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Databricks vs Looker comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

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

ROI

Sentiment score
6.5
Databricks reduces costs and boosts efficiency, yet some users struggle to realize financial gains despite improved productivity.
Sentiment score
7.4
Looker provides large organizations with efficiency and savings, while smaller companies benefit from integration and automation improvements.
This reduction in both time and money resulted in real-time impact and significant cost savings.
Consultant at Nice Software Solutions
For a lot of different tasks, including machine learning, it is a nice solution.
Senior Data Engineer at a logistics company with 51-200 employees
When it comes to big data processing, I prefer Databricks over other solutions.
Head CEO at bizmetric
The number of employees has reduced to half, and time is saved by nearly two to three hours a day because of automated reports.
Data Engineer at a tech vendor with 10,001+ employees
 

Customer Service

Sentiment score
6.9
Databricks support is professional and responsive, with users appreciating efficient issue resolution and effective assistance despite occasional delays.
Sentiment score
6.2
Looker's support is generally praised for responsiveness and community help, but complex issues and weekends need improvement.
Whenever we reach out, they respond promptly.
Senior Data Engineer at a logistics company with 51-200 employees
As of now, we are raising issues and they are providing solutions without any problems.
Data Platform Architect at KELLANOVA
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
Data Engineer at CRAFT Tech
Anyone with minimal knowledge of Looker can easily go through the documentation and understand the steps and the UI.
Data Engineer at a tech vendor with 10,001+ employees
 

Scalability Issues

Sentiment score
7.4
Databricks is praised for scalable, cost-effective cloud compatibility, efficient data handling, and seamless integration with Azure and AWS.
Sentiment score
6.8
Looker is praised for scalability, especially with Amazon Redshift, though large deployments require careful data management for optimal performance.
The sky's the limit with Databricks.
Governance And Engagement Lead
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Senior Data Engineer at a logistics company with 51-200 employees
Databricks is an easily scalable platform.
Data Platform Architect at KELLANOVA
I rate Looker's scalability a nine because of how easily we can scale by simply combining tables.
Data Engineer at a tech vendor with 10,001+ employees
 

Stability Issues

Sentiment score
7.6
Databricks is generally stable and reliable, with occasional glitches, handling large data sets effectively according to users.
Sentiment score
7.4
Looker is reliable and cloud-based, integrates with BigQuery, handles large data well despite minor bugs and setup challenges.
They release patches that sometimes break our code.
Senior Data Engineer at a logistics company with 51-200 employees
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
Data Platform Architect at KELLANOVA
Databricks is definitely a very stable product and reliable.
Data Engineer at a tech vendor with 1,001-5,000 employees
 

Room For Improvement

Databricks requires better visualization, integration, pricing, user experience, scalability, and documentation to enhance functionality and user adaptation.
Looker's customization, integration, and performance lag behind competitors, with users seeking more features, transparency, and training resources.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
Data Engineer at a engineering company with 1,001-5,000 employees
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
Senior Data Engineer at a logistics company with 51-200 employees
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
Solution Architect at Mercedes-Benz AG
The performance is not optimal on large data sets.
Data Engineer at a tech vendor with 10,001+ employees
 

Setup Cost

Databricks offers competitive, flexible pay-per-use pricing, but costs vary by usage, often higher than open-source alternatives.
Looker's pricing suits medium to large enterprises but may be costly for small businesses with additional expenses.
It is not a cheap solution.
Data Platform Architect at KELLANOVA
I believe that in terms of credits for Databricks, we're spending between £15,000 and £20,000 a month.
Governance And Engagement Lead
 

Valuable Features

Databricks offers scalable analytics with powerful machine learning, seamless cloud integration, and efficient data governance for rapid data processing.
Looker enhances data-driven decisions with intuitive modeling, robust integrations, and tools for advanced analytics without extensive SQL.
Databricks' capability to process data in parallel enhances data processing speed.
Data Engineer at a engineering company with 1,001-5,000 employees
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
Data Platform Architect at KELLANOVA
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
Data Engineer at CRAFT Tech
For a business user to understand data from free form or Essbase, it usually takes thirty minutes to an hour to go through the previous day's trend. However, with the visualization tools in Looker, they can easily understand the data and compare it to the previous one or two years using a bar graph, pie chart, or other graphs.
Data Engineer at a tech vendor with 10,001+ employees
 

Categories and Ranking

Databricks
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
93
Ranking in other categories
Cloud Data Warehouse (5th), Data Science Platforms (1st), Data Management Platforms (DMP) (5th), Streaming Analytics (1st)
Looker
Average Rating
8.0
Reviews Sentiment
6.4
Number of Reviews
20
Ranking in other categories
Embedded BI (9th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Databricks is designed for Cloud Data Warehouse and holds a mindshare of 10.2%, up 8.5% compared to last year.
Looker, on the other hand, focuses on Embedded BI, holds 5.6% mindshare, down 9.2% since last year.
Cloud Data Warehouse Mindshare Distribution
ProductMindshare (%)
Databricks10.2%
Snowflake15.2%
Teradata8.3%
Other66.3%
Cloud Data Warehouse
Embedded BI Mindshare Distribution
ProductMindshare (%)
Looker5.6%
Tableau Enterprise16.9%
Qlik Sense8.0%
Other69.5%
Embedded BI
 

Featured Reviews

SimonRobinson - PeerSpot reviewer
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.
Hemanthreddy Vakiti - PeerSpot reviewer
Data Engineer at a tech vendor with 10,001+ employees
Automated dashboards have transformed daily trend analysis and now drive faster business decisions
One of the best features that Looker offers is LookML, which allows us to develop dashboards directly rather than dragging and dropping. We can also customize some fields that are not available in the table by combining different tables using LookML. Compared to other reporting tools, Looker is somewhat more customizable and interactive. With the help of Views and Explore in Looker, we can combine different tables and create a unique column that is not available in the table, and then use that column in our reporting dashboard. Since we brought Looker into our project, it has improved data visibility and enabled faster interaction between us and our client. Rather than simply using free form or Essbase, seeing customizable and visualized data in Looker allows the business users to understand the trends in the data more easily rather than just seeing numbers. It has helped us save a lot of time. The automated dashboards that we develop allow us to run the dashboard for the coming years with only some modifications, which has reduced some manpower and working hours. When I joined this project, there were around eight to nine people for developing Looker dashboards. Once the development part is completed for almost all cases, the next part is just monitoring the dashboards and making some minor changes required to align with business goals. After development, the team was reduced to three or four people, so the manpower has significantly reduced, and time taken has decreased as well. For a business user to understand data from free form or Essbase, it usually takes thirty minutes to an hour to go through the previous day's trend. However, with the visualization tools in Looker, they can easily understand the data and compare it to the previous one or two years using a bar graph, pie chart, or other graphs. The return on investment has been very good. The number of employees has reduced to half, and time is saved by nearly two to three hours a day because of automated reports. We are just monitoring the reports to ensure they are released. This has saved nearly two hours per day, and the team has been reduced to half.
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Manufacturing Company
9%
Computer Software Company
8%
Healthcare Company
6%
Financial Services Firm
14%
Retailer
9%
Media Company
7%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business27
Midsize Enterprise12
Large Enterprise56
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise8
Large Enterprise7
 

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...
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Comparisons

 

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
Yahoo!, Etsy, Kohler, Hipcamp, Hubspot, Kickstarter, Venmo, Dollar Shave Club, 600+ customer
Find out what your peers are saying about Snowflake Computing, Teradata, Google and others in Cloud Data Warehouse. Updated: March 2026.
886,349 professionals have used our research since 2012.