Try our new research platform with insights from 80,000+ expert users

Databricks vs Google Cloud Datalab comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 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

Databricks
Ranking in Data Science Platforms
1st
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
88
Ranking in other categories
Cloud Data Warehouse (7th), Streaming Analytics (1st)
Google Cloud Datalab
Ranking in Data Science Platforms
16th
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
6
Ranking in other categories
Data Visualization (18th)
 

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Databricks is 18.2%, down from 19.1% compared to the previous year. The mindshare of Google Cloud Datalab is 0.9%, down from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

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.
Nilesh Gode - PeerSpot reviewer
Easy to setup, stable and easy to design data pipelines
The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.

Quotes from Members

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

Pros

"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"The processing capacity is tremendous in the database."
"The setup was straightforward."
"Databricks helps crunch petabytes of data in a very short period of time."
"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."
"The fast data loading process and data storage capabilities are great."
"I haven't heard about any major stability issues. At this time I feel like it's stable."
"The integration with Python and the notebooks really helps."
"All of the features of this product are quite good."
"The APIs are valuable."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"For me, it has been a stable product."
"Google Cloud Datalab is very customizable."
 

Cons

"As a data engineer, I see cluster failure in our Databricks user databases as a major issue."
"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."
"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."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"The API deployment and model deployment are not easy on the Databricks side."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."
"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."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."
"The interface should be more user-friendly."
"The product must be made more user-friendly."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
 

Pricing and Cost Advice

"Databricks uses a price-per-use model, where you can use as much compute as you need."
"There are different versions."
"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."
"We only pay for the Azure compute behind the solution."
"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
"The pricing depends on the usage itself."
"I'm not involved in the financing, but I can say that the solution seemed reasonably priced compared to the competitors. Similar products are usually in the same price range. With five being affordable and one being expensive, I would rate Databricks a four out of five."
"I would rate Databricks' pricing seven out of ten."
"The pricing is quite reasonable, and I would give it a rating of four out of ten."
"It is affordable for us because we have a limited number of users."
"The product is cheap."
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
848,716 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
Financial Services Firm
20%
University
12%
Computer Software Company
12%
Manufacturing Company
7%
 

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 ...
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...
What do you like most about Google Cloud Datalab?
Google Cloud Datalab is very customizable.
What needs improvement with Google Cloud Datalab?
Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcin...
What is your primary use case for Google Cloud Datalab?
It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...
 

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
No data available
 

Overview

 

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Information Not Available
Find out what your peers are saying about Databricks vs. Google Cloud Datalab and other solutions. Updated: April 2025.
848,716 professionals have used our research since 2012.