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 #14 with an average rating of 9.3. Databricks holds a 7.9% mindshare in SA, compared to Redpanda Data’s 2.0% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Redpanda Data users who would recommend it.

Databricks

Read 94 Databricks reviews

22,831 Views
4,338 Comparison Views

96% willing to recommend

Redpanda

Read 6 Redpanda reviews

1,148 Views
1,137 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: June 2026).

Buyer's Guide

Databricks vs. Redpanda

June 2026

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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 (4th), Data Science Platforms (1st), Data Management Platforms (DMP) (5th)

Redpanda

Ranking in Streaming Analytics

14th

Average Rating

9.4

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2026, in the Streaming Analytics category, the mindshare of Databricks is 7.9%, down from 14.5% compared to the previous year. The mindshare of Redpanda is 2.0%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Databricks	7.9%
Redpanda	2.0%
Other	90.1%

Streaming Analytics

Featured Reviews

SimonRobinson

Governance And Engagement Lead

Improved data governance has enabled sensitive data tracking but cost management still needs work

I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs. We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake. I think introducing customer repositories would facilitate easier implementation with Databricks.

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ArpitShah

Software Analyst at CLSA

Event streaming has simplified video data cleanup and now powers real-time analytics

One area for improvement is providing more examples. For instance, Redpanda could be more useful as a sink where you get the data and can directly push to S3. While this is possible through the API, there are better and faster ways to do it. You can make a million API calls and accomplish the task in one and a half hours, but the same thing can be done in ten minutes through other methods. These faster approaches are not documented in obvious places. You have to find information scattered across various blogs. Redpanda should collect all the good blogs and best practices and put them in their documentation. This is more about knowledge management and making it easy for users to understand the product for complex use cases. For simple use cases, it is straightforward. We all use the basic pipe functionality. However, providing more examples would be useful. For example, integration with AWS and the AWS ecosystem would be cool.

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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

"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."

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

"It is a cost-effective solution."

"The initial setup is pretty easy."

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

"It's easy to increase performance as required."

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

"Databricks is a truly essential platform for data engineering needs, and I recommend it to anyone looking to advance in the data engineering field."

More Databricks pros

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

"The cost savings have been significant."

"The performance is superb, and the value we are getting for the money we pay is great."

"Aside from its lightweight design, Redpanda is essentially a clone of Kafka with all the good features of Kafka, with the only difference being that Kafka needs too many resources while Redpanda is a very good, lightweight, and very fast database."

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

"Redpanda is developer-friendly, and we need to do much less configuration because Redpanda provides out-of-the-box configuration for us."

"I would recommend Redpanda to others because it's easy to set up, consumes less resources, and is stable compared to other tools."

"Redpanda was simple and fast, so we went with Redpanda and it just works."

Cons

"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."

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

"The product should provide more advanced features in future releases."

"Databricks can improve by making the documentation better."

"In my view, the fundamental approach of implementing Databricks is still very code heavy, more than you find in Azure Data Factory and other technologies like Informatica or SQL Server Integration Service."

"In my opinion, areas of Databricks that have room for improvement involve the dashboards. Until recently, everyone used third-party systems such as Power BI to connect to Databricks for dashboards and reports, but they're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further."

"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."

"From a purely technical perspective, I would rate Databricks an eight out of ten. However, there is a failure in terms of user adoption."

More Databricks cons

"In Redpanda, the areas that have room for improvement are in the clustering part."

"I think Redpanda is overall very good for us, and I am uncertain whether Redpanda can scale to very large companies as we are a medium-sized startup."

"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."

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

"One area for improvement is providing more examples."

Pricing and Cost Advice

"We're charged on what the data throughput is and also what the compute time is."

"Databricks' cost could be improved."

"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."

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

"The product pricing is moderate."

"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."

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

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

More Databricks pricing and cost advice

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

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

10%

Computer Software Company

Healthcare Company

Financial Services Firm

19%

Comms Service Provider

11%

Computer Software Company

Energy/Utilities Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
Midsize Enterprise	12
Large Enterprise	57

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	1
Large Enterprise	2

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?

In terms of pricing, Redpanda is free. We do not have to pay anything. It is not open source, but it is free.

See all answers

What needs improvement with Redpanda?

In Redpanda, the areas that have room for improvement are in the clustering part. Setting up clustering initially is very easy. However, if you are removing a node and attaching another node, signi...

See all answers

What is your primary use case for Redpanda?

Redpanda serves two primary purposes for our organization. First, we use it as a drop-in replacement for Kafka. Second, we utilize it for streaming analytics. We do not use Redpanda for IoT data st...

See all answers

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Snowflake vs Databricks

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Compared 16% of the time

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Compared 14% of the time

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Compared 11% of the time

Apache Flink vs Redpanda

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Product Reports

Buyer's Guide

Databricks

June 2026

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Buyer's Guide

Redpanda

June 2026

Download Redpanda product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Overview

Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

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

June 2026

Free Report: Databricks vs. Redpanda

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