Confluent vs Databricks comparison

Confluent and Databricks are both solutions in the Streaming Analytics category. Confluent is ranked #4 with an average rating of 8.1, while Databricks is ranked #1 with an average rating of 8.3. Confluent holds a 8.5% mindshare in SA, compared to Databricks’s 14.6% mindshare. Additionally, 95% of Confluent users are willing to recommend the solution, compared to 96% of Databricks users who would recommend it.

Confluent

Read 23 Confluent reviews

4,827 Views
3,652 Comparison Views

95% willing to recommend

Databricks

Read 88 Databricks reviews

5,646 Views
4,053 Comparison Views

96% willing to recommend

Confluent

Databricks

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Executive SummaryUpdated on Dec 17, 2024

Databricks and Confluent are significant players in data analytics and real-time data streaming. Databricks holds an advantage in data manipulation and machine learning capabilities, while Confluent excels in scalability and real-time data streaming integration.

Features: Databricks offers large-scale analytics with built-in machine learning capabilities and Delta Lake for data engineering, alongside seamless integration with multiple programming languages. Confluent, based on Kafka, provides robust real-time data streaming, easy system integration, scalability, and adaptability, making it a strong competitor.

Room for Improvement: Databricks could improve in visualization capabilities, integration with third-party tools like Power BI, and offer better cost efficiency. Confluent could focus on developing intuitive plugins, broadening its range of managed connectors, and enhancing its user interfaces for improved user experiences and metric systems.

Ease of Deployment and Customer Service: Databricks is noted for its ease of deployment across cloud environments and good customer support, although some users experience delays. Confluent provides robust cloud support, positive customer service feedback, and detailed documentation that simplifies deployment.

Pricing and ROI: Databricks uses a pay-per-use model, which can be expensive, but it justifies the cost with its comprehensive toolset, showing ROI in batch processing environments. Confluent's pricing is competitive relative to its streaming capabilities, providing value through configurability and scalability that translates into reasonable ROI for robust data streaming needs.

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

Buyer's Guide

Confluent vs. Databricks

March 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

Confluent

Ranking in Streaming Analytics

4th

Average Rating

8.2

Reviews Sentiment

6.7

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

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Confluent is 8.5%, down from 11.4% compared to the previous year. The mindshare of Databricks is 14.6%, up from 10.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Yantao Zhao

Software Integration Engineer at Thales

Great tool for sharing knowledge, internal communication and allows for real-time collaboration on pages

Confluence is easy to use and modify. However, sometimes there are too many pages. We have to reorganize the folder or parent account. Since everyone can create a page, the same knowledge might be created in multiple places by different people. This leads to redundancy and makes it difficult to find information. It's not centralized. So it could be more user-friendly and centralized. A way to reduce redundancy would be helpful. It's very easy to use, so everyone can create knowledge. But it would be good to synchronize and organize that information a bit better. Another improvement would be in Confluence search. You can search for keywords, but it's not like AI, not even ChatGPT or OpenAI. It would be nice to get more relevant or organized answers. If you're outside the company, you just get some titles containing the keyword you input. But if Confluence were like a database, you could input something and get a well-organized search offering from multiple pages.

Read full review

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.

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

"With Confluent Cloud we no longer need to handle the infrastructure and the plumbing, which is a concern for Confluent. The other advantage is that all portfolios have access to the data that is being shared."

"The solution can handle a high volume of data because it works and scales well."

"The design of the product is extremely well built and it is highly configurable."

"I would rate the scalability of the solution at eight out of ten. We have 20 people who use Confluent in our organization now, and we hope to increase usage in the future."

"The most valuable feature that we are using is the data replication between the data centers allowing us to configure a disaster recovery or software. However, is it's not mandatory to use and because most of the features that we use are from Apache Kafka, such as end-to-end encryption. Internally, we can develop our own kind of product or service from Apache Kafka."

"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."

"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."

"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."

More Confluent pros

"The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle."

"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."

"The most valuable feature of Databricks is the integration with Microsoft Azure."

"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."

"The load distribution capabilities are good, and you can perform data processing tasks very quickly."

"Databricks offers various courses that I can use, whether it's PySpark, Scala, or R."

"The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient."

"Databricks serves as a single platform for conducting the entire end-to-end lifecycle of machine learning models or AI ops."

More Databricks pros

Cons

"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."

"It would help if the knowledge based documents in the support portal could be available for public use as well."

"It could be improved by including a feature that automatically creates a new topic and puts failed messages."

"It requires some application specific connectors which are lacking. This needs to be added."

"It could have more integration with different platforms."

"In Confluent, there could be a few more VPN options."

"It could have more themes. They should also have more reporting-oriented plugins as well. It would be great to have free custom reports that can be dispatched directly from Jira."

"The Schema Registry service could be improved. I would like a bigger knowledge base of other use cases and more technical forums. It would be good to have more flexible monitoring features added to the next release as well."

More Confluent cons

"The product needs samples and templates to help invite users to see results and understand what the product can do."

"The integration of data could be a bit better."

"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."

"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."

"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."

"It should have more compatible and more advanced visualization and machine learning libraries."

"The integration features could be more interesting, more involved."

"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

Pricing and Cost Advice

"Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenance complexity justify the cost, especially as we scale our platform use."

"Confluence's pricing is quite reasonable, with a cost of around $10 per user that decreases as the number of users increases. Additionally, it's worth noting that for teams of up to 10 users, the solution is completely free."

"Confluent has a yearly license, which is a bit high because it's on a per-user basis."

"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."

"Confluent is highly priced."

"The pricing model of Confluent could improve because if you have a classic use case where you're going to use all the features there is no plan to reduce the features. You should be able to pick and choose basic services at a reduced price. The pricing was high for our needs. We should not have to pay for features we do not use."

"You have to pay additional for one or two features."

"It comes with a high cost."

More Confluent pricing and cost advice

"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."

"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."

"The solution is affordable."

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

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

"The cost is around $600,000 for 50 users."

"I would rate Databricks' pricing seven out of ten."

"I would rate the tool’s pricing an eight out of ten."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

16%

Manufacturing Company

Insurance Company

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Confluent?

I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and to...

See all answers

What is your experience regarding pricing and costs for Confluent?

They charge a lot for scaling, which makes it expensive.

See all answers

What needs improvement with Confluent?

I am not very impressed by Confluent. We continuously face issues, such as Kafka being down and slow responses from the support team. The lack of easy access to the Confluent support team is also a...

See all answers

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

Comparisons

Amazon Kinesis vs Confluent

Compared 17% of the time

Amazon MSK vs Confluent

Compared 11% of the time

AWS Glue vs Confluent

Compared 4% of the time

Cribl vs Confluent

Compared 4% of the time

Fivetran vs Confluent

Compared 4% of the time

More Confluent Competitors

Dataiku vs Databricks

Compared 11% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

More Databricks Competitors

Product Reports

Buyer's Guide

Confluent

March 2025

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Buyer's Guide

Databricks

March 2025

Download Databricks product report

Also Known As

No data available

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Overview

Confluent is an enterprise-ready, full-scale streaming platform that enhances Apache Kafka.

Confluent has integrated cutting-edge features that are designed to enhance these tasks:

Speed up application development and connectivity
Enable transformations through stream processing
Streamline business operations at scale
Adhere to strict architectural standards

Confluent is a more complete distribution of Kafka in that it enhances the integration possibilities of Kafka by introducing tools for managing and optimizing Kafka clusters while providing methods for making sure the streams are secure. Confluent supports publish-and-subscribe as well as the storing and processing of data within the streams. Kafka is easier to operate and build thanks to Confluent.

Confluent's software is available in three different varieties:

A free, open-source streaming platform that makes it simple to start using real-time data streams
An enterprise-grade version of the product with more administrative, ops, and monitoring tools
A premium cloud-based version.

Confluent Advantage Features

Confluent has many valuable key features. Some of the most useful ones include:

Multi-language
- Clients: C++, Python, Go, and .NET
- REST proxy: Can connect to Kafka from any connected network device
- Admin REST APIs: RESTful interface for performing administrator operations

Pre-built ecosystem
- Connectors: More than 100 supported connectors, including S3, Elastic, HDFS, JDBC
- MQTT proxy: Gain access to Kafka from MQTT gateways and devices
- Schema registry: Centralized database to guarantee data compatibility

Streaming database
- ksqlDB: Materialized views and real-time stream processing

GUI management
- Control panel: GUI for scalable Kafka management and monitoring
- Health+: Smart alerts and cloud-based control centers

DevOps automation that is flexible
- Confluent for Kubernetes: Complete API to deploy on Kubernetes
- Automated Ansible deployment on non-containerized environments

Dynamic performance
- Self-balancing clusters: Automated partition re-balancing across brokers in the cluster
- Tiered storage: Older Kafka data offloading to object storage with transparent access

Security that is enterprise-grade
- Role-based access control: Granular user/group access authorization
- Audit logs that are structured: Logs of user actions kept in dedicated Kafka topics
- Secret protection: Sensitive information is encrypted

Global resilience
- Linking clusters: A real-time, highly reliable, and consistent bridge across on-premises and cloud environments
- Multiple-region clusters: Single Kafka cluster with automated client failover distributed across multiple data centers
- Replicator: Asynchronous replication that is based on the Kafka Connect framework

Support
- Round the clock enterprise support from Kafka experts

Reviews from Real Users

Confluent stands out among its competitors for a number of reasons. Two major ones are its robust enterprise support and its open source option. PeerSpot users take note of the advantages of these features in their reviews:

Ravi B., a solutions architect at a tech services company, writes of the solution, “KSQL is a valuable feature, as is the Kafka Connect framework for connecting to the various source systems where you need not write the code. We get great support from Confluent because we're using the enterprise version and whenever there's a problem, they support us with fine-tuning and finding the root cause.”

Amit S., an IT consultant, notes, “The biggest benefit is that it is open source. You have the flexibility of opting or not opting for enterprise support, even though the tool itself is open source.” He adds, “The second benefit is it's very modern and built on Java and Scala. You can extend the features very well, and it doesn't take a lot of effort to do so.”

Confluent

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

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost

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

Buyer's Guide

Confluent vs. Databricks

March 2025

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