Databricks Reviews

Name: Databricks
Brand: Databricks
Rating: 4.1 (84 reviews)

Vendor: Databricks

4.1 out of 5

84 reviews
96% willing to recommend

5,277 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 and Streaming Analytics tools. PeerSpot users give Databricks an average rating of 8.2 out of 10. Based on the analysis of the 84 most recent Databricks reviews, the overall sentiment is positive, with a sentiment score of 7.4. (Among the highest in the category). Databricks is most commonly compared to Microsoft Azure Machine Learning Studio: Databricks vs Microsoft Azure Machine Learning Studio. 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 16% of all views.

Helped 816,660 peers since 2012

Featured reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of its strengths. Another strength is that the platform makes it very easy to manage resources. For example, setting up a cluster of five or fifteen nodes is straightforward with Databricks. The notebook environment is also excellent, making it easy to perform various tasks.

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Sudhendra Umarji

Technical Architect at Infosys

Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing. There should be reliability between these two. Databricks is based on open sources. If it's more synchronous between the Microsoft technology and the programming languages, it'll be better. Python has better languages, but compatibility would be a great help. I would like to have better support for Microsoft technology and better language components. With Azure or Cosmo DB, I can store other data links or time series data tables. That would be a great help for analytics in real time.

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Monalisha Nayak

Senior Data Engineer at Shell

Databricks is used for transformations and streaming data processing. We utilize it primarily for data analytics, including the use of Delta Lake and Delta Life tables for ETL processes, dashboards for analysis, and the Unity catalog for role management Databricks improves our data analysis tasks…

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Databricks mindshare

Product category:

As of November 2024, the mindshare of Databricks in the Data Science Platforms category stands at 19.1%, up from 19.1% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

Key learnings from peers

Last updated Nov 7, 2024

Valuable Features

Databricks' valuable features include easy large-scale analytics, built-in optimization, Delta data format, notebooks on Spark, multiple cluster support, automation via API, combined SQL and code interface, large inbuilt functions, elasticity, machine learning libraries, image visualization, quick data loading, scalable performance, collaborative workspaces, intuitive UI, cloud integration, fast processing, robust data governance, wide language support, shared notebooks, ML capabilities, seamless integration, and detailed documentation.

"It offers AI functionalities that assist with code management and machine learning processes."
"It helps integrate data science and machine learning capabilities."
"The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle."

Room for Improvement

Databricks requires better dataset visualization, more advanced visualization tools, more predictive analytics and machine learning libraries, improved pricing, enhanced error messages, and easier integration with third-party tools. Users need better user interfaces, more library imports, improved documentation, and clearer updates. Databricks lag behind in ease of use for non-technical users, integration with various platforms, more model scoring abilities, and handling spatial data. Also, enhanced tutorials and examples would be beneficial.

"While Databricks is generally a robust solution, I have noticed a limitation with debugging in the Delta Live Table, which could be improved."
"Performance could be improved."
"I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier."

ROI

Entities reported monetary and operational efficiency gains from Databricks, achieving substantial cost savings by transitioning from expensive on-site clusters to more cost-effective cloud workspaces. They observed performance improvements, notably faster query execution and reduced expenditure on traditional databases. While some have yet to finalize their assessments due to recent adoption, most acknowledged time and effort savings. Smaller organizations noted favorable ROI, attributing it to ease of use and accessibility, despite challenges in quantifying direct financial returns.

Pricing

Databricks offers a pay-as-you-go model, is generally viewed as expensive but competitive compared to other solutions. Costs can vary widely based on cloud platform, data volume, and compute needs. Some users find it cheaper on Azure. High performance typically incurs higher costs, yet some appreciate the value for batch processing and large workloads. Licensing is tiered and flexible, with pricing dependent on usage and infrastructure. Users highlight variability in setup costs, with some opting for internal high-performance solutions to manage expenses.

"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 use Databricks for data processing, running large analytics queries, integrating data from various sources, and machine learning tasks. It aids in ETL, streaming analytics, predictive analysis, and developing custom models. Users leverage its capabilities for DevOps, managing data pipelines, and financial reporting. It supports data management, advanced analytics, and big data projects across various industries, including insurance, retail, and pharmaceuticals. They utilize it for forecasting, customer marketing analytics, and reliability engineering.

Service and Support

Databricks customer service and support are generally rated positively. Many users note the comprehensive documentation, reducing the need for direct support. Those who have interacted with technical support describe it as knowledgeable, timely, and responsive. While several users did not find it necessary to contact support, others mentioned effective communication and proactive assistance. Some isolated cases reported communication issues or delays, but these were rare. Interaction through Microsoft partnerships also influences the experiences reported.

Deployment

Users find Databricks's initial setup generally straightforward, especially with adequate training and documentation. Deployments vary in complexity and duration based on factors like experience and business needs. Cloud-based deployment simplifies the process but still requires technical expertise. External databases and configurations can add complexity. Many successfully complete initial setups quickly, within hours to weeks, but some report challenges with longer deployments. Databricks integrates well with platforms like Azure, but the ease of use depends on technical proficiency.

Scalability

Databricks is known for its high scalability, handling data sizes from megabytes to petabytes efficiently. Many users appreciate its simple and seamless scaling process. Budget can be a limitation for some, and enhancements for automatic node configuration are desired. Both small and large companies utilize it, with some noting significant expansion plans. Databricks’ ability to scale across multiple cloud providers and various use cases is frequently highlighted and rated highly.

Stability

Many users find Databricks stable and reliable, citing its robustness and minimal downtime. Some mention occasional issues with version updates or long-running processes but see these as minor. Most describe it as very stable, rating its performance highly, often between eight and ten out of ten. Few mentions of bugs or glitches are present, and its reliability is consistently praised. Users appreciate its cluster management capabilities and support included as part of the service.

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

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Top industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Insurance Company

Retailer

Government

Energy/Utilities Company

University

Educational Organization

Media Company

Construction Company

Comms Service Provider

Real Estate/Law Firm

Non Profit

Transportation Company

Hospitality Company

Wholesaler/Distributor

Pharma/Biotech Company

Recreational Facilities/Services Company

Legal Firm

Logistics Company

Performing Arts

Outsourcing 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.