Databricks vs Redpanda comparison

Databricks and Redpanda Data are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.3, while Redpanda Data is ranked #15 with an average rating of 8.8. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Redpanda Data users who would recommend it.

Databricks

Read 89 Databricks reviews

5,646 Views
4,053 Comparison Views

96% willing to recommend

Redpanda

Read 4 Redpanda reviews

202 Views
77 Comparison Views

100% willing to recommend

Databricks

Redpanda

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Executive SummaryUpdated on Dec 17, 2024

Databricks and Redpanda compete in the analytics and data streaming categories. Databricks seems to have the upper hand in big data capabilities and machine learning support, while Redpanda leads in cost-effectiveness and performance efficiency.

Features: Databricks offers seamless cluster management, an integration of Spark, and programming support for SQL and Python. It emphasizes big data capabilities, collaborative features, and scalability. Redpanda focuses on performance with high-speed data streaming built on C++, making it efficient and adaptable. It distinguishes itself with cost-effectiveness and simplicity.

Room for Improvement: Databricks users report vague error messages and limited online information, particularly in cost visibility and integration with popular BI tools. Redpanda can improve its documentation, especially for self-hosting scenarios, and refine its command-line tools for better data insights.

Ease of Deployment and Customer Service: Databricks offers deployment across public, private, and hybrid clouds with a reputable technical support team, though it sometimes faces delays and language barriers. Redpanda is centered on on-premises deployment, indicating less flexibility in cloud dynamics but benefits from a simpler setup. Both platforms need to enhance documentation and deployment guidance.

Pricing and ROI: Databricks is often seen as expensive, with users favoring a pay-as-you-go model, although it delivers positive ROI in reducing traditional RDBMS costs. Redpanda stands out for affordability, being cheaper than Kafka alternatives, and is praised for its free versions, offering high cost-effectiveness.

To learn more, read our detailed Databricks vs. Redpanda Report (Updated: April 2025).

Buyer's Guide

Databricks vs. Redpanda

April 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

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th), Data Science Platforms (1st)

Redpanda

Ranking in Streaming Analytics

15th

Average Rating

8.8

Reviews Sentiment

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

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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Vishal M Godi

Big Data Teaching Assistant at Center for Cloud Computing and Big Data, PES University

High-performance message brokering with excellent documentation and an easy setup

The industry standard for this kind of platform is Kafka. Confluent Kafka has acquired it. Kafka is an open-source platform built by Apache. Confluent is the commercial version of it. The major improvement of Redpanda over Kafka is firstly, good documentation. Redpanda's documentation is very easily understandable, and they have a lot of examples. In addition to that, most of the setups include using another technology called Docker, which I am very familiar with. Setting up technologies using Docker is very convenient to me, and Redpanda has provided many templates for that. Redpanda has its own built-in metrics exporter, making it easier to monitor and check performance. What makes Redpanda superior is its performance since it's written in C++. C++ is pretty much the standard for high-performance applications.

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Quotes from Members

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

Pros

"The ability to stream data and the windowing feature are valuable."

"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."

"The setup was straightforward."

"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."

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

"The most valuable feature of Databricks is the notebook, data factory, and ease of use."

"I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well."

"We can scale the product."

More Databricks pros

"The UI is modern."

"What makes Redpanda superior is its performance since it's written in C++. C++ is pretty much the standard for high-performance applications."

"The cost savings have been significant."

"I tested it with ten-plus nodes, and it's highly scalable."

Cons

"The API deployment and model deployment are not easy on the Databricks side."

"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."

"The solution has some scalability and integration limitations when consolidating legacy systems."

"As a data engineer, I see cluster failure in our Databricks user databases as a major issue."

"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."

"Implementation of Databricks is still very code heavy."

"Can be improved by including drag-and-drop features."

"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."

More Databricks cons

"Recently, for the documentation, they've built their own AI chatbot, which is focused on giving you answers based on their documentation. While using that, I did not find it to be very good."

"The command-line tools need to be improved. To quickly check the status of the topics and all."

"The version control mechanism must be improved."

"When it comes to self-hosting, their documentation could be improved."

Pricing and Cost Advice

"It is an expensive tool. The licensing model is a pay-as-you-go one."

"The solution is a good value for batch processing and huge workloads."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

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

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

"I rate the price of Databricks as eight out of ten."

"The solution is based on a licensing model."

"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."

More Databricks pricing and cost advice

"It's free. Everybody can use it, only support is paid."

"Redpanda is cheaper than its competitors."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

No data available

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 is your experience regarding pricing and costs for Redpanda?

Redpanda is actually a commercial platform, but they do provide free versions as well. I've been working only with the free versions.

See all answers

What needs improvement with Redpanda?

Recently, for the documentation, they've built their own AI chatbot, which is focused on giving you answers based on their documentation. While using that, I did not find it to be very good. Maybe ...

See all answers

What is your primary use case for Redpanda?

I have worked with Redpanda for the past two to three months. Mainly in the tech industry or software industry, there's a huge rise of streaming data. Redpanda serves as a very reliable and fast me...

See all answers

Comparisons

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Compared 11% of the time

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Compared 9% of the time

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Compared 56% of the time

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Product Reports

Buyer's Guide

Databricks

April 2025

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Buyer's Guide

Redpanda

April 2025

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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

Redpanda offers a modern, intuitive interface with efficient resource usage, seamlessly integrating with Kafka, and enhancing performance through fast operations and reliable support. Organizations benefit from its memory efficiency and high performance for demanding data workloads.

Built on a C++ foundation, Redpanda integrates easily with Kafka clients and stands out for fast operations, simplified Docker setup, and effective metrics monitoring. Performance is enhanced by memory efficiency and high throughput capabilities. The community provides robust support, and clear documentation aids the adoption process. However, improvements could be made in version control, command-line tools, and documentation, particularly in areas such as automation file management and chatbot documentation assistance. Redpanda is widely utilized in data streaming and normalization, efficiently handling large telemetry data volumes with minimal latency, essential for building asynchronous applications across microservices and monitoring systems.

What are the most important features of Redpanda?

Modern UI: Provides an intuitive interface for ease of use.
Efficient Resource Usage: Built on C++ for performance optimization.
Seamless Integration: Works well with Kafka clients for smooth operations.
Simplified Setup: Docker templates facilitate easy installation.
Built-in Metrics Exporters: Enhance monitoring and visibility.
High Performance: Ensures memory efficiency and quick data processing.

What benefits and ROI can users expect?

Fast Operations: Accelerates data processing for efficient workflows.
Reliable Support: Strong community aids troubleshooting and development.
Clear Documentation: Helps users quickly onboard and manage Redpanda.
Low Latency: Critical for applications requiring quick response times.
Sub-second Response: Supports demanding performance and latency needs.

Redpanda is commonly implemented in tech and software industries to streamline data streaming and normalization processes, handling high telemetry data volumes effectively. Its capacity for sub-second response times makes it crucial for companies developing asynchronous applications, especially in microservices and monitoring systems.

Redpanda Data

Sample Customers

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

Information Not Available

Buyer's Guide

Databricks vs. Redpanda

April 2025

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