Databricks vs VAST Data comparison

The compared Databricks and VAST Data solutions aren't in the same category. Databricks is ranked #8 in CDW , with an average rating of 8.3, and holds a 9.0% mindshare in the category. VAST Data is ranked #11 in N , and holds a 6.1% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of VAST Data users who would recommend it.

Databricks

Read 91 Databricks reviews

2,043 Views
109 Comparison Views

96% willing to recommend

VAST Data

Read 2 VAST Data reviews

1,260 Views
417 Comparison Views

100% willing to recommend

Databricks

VAST Data

Comparison Buyer's Guide

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Executive SummaryUpdated on Apr 20, 2025

Databricks and VAST Data are notable players in the data technology space, excelling in data processing and storage solutions, respectively. Databricks seems to have the upper hand with its seamless cloud integration and collaborative analytics capabilities, which can be more advantageous for real-time data processing needs.

Features: Databricks provides robust support for big data processing, real-time analytics, and a collaborative data science environment. Its integration with cloud platforms enhances efficiency and support for varying data analytics tasks. VAST Data offers high-speed, scalable data storage solutions with a focus on cost-effective hardware and software integration. It emphasizes storage innovation, providing efficient, high-performance data management capabilities.

Ease of Deployment and Customer Service: Databricks facilitates a straightforward deployment within cloud environments, supported by comprehensive documentation and a large community. Its cloud-centric nature simplifies deployment processes. VAST Data's deployment focuses on its storage solutions, which may involve more complex configurations, especially within enterprise settings. Both offer responsive customer service, but Databricks benefits from a wider array of support resources due to its extensive ecosystem.

Pricing and ROI: Databricks usually presents a lower initial setup cost, integrating effectively with existing cloud investments and promising quicker ROI. VAST Data might entail higher upfront expenses due to its sophisticated storage components but can offer significant savings over time through storage efficiency. Databricks is cost-effective owing to its cloud integration, while VAST Data provides substantial long-term value through its efficient storage solutions.

To learn more, read our detailed Cloud Data Warehouse Report (Updated: June 2025).

Buyer's Guide

Cloud Data Warehouse

June 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Databricks

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (8th), Data Science Platforms (1st), Streaming Analytics (1st)

VAST Data

Average Rating

10.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

All-Flash Storage (32nd), File and Object Storage (18th), NVMe All-Flash Storage Arrays (11th)

Mindshare comparison

Databricks and VAST Data aren’t in the same category and serve different purposes. Databricks is designed for Cloud Data Warehouse and holds a mindshare of 9.0%, up 4.6% compared to last year.
VAST Data, on the other hand, focuses on NVMe All-Flash Storage Arrays, holds 6.1% mindshare, down 6.6% since last year.

Cloud Data Warehouse

NVMe All-Flash Storage Arrays

Featured Reviews

ShubhamSharma7

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

Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

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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Alan Powers

HPC CTO at a manufacturing company with 10,001+ employees

Stability-wise, a device that has been up and running for years

The failover capability and resiliency are some of the solution's valuable features. The big thing is resilience because it has richer coding in it, so multiple devices can't fail. Also, one can still access a number of CBoxes that can allow one to access their file system. Once a device fails, it fails the transparency of the end-user, and it just starts using another resource. The encryption capability, the snapshots, along with a whole bunch of features make the tool valuable. VAST Data keeps adding more and more features all the time.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The solution is very simple and stable."

"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."

"The simplicity of development is the most valuable feature."

"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."

"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."

"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."

"Databricks has a Unified Catalog that assists with secured access and governance."

"It is fast, it's scalable, and it does the job it needs to do."

More Databricks pros

"This has been one of the most reliable storage systems that I have ever used."

"The solution is useful for machine learning and scientific applications, including computer simulations."

Cons

"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."

"In the future, I would like to see Data Lake support. That is something that I'm looking forward to."

"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."

"I would like more integration with SQL for using data in different workspaces."

"We'd like a more visual dashboard for analysis It needs better UI."

"In the next release, I would like to see more optimization features."

"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."

"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."

More Databricks cons

"The read/write ratio is an area in the solution with some flaws and needs improvement."

"The write performance could be improved because it is less than half of the read performance."

Pricing and Cost Advice

"The solution is based on a licensing model."

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."

"We only pay for the Azure compute behind the solution."

"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."

"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"Price-wise, I would rate Databricks a three out of five."

"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."

More Databricks pricing and cost advice

"Price-wise, VAST Data is not the cheapest, not the most expensive one."

"We acquired VAST Data as a one-time, capital purchase."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Computer Software Company

15%

Manufacturing Company

14%

Financial Services Firm

13%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

See all answers

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

See all answers

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

See all answers

Can you provide any information you may have on VAST Data?

Here are some technical details about VAST Data: VAST Data is a software-defined storage platform designed to scale to petabyte and exabyte levels. The platform uses a unique architecture that de...

See all answers

Comparisons

Dataiku vs Databricks

Compared 10% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 7% of the time

Domino Data Science Platform vs Databricks

Compared 2% of the time

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NetApp AFF vs VAST Data

Compared 11% of the time

Dell PowerScale (Isilon) vs VAST Data

Compared 10% of the time

Pure Storage FlashBlade vs VAST Data

Compared 9% of the time

Qumulo vs VAST Data

Compared 8% of the time

MinIO vs VAST Data

Compared 7% of the time

More VAST Data Competitors

Product Reports

Buyer's Guide

Databricks

June 2025

Download Databricks product report

Buyer's Guide

NVMe All-Flash Storage Arrays

June 2025

Download VAST Data product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Overview

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

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

Innovation to break decades of data storage trade-offs.

VAST Data

Sample Customers

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

Norwest Venture Partners, General Dynamics Information Technology, Ginkgo Bioworks

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