Databricks Reviews

Name: Databricks
Brand: Databricks
Rating: 4.1 (88 reviews)

Vendor: Databricks

4.1 out of 5

88 reviews
96% willing to recommend

5,280 followers

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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 #7 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 71% 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.

Helped 839,164 peers since 2012

Featured reviews

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

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Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster. This reduces costs. The scheduling part is managed by Databricks itself, for example, when it is idle, it will automatically turn off. All these features are handled by Databricks, reducing costs. We do not need to schedule separately. For example, on AWS EC2, we have to create a Lambda function or use System Manager templates to schedule EC2 and EMRs. Here, it is taken care of, saving significant resources. Additionally, notebooks can be shared within the development team which saves effort. Developers can share their notebooks. Git and Azure DevOps integration on the Databricks side is also very helpful.

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Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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Databricks mindshare

Product category:

As of March 2025, the mindshare of Databricks in the Cloud Data Warehouse category stands at 7.8%, up from 2.9% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Cloud Data Warehouse

PeerAnalyst reports

Type	Title	Date
Category	Cloud Data Warehouse	Mar 1, 2025	Download
Product	Reviews, tips, and advice from real users	Mar 1, 2025	Download
Comparison	Databricks vs Snowflake	Mar 1, 2025	Download
Comparison	Databricks vs Microsoft Azure Synapse Analytics	Mar 1, 2025	Download
Comparison	Databricks vs Azure Data Factory	Mar 1, 2025	Download

Title	Rating	Mindshare	Recommending
Teradata	4.1	8.9%	87%	76 interviews Add to research
Dremio	4.3	10.4%	100%	8 interviews Add to research

Valuable Features

Databricks is praised for its ease in running large-scale analytics with a user-friendly interface. Key features include a notebook for data processing and model execution, seamless integration with SQL and Python, and scalable Spark clusters. Users value its machine learning capabilities, automation, and flexibility in data handling. The Delta Lake and Unity Catalog offer strong data governance. Collaboration is enhanced through shared notebooks, and cloud integration is seamless.

"Databricks' capability to process data in parallel enhances data processing speed."
"I would rate them ten out of ten."
"Databricks serves as a single platform that can handle numerous end-to-end machine learning tasks."

Room for Improvement

Databricks users suggest improvements in pricing transparency, more robust integration with tools like Power BI, enhanced machine learning libraries, and better visualization capabilities. Enhanced user interface, clearer error messaging, and expanded cloud platform support are needed. Improved data source connectors, more comprehensive documentation, and a more user-friendly experience for non-technical users are also recommended. Debugging, scalability, and advanced data storage options require focus for enhancement.

"Cluster failure is one of the biggest weaknesses I notice in our Databricks."
"The API deployment and model deployment are not easy on the Databricks side."
"There has been a significant evolution in databases. One area of improvement is the Databricks File System (DBFS), where command-line challenges arise when accessing files."

ROI

Many report a return on investment with Databricks, citing cost savings from transitioning workloads from expensive onsite systems to the cloud, improving query efficiency, and reducing traditional system expenses. Users appreciate the ease of use, contributing to their ROI in terms of time and effort. Cost competitiveness is noted, however, some find it challenging to convert efficiency into direct monetary returns, particularly for nonprofits. Overall, positive outcomes are observed in varied use cases.

Pricing

Databricks' pricing varies widely based on usage, cluster type, and cloud provider. Many enterprise users find it expensive, especially in regions with higher exchange rates. Databricks employs a pay-per-use model, leading to variable costs dependent on compute and storage needs. Users appreciate its competitive pricing over on-premises solutions and note that costs can be optimized by managing resources efficiently. While some find it costly, many rate it as reasonable compared to its competitors.

"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

Databricks is primarily used for data processing, advanced analytics, machine learning, and ETL. Users leverage it for running Spark jobs, building custom models, managing large-scale data, and data transformation. It supports integration with Azure and AWS for deploying machine learning models, creating data lakes, and API interfaces. Companies in various sectors, including financial services, pharmaceuticals, and insurance, utilize Databricks for data engineering, analytics, and managing real-time and batch data processing.

Service and Support

Databricks customer service and support are described as responsive and beneficial, with many users praising its effectiveness. Although some users have had limited contact, those who did interact reported prompt assistance and knowledgeable support staff. Several highlighted comprehensive documentation and good experiences due to partnerships. There were occasional issues with communication, primarily due to language barriers or when interacting through Microsoft support. However, most users expressed high satisfaction, often rating it highly.

Deployment

Databricks's initial setup is generally regarded as straightforward, with users noting that deployment varies based on use cases and requirements. Many emphasize the ease when setting up in cloud environments like Azure. While not considered overly complex, setup can require technical expertise, particularly if involving custom configurations or external database integrations. Documentation and available training are highlighted as helpful resources, making the process smoother for those with relevant experience in Spark or data engineering.

Scalability

Databricks offers impressive scalability, with easy expansion across various data sizes and workloads. Many find it simple to scale up from smaller projects to large-scale implementations. The standout feature is the ability to scale both vertically and horizontally, with cloud-based elasticity enhancing this capability. Costs and manual node configuration can pose challenges, but the versatility of Databricks across platforms like Azure, AWS, and its own cloud is beneficial. Scalability ratings are generally high, emphasizing its robust performance.

Stability

Databricks is widely regarded as stable, with most users rating its stability between seven to nine out of ten. It handles large datasets effectively, although some report occasional issues during version updates or long-running processes. Stability is consistent in development environments, although some hiccups occur when scaling to production or due to inefficient usage. Bugs are minimal, often quickly resolved with support included, and patches sometimes disrupt operations but do not drastically impact uptime.

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

By visitors reading reviews

Top industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Insurance Company

Government

Retailer

Energy/Utilities Company

University

Educational Organization

Media Company

Comms Service Provider

Real Estate/Law Firm

Construction Company

Non Profit

Transportation Company

Hospitality Company

Pharma/Biotech Company

Recreational Facilities/Services Company

Wholesaler/Distributor

Legal Firm

Performing Arts

Outsourcing Company

Logistics Company

Consumer Goods Company

Compare Databricks with alternative products

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.