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Databricks Reviews

Vendor: Databricks
4.1 out of 5
Badge Ranked 1
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What is Databricks?

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

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Databricks is the #1 ranked solution in top Data Science Platforms, #1 ranked solution in Streaming Analytics tools, and #8 ranked solution in top Cloud Data Warehouse solutions. PeerSpot users give Databricks an average rating of 8.2 out of 10. Databricks is most commonly compared to Teradata: Databricks vs Teradata. Databricks is popular among the large enterprise segment, accounting for 70% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 17% of all views.
Buyer's Guide
Databricks
August 2025
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Databricks mindshare

Product category:
Cloud Data Warehouse
As of August 2025, the mindshare of Databricks in the Cloud Data Warehouse category stands at 8.5%, up from 5.1% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Cloud Data Warehouse Market Share Distribution
ProductMarket Share (%)
Databricks8.5%
Snowflake17.8%
Dremio10.1%
Other63.6%
Cloud Data Warehouse

PeerResearch reports based on Databricks reviews

TypeTitleDate
CategoryCloud Data WarehouseAug 29, 2025Download
ProductReviews, tips, and advice from real usersAug 29, 2025Download
ComparisonDatabricks vs SnowflakeAug 29, 2025Download
ComparisonDatabricks vs Azure Data FactoryAug 29, 2025Download
ComparisonDatabricks vs BigQueryAug 29, 2025Download
Suggested products
TitleRatingMindshareRecommending
Teradata4.18.7%87%76 interviewsAdd to research
KNIME Business Hub4.1N/A94%60 interviewsAdd to research
 
 
Key learnings from peers

Valuable Features

Databricks excels in scalability, ease of use, and integration. Users appreciate the Delta Lake for data processing and governance, as well as the Notebook for collaboration and multi-language support. The ability to switch workloads across clusters, built-in optimization, and fast processing enhances efficiency. Its seamless API automation, cloud scalability, and interactive clusters attract many for big data, machine learning, and analytics. The platform's versatility, intuitive interface, and robust support further its appeal.
  • "Databricks is definitely a very stable product and reliable."
  • "I think Databricks is very good at facilitating AI and machine learning projects; they implement AI and machine learning models very well, and clients can run their models on Databricks."
  • "Databricks has a Unified Catalog that assists with secured access and governance."

Room for Improvement

Databricks can enhance its visualization capabilities and improve integration with tools like Jupyter Notebook, Power BI, and Tableau. Pricing concerns are notable, and better error messages could aid debugging. More compatibility with libraries, better data governance, and clearer documentation are needed. Expanding support for various cloud platforms and integration features, optimizing user interfaces, and offering more advanced data science tools would benefit users. Enhancements in machine learning and predictive analytics capabilities are also desired.
  • "My experience with the pricing and licensing model is that it remains relatively expensive. Though it's less expensive than AWS, we still need a more cost-effective solution."
  • "In my opinion, areas of Databricks that have room for improvement involve the dashboards. Until recently, everyone used third-party systems such as Power BI to connect to Databricks for dashboards and reports, but they're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further."
  • "The initial setup of Databricks could be complex."

ROI

Multiple users report financial benefits from Databricks with annual savings over $75k for some deployments, transitioning from expensive in-house systems to efficient cloud solutions. Databricks is praised for its ease of use and ability to process large datasets efficiently. However, some still assess its financial gains due to recent adoption. Smaller entities appreciate its cost-effectiveness, though some remain unsure of direct returns due to integration with other platforms.

Pricing

Databricks pricing varies widely depending on usage, data size, and cloud provider choice. Many enterprise users find it expensive, especially for large data volumes or high compute needs. The platform employs a pay-per-use model, with charges based on computing time and storage. Licensing is often tiered and flexible, but setup costs can escalate, especially compared to open-source alternatives. While some find it competitively priced, others view it as high, yet justified by its capabilities.
  • "It is an expensive tool. The licensing model is a pay-as-you-go one."
  • "The product pricing is moderate."
  • "Databricks are not costly when compared with other solutions' prices."

Popular Use Cases

Organizations primarily use Databricks for data analytics, machine learning, and integration in cloud environments like Azure and AWS. They leverage its clustering for big data processing, ETL tasks, streaming analytics, and building data pipelines. Others employ it for developing machine learning models, handling data ingestion, and transforming data. Teams often utilize it for DevOps, deploying in sandbox environments, and integrating with various data sources to achieve scalability and efficient data management.

Service and Support

Many customers did not require Databricks support, citing comprehensive documentation or reliance on Microsoft support. Those who engaged with Databricks described the support as generally good, responsive, and timely; some experienced challenges like communication issues. Support was rated between 3 to 10 out of 10. Feedback highlighted helpfulness and knowledgeability, especially for customers with direct agreements or partnerships. Some reported initial technical difficulties being resolved effectively.

Deployment

Users found Databricks's initial setup generally straightforward, thanks to comprehensive documentation and cloud-based deployment. While deployment varied from quick completion to taking months, it often depended on team readiness and complexity. Training and prior Spark experience eased the process. Some faced challenges with new architecture or configuring databases, but overall, it wasn't described as difficult. Users frequently mentioned Azure and AWS integration, and technical proficiency helped streamline the setup.

Scalability

Databricks is highly scalable, supporting large datasets and swift cluster creation. Users find it easy to expand usage, leveraging cloud capabilities. Scalability depends on project needs, with some noting the cost impact. Users appreciate its efficiency in managing diverse workloads across various cloud platforms. Organizations value its real-time optimization recommendations and ease of vertical and horizontal expansion. However, some face challenges with node configuration and costs when scaling significantly. Overall, Databricks receives high marks for scalability.

Stability

Databricks exhibits strong stability, appreciated by many for its consistent performance. While some users report minor issues like memory overruns or bugs during version updates, others find it highly reliable with minimal downtime. Its robustness is often linked to its ability to handle large data volumes effectively. Ratings frequently range from eight to nine out of ten, indicating satisfaction. Common concerns include occasional cluster failures, which are often addressed with prompt fixes.
These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our Databricks Buyer's Guide for additional reliable information.

Review data by company size

By reviewers
Company SizeCount
Small Business24
Midsize Enterprise12
Large Enterprise50
By reviewers
By visitors reading reviews
Company SizeCount
Small Business861
Midsize Enterprise630
Large Enterprise3499
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
Insurance Company
6%
Retailer
5%
Government
5%
University
4%
Energy/Utilities Company
4%
Educational Organization
4%
Comms Service Provider
3%
Media Company
3%
Real Estate/Law Firm
2%
Construction Company
2%
Hospitality Company
2%
Transportation Company
2%
Outsourcing Company
2%
Non Profit
2%
Performing Arts
2%
Pharma/Biotech Company
2%
Wholesaler/Distributor
2%
Recreational Facilities/Services Company
1%
Consumer Goods Company
1%
Legal Firm
1%
Logistics Company
1%
Marketing Services Firm
1%

Compare Databricks with alternative products

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Learn more about Databricks

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

  • User-friendly interface: Simplifies operations and usability.
  • Built-in machine learning libraries: Facilitates machine learning tasks.
  • Support for multiple programming languages: Enhances flexibility.
  • Scalability: Efficiently handles growing data needs.
  • Fast processing: Improves performance and speed.
  • Automated optimization: Reduces manual efforts.
  • Data visualization: Provides insightful visuals.
  • Collaborative features: Enhances teamwork.
  • Delta Lake performance: Boosts data management.
  • Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

  • Efficiency in handling large datasets: Ensures smooth processing.
  • Interactive workspace environment: Improves user collaboration.
  • Integration with platforms: Provides connectivity benefits.
  • Performance optimization: Enhances overall system performance.
  • Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Databricks was previously known as Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash.

Databricks customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

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Which ETL or Data Integration tool goes the best with Amazon Redshift?
  • 131
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What are the key reasons for choosing Snowflake as a data lake over other data lake solutions?
  • 23
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What cloud data warehouse solution do you recommend?

Product Categories

Product Categories
Cloud Data Warehouse
Data Science Platforms
Streaming Analytics

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Databricks Reviews Summary
Author infoRatingReview Summary
Data Engineer at a engineering company with 1,001-5,000 employees4.0As a data engineer at Fractal, I frequently use Databricks within Azure for efficient ED and Synapse integration. Databricks' extensive course offerings and visualization tool compatibility are valuable, though cluster failures remain a significant challenge despite efforts to resolve them.
Data Platform Architect at KELLANOVA3.5I am currently working as an IT architect, using Databricks on our AWS analytics platform to enhance our AI/ML projects. It offers valuable features like a Unified Catalog and serverless computing, but we have yet to see a definitive ROI.
Senior Data Engineer at a logistics company with 51-200 employees3.5I handle data ingestion and create warehouses with Databricks, utilizing it for analytics support. The all-in-one approach is beneficial, but maintaining infrastructure and managing costs is challenging for our startup. Using Microsoft Azure enhances our operations.
Head CEO at bizmetric4.5I use Databricks for data engineering and machine learning, appreciating its Unity Catalog and MLflow features. However, improvements in the Databricks File System are needed. Overall, Databricks is cost-effective and offers a convenient setup with Spark clusters.
Solution Architect at Mercedes-Benz AG4.5We use Databricks on Azure for collaboration and cost efficiency, benefiting from features like notebooks and automatic scheduling. However, API and model deployment could improve. For simple data tasks, ADF might be more cost-effective, but Databricks excels in analytics.
Data Engineer at CRAFT Tech4.0I use Databricks primarily to build data lakehouses for clients. Its Delta Lake and Unity Catalog are valuable features, aiding in data governance and integration. Improvements are needed in dashboards, but their new IBI dashboard shows promise.
Data Scientist at a financial services firm with 10,001+ employees4.0I primarily use Databricks for processing large-scale datasets with Apache Spark due to its strong native integration and ease of resource management. However, its capabilities with spatial data need improvement. Despite this, its usability significantly boosts our ROI.
Senior Data Engineer at Shell4.5I use Databricks for data transformations and analytics, leveraging Delta Lake and Delta Life tables for ETL processes. The platform offers valuable features like a user-friendly interface and AI functionalities, although debugging in Delta Live Table needs improvement.