Databricks Reviews

Name: Databricks
Brand: Databricks
Rating: 4.1 (85 reviews)

Vendor: Databricks

4.1 out of 5

85 reviews
96% willing to recommend

5,278 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 Teradata: Databricks vs Teradata. Databricks is popular among the large enterprise segment, accounting for 72% 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 831,020 peers since 2012

Featured reviews

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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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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PeerAnalyst reports

Type	Title	Date
Category	Cloud Data Warehouse	Jan 17, 2025	Download
Product	Reviews, tips, and advice from real users	Jan 17, 2025	Download

Title	Rating	Mindshare	Recommending
Teradata	4.1	N/A	87%	76 interviews Add to research
Dremio	4.3	4.6%	100%	7 interviews Add to research

Valuable Features

Databricks offers ease in running large-scale analytics with a slick UI, built-in optimization, and Delta data format. Users value the Notebook, parallel execution, SQL usage, and API automation. Its integration with Spark provides fast performance and machine learning libraries. Features like clustering, scalability, collaboration, and multiple language support are highly appreciated. The platform's cloud integration, data processing, governance, and user-friendly interface also stand out, making it versatile for data engineering and machine learning tasks.

"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."
"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."
"It offers AI functionalities that assist with code management and machine learning processes."

Room for Improvement

Databricks could enhance its visualization capabilities and pricing structure. There is a need for better integration, particularly with other BI tools. Improvements in user interface, predictive analytics features, and additional libraries like PyTorch or TensorFlow are desired. Users face challenges with error messages and data integration. More open-source support, flexible licensing, and ease of use would benefit users. Adding trial versions and better documentation could help newcomers explore Databricks effectively.

"They release patches that sometimes break our code. These patches are supposed to fix issues, but sometimes they cause disruptions."
"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."
"While Databricks is generally a robust solution, I have noticed a limitation with debugging in the Delta Live Table, which could be improved."

ROI

Databricks provided an annual return of approximately USD $75k, as users migrated from Hadoop clusters, reducing expenses by a significant margin. Users appreciated the ease of use for large-scale data processing and cost efficiency, especially for smaller organizations. Although its financial impact is still being assessed by some, others noted significant time and effort savings. Benefits were maximized with varied use cases but less suitable for just data ingestion tasks.

Pricing

Databricks' pricing, often seen as high, varies significantly based on usage, data volume, and cloud provider, typically operating on a pay-per-use model. Costs depend on the compute and storage needs, the chosen cloud platform, and whether using a standard or enterprise version. Some users find it expensive compared to open-source alternatives, yet others see it as competitive when factoring in functionality and cloud efficiencies. Licensing can be complex, with fees influenced by geographic location and specific use cases.

"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 utilized for multiple purposes including running large analytics queries, ETL processes, streaming analytics, machine learning, data integration, and batch processing. Teams leverage Databricks for DevOps, advanced analytics, building data lakes, and managing data pipelines. It integrates with Azure and supports diverse environments for data scientists, engineers, and IT professionals across industries like finance, insurance, retail, and pharmaceuticals, enabling collaboration and deploying models for client-driven data solutions.

Service and Support

Databricks' customer service and support have mixed feedback. Many users praise the prompt, helpful, and knowledgeable responses, giving high ratings. Some users report satisfactory experiences without direct contact due to strong documentation or Microsoft assistance. A few highlight communication issues or slow responses, especially without premium support. Many appreciate proactive customer engagement and training, enhancing usability and satisfaction. Overall, users find the support beneficial, especially when partnered, though some mention room for improvement in response times.

Deployment

Many users describe Databricks's initial setup as straightforward, especially for those familiar with Spark or using cloud platforms. It's noted for good documentation, which aids in deployment. While some mention complexity in certain cases, especially requiring technical expertise or involving business requirements, most find the setup quick and aided by a user-friendly interface. Deployment times vary, often depending on workload and team readiness, with some experiencing longer durations due to unique configurations.

Scalability

Databricks is praised for its high scalability, making it suitable for handling massive datasets across various cloud platforms. Users find it easy to scale, though cost considerations can be challenging. Many appreciate its ability to manage workloads efficiently and the flexibility to expand usage. The platform is widely adopted across different teams, seeing significant numbers of users and proving beneficial for large-scale data processing and analytics.

Stability

Databricks is generally perceived as stable, with many users describing it as reliable and robust. Challenges occur primarily during version transitions and when recommendations are not followed, leading to minor disruptions like restarts or bugs. Patches can sometimes affect stability. However, many find Databricks consistent with high ratings, particularly when effectively used. Some users have noted rare issues but acknowledge the support included with Databricks helps maintain its functionality.

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.

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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

Construction Company

Real Estate/Law Firm

Transportation Company

Non Profit

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Pharma/Biotech Company

Recreational Facilities/Services Company

Legal Firm

Wholesaler/Distributor

Performing Arts

Logistics Company

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.