Apache Pulsar vs Databricks comparison

Pulsar and Databricks are both solutions in the Streaming Analytics category. Pulsar is ranked #19, while Databricks is ranked #1 with an average rating of 8.3. Pulsar holds a 2.1% mindshare in SA, compared to Databricks’s 14.2% mindshare. Additionally, 100% of Pulsar users are willing to recommend the solution, compared to 96% of Databricks users who would recommend it.

Apache Pulsar

Read 1 Apache Pulsar review

843 Views
843 Comparison Views

100% willing to recommend

Databricks

Read 91 Databricks reviews

25,542 Views
4,598 Comparison Views

96% willing to recommend

Apache Pulsar

Databricks

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Executive Summary

We performed a comparison between Apache Pulsar and Databricks based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics.

To learn more, read our detailed Streaming Analytics Report (Updated: July 2025).

Buyer's Guide

Streaming Analytics

July 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

Apache Pulsar

Ranking in Streaming Analytics

19th

Average Rating

8.0

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

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

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.1%, up from 1.9% compared to the previous year. The mindshare of Databricks is 14.2%, up from 11.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

it_user1087029

Solution Architect at Vlaanderen connect.

The solution can mimic other APIs without changing a line of code

The solution operates as a classic message broker but also as a streaming platform. It operates differently than a traditional streaming platform with storage and computing handled separately. It scales easier and better than Kafka which can be stubborn. You can even make it act like Kafka because it understands Kafka APIs. There are even companies that will sell you Kafka but underneath it is Apache Pulsar. The solution is very compatible because it can mimic other APIs without changing a line of code.

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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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Quotes from Members

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

Pros

"The solution operates as a classic message broker but also as a streaming platform."

"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."

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

"We have the ability to scale, collaborate and do machine learning."

"There are good features for turning off clusters."

"The technical support is good."

"The initial setup is pretty easy."

"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."

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

More Databricks pros

Cons

"Documentation is poor because much of it is in Chinese with no English translation."

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

"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."

"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."

"I believe that this product could be improved by becoming more user-friendly."

"It's not easy to use, and they need a better UI."

"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."

"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."

"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."

More Databricks cons

Pricing and Cost Advice

Information not available

"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."

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

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

"The solution requires a subscription."

"There are different versions."

"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 is affordable."

"The solution is based on a licensing model."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

26%

Financial Services Firm

10%

Retailer

Manufacturing Company

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

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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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Comparisons

Apache Flink vs Apache Pulsar

Compared 38% of the time

Amazon MSK vs Apache Pulsar

Compared 21% of the time

Apache Spark Streaming vs Apache Pulsar

Compared 18% of the time

Redpanda vs Apache Pulsar

Compared 15% of the time

Amazon Kinesis vs Apache Pulsar

Compared 8% of the time

More Apache Pulsar Competitors

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 6% of the time

Tableau vs Databricks

Compared 5% of the time

More Databricks Competitors

Product Reports

Buyer's Guide

Streaming Analytics

July 2025

Download Apache Pulsar product report

Buyer's Guide

Databricks

July 2025

Download Databricks product report

Also Known As

No data available

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Overview

Apache Pulsar is a cloud-native, distributed messaging and streaming platform originally created at Yahoo! and now a top-level Apache Software Foundation project

Pulsar

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

Sample Customers

Information Not Available

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

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics. Updated: July 2025.

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