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Cloudera DataFlow vs Databricks comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

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

Cloudera DataFlow
Ranking in Streaming Analytics
15th
Average Rating
7.4
Reviews Sentiment
6.5
Number of Reviews
5
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
91
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 Cloudera DataFlow is 1.2%, down from 1.4% 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

Mohamed-Saied - PeerSpot reviewer
Efficient data integration and workflow scheduling elevate project performance
Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily for operational tasks, and it integrates well within Cloudera's ecosystem for high performance and…
ShubhamSharma7 - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The initial setup was not so difficult"
"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."
"DataFlow's performance is okay."
"The most effective features are data management and analytics."
"This solution is very scalable and robust."
"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 has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"The technical support is good."
"Databricks' capability to process data in parallel enhances data processing speed."
"Databricks serves as a single platform that can handle numerous end-to-end machine learning tasks."
"I work in the data science field and I found Databricks to be very useful."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
 

Cons

"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
"Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today."
"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."
"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"I would like more integration with SQL for using data in different workspaces."
"In the next release, I would like to see more optimization features."
"Pricing is one of the things that could be improved."
"Anyone who doesn't know SQL may find the product difficult to work with."
"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 more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."
 

Pricing and Cost Advice

"DataFlow isn't expensive, but its value for money isn't great."
"The pricing depends on the usage itself."
"The solution is a good value for batch processing and huge workloads."
"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."
"The solution is based on a licensing model."
"The cost is around $600,000 for 50 users."
"The billing of Databricks can be difficult and should improve."
"I would rate the tool’s pricing an eight out of ten."
"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."
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Top Industries

By visitors reading reviews
University
16%
Financial Services Firm
15%
Computer Software Company
13%
Retailer
7%
Financial Services Firm
18%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Cloudera DataFlow?
The most effective features are data management and analytics.
What needs improvement with Cloudera DataFlow?
Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today.
What is your primary use case for Cloudera DataFlow?
Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily...
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 ...
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...
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...
 

Comparisons

 

Also Known As

CDF, Hortonworks DataFlow, HDF
Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
 

Overview

 

Sample Customers

Clearsense
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Find out what your peers are saying about Cloudera DataFlow vs. Databricks and other solutions. Updated: June 2025.
861,390 professionals have used our research since 2012.