer9%
    Manufacturing Company9%
    VISITORS READING REVIEWS
    Financial Services Firm15%
    Computer Software Company12%
    Manufacturing Company8%
    Healthcare Company6%
    VISITORS READING REVIEWS
    Financial Services Firm30%
    Computer Software Company11%
    Manufacturing Company8%
    Retailer5%
    Company Size
    REVIEWERS
    Small Business27%
    Midsize Enterprise14%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise11%
    Large Enterprise74%
    Buyer's Guide
    Databricks vs. Dremio
    March 2024
    Find out what your peers are saying about Databricks vs. Dremio and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    Databricks is ranked 1st in Data Science Platforms with 77 reviews while Dremio is ranked 8th 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 "Quick database capabilities but sometimes shows minor errors". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Tableau, whereas Dremio is most compared with Snowflake, Starburst Enterprise, Amazon Redshift, Microsoft Azure Synapse Analytics and BigQuery. See our Databricks vs. Dremio report.

    See our list of best Data Science Platforms vendors.

    We monitor all Data Science Platforms 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.