Databricks Reviews

Name: Databricks
Brand: Databricks
Rating: 4 (82 reviews)

Vendor: Databricks

4.1 out of 5

82 reviews
96% willing to recommend

5,273 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. Databricks is most commonly compared to Microsoft Power BI: Databricks vs Microsoft Power BI. 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 814,649 peers since 2012

Featured reviews

Dunstan Matekenya

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

I primarily use Databricks to process large-scale data sets with Apache Spark. My main use case is processing large data sets, such as 600 GB or 800 GB Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of…

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Sudhendra Umarji

Technical Architect at Infosys

We use this solution for finding anomalies and applying the rules to the streaming data. There are around 50 people using this solution in my organization, including data scientists The ability to stream data and the windowing feature are valuable. There are a number of targeted integration…

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Axel Richier

Tech Lead Consultant | Manager Data Engineering at Ekimetrics

The shared experience of collaborative notebooks is probably the most useful aspect since, as an expert, it allows me to help my juniors debug their books and their code live. I can do some live coding with them or help them find the errors very efficiently. It has become very simple to set up thanks to its official Terraform provider and the open-source modules made available on GitHub. I love Databricks due to the fact that we can now deploy it in 15 minutes and it's ready to use. That's very nice since we often help our clients in deploying their first Data Platform with Databricks. The solution is stable, with LTS Runtimes that have proven to remain stable over the years.

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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 Sep 29, 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.

"The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle."
"The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale."
"Databricks is a robust solution for big data processing, offering flexibility and powerful features."

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.

"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."
"The biggest problem associated with the product is that it is quite pricey."
"The product could be improved regarding the delay when switching to higher-performing virtual machines compared to other platforms."

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

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Insurance Company

Retailer

Government

Energy/Utilities Company

University

Educational Organization

Media Company

Construction Company

Comms Service Provider

Real Estate/Law Firm

Transportation Company

Non Profit

Hospitality Company

Wholesaler/Distributor

Pharma/Biotech Company

Logistics Company

Recreational Facilities/Services Company

Legal Firm

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.