Databricks vs VAST Data comparison

The compared Databricks and VAST Data solutions aren't in the same category. Databricks is ranked #1 in Data Science Platforms , with an average rating of 8.3, and holds a 19.1% mindshare in the category. VAST Data is ranked #8 in N , with an average rating of 10.0, and holds a 5.4% 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 84 Databricks reviews

17,588 Views
11,896 Comparison Views

96% willing to recommend

VAST Data

Read 2 VAST Data reviews

1,261 Views
947 Comparison Views

100% willing to recommend

Databricks

VAST Data

Comparison Buyer's Guide

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Executive SummaryUpdated on Oct 8, 2024

Databricks and VAST Data are leaders in data analytics and data storage. Databricks is preferred for its pricing and support, while VAST Data is noted for strong features.

Features: Databricks provides advanced machine learning, seamless cloud integration, and scalability. Its collaborative environment is highly valued. VAST Data features efficient storage, high performance, and data integrity.

Room for Improvement: Databricks needs simpler configuration and better documentation. VAST Data should improve analytics capabilities and interoperability with various data formats.

Ease of Deployment and Customer Service: Databricks is easier to deploy, with comprehensive support and guidance. VAST Data faces mixed reviews on deployment, but receives praise for customer service.

Pricing and ROI: Databricks is known for competitive pricing and valuable ROI in analytics. VAST Data has a higher upfront cost, with long-term savings in storage efficiency.

To learn more, read our detailed Data Science Platforms Report (Updated: December 2024).

Buyer's Guide

Data Science Platforms

December 2024

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Helped 829,541 peers since 2012

Categories and Ranking

Databricks

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

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 (20th), File and Object Storage (8th), NVMe All-Flash Storage Arrays (8th)

Mindshare comparison

Databricks and VAST Data aren’t in the same category and serve different purposes. Databricks is designed for Data Science Platforms and holds a mindshare of 19.1%, up 18.5% compared to last year.
VAST Data, on the other hand, focuses on NVMe All-Flash Storage Arrays, holds 5.4% mindshare, down 6.7% since last year.

Data Science Platforms

NVMe All-Flash Storage Arrays

Featured Reviews

Dunstan Matekenya

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

Process large-scale data sets and integrates with Apache Spark with notebook environment

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

"I work in the data science field and I found Databricks to be very useful."

"Ability to work collaboratively without having to worry about the infrastructure."

"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."

"The solution is very easy to use."

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

"Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily."

"Databricks' most valuable feature is the data transformation through PySpark."

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

"Costs can quickly add up if you don't plan for it."

"A lot of people are required to manage this solution."

"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."

"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."

"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."

"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."

"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."

"Performance could be improved."

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

"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."

"The product pricing is moderate."

"Databricks uses a price-per-use model, where you can use as much compute as you need."

"The billing of Databricks can be difficult and should improve."

"The solution requires a subscription."

"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."

"The pricing depends on the usage itself."

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

More Databricks pricing and cost advice

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Computer Software Company

17%

Manufacturing Company

15%

Financial Services Firm

11%

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 ...

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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...

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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...

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What do you like most about VAST Data?

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

See all answers

What is your experience regarding pricing and costs for VAST Data?

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

See all answers

What needs improvement with VAST Data?

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

See all answers

Comparisons

Dataiku vs Databricks

Compared 11% of the time

Dremio vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 7% of the time

More Databricks Competitors

Pure Storage FlashBlade vs VAST Data

Compared 12% of the time

NetApp AFF vs VAST Data

Compared 10% of the time

Dell PowerScale (Isilon) vs VAST Data

Compared 10% of the time

Qumulo vs VAST Data

Compared 10% of the time

Snowflake vs VAST Data

Compared 8% of the time

More VAST Data Competitors

Product Reports

Buyer's Guide

Databricks

January 2025

Download Databricks product report

Buyer's Guide

NVMe All-Flash Storage Arrays

December 2024

Download VAST Data product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Learn More

Databricks

VAST Data

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.

Innovation to break decades of data storage trade-offs.

Sample Customers

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

Norwest Venture Partners, General Dynamics Information Technology, Ginkgo Bioworks

Buyer's Guide

Data Science Platforms

December 2024

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