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Databricks vs Looker Studio 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:
 

Categories and Ranking

Databricks
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
91
Ranking in other categories
Cloud Data Warehouse (9th), Data Science Platforms (1st), Streaming Analytics (1st)
Looker Studio
Average Rating
7.8
Reviews Sentiment
6.6
Number of Reviews
14
Ranking in other categories
Reporting (8th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Databricks is designed for Cloud Data Warehouse and holds a mindshare of 8.3%, up 5.6% compared to last year.
Looker Studio, on the other hand, focuses on Reporting, holds 3.0% mindshare, up 2.4% since last year.
Cloud Data Warehouse Market Share Distribution
ProductMarket Share (%)
Databricks8.3%
Snowflake17.7%
Dremio9.4%
Other64.6%
Cloud Data Warehouse
Reporting Market Share Distribution
ProductMarket Share (%)
Looker Studio3.0%
Microsoft Power BI24.3%
Tableau Enterprise20.6%
Other52.099999999999994%
Reporting
 

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.
Vijay Subramanyam - PeerSpot reviewer
The ability to perform aggregation before visualization allows for effective data slicing and dicing
Looker's ability to perform aggregation before visualization allows for effective data slicing and dicing. The seamless integration with Excel and Google Sheets and custom filter creation features facilitate detailed trend reports and analysis. Looker has significantly improved productivity by reducing the time spent on creating summary reports from weeks to a day.

Quotes from Members

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

Pros

"Databricks' most valuable feature is the data transformation through PySpark."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"Databricks has a Unified Catalog that assists with secured access and governance."
"I would rate them ten out of ten."
"The solution is very easy to use."
"It's very simple to use Databricks Apache Spark."
"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."
"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."
"The solution is free so that is a good feature."
"The integration with Excel and Google Sheets is a valuable feature, making it easy to access and visualize data."
"The company dashboard is useful because we could share it via a link as a reminder for everyone to check it weekly. We observed the progress of our portfolio from last week to the current week, allowing us to compare revenues."
"I am impressed with the tool's scheduling mechanism, refresh mechanism, and different types of charts."
"The best thing about the tool is that it allows you to share information easily and dashboards with colleagues and teammates because it's all online. No matter where you are, as long as you have the Internet, you can connect and use these dashboards. Google made good documentation for this tool. There are many examples of how to use and create dashboards and videos on YouTube. So, I think it's easy to learn."
"The ability to design complex data models and equations."
"The ability to integrate with a great variety of data sources."
"Valuable features include advanced integrated analysis and easy implementation."
 

Cons

"Performance could be improved."
"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."
"The Databricks cluster can be improved."
"Implementation of Databricks is still very code heavy."
"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."
"Databricks can improve by making the documentation better."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"There is room for improvement in the documentation of processes and how it works."
"Insisting on forums, blogs and community outreach in communications, and posting videos on an established calendar would be useful."
"The tool should come up with data modeling layer features that are present in other products like Power BI."
"The tool should improve on live data integration for quicker data reflection."
"Other tools might be worth considering if you need more advanced features or support for a larger user base."
"There are issues with integration and I encountered limits and warnings, especially with my pivot table size."
"The performance of Looker needs improvement, particularly the report loading time, which is critical for business users."
"There is a significant degree of sophistication required to compete with Tableau or Cognos."
"Panels are not as easy to use as other data extraction UIs."
 

Pricing and Cost Advice

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
"The solution requires a subscription."
"The pricing depends on the usage itself."
"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."
"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."
"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."
"The solution is based on a licensing model."
"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."
"The product is available for free."
"The solution is free but the Google Looker is expensive."
"The cost is quite affordable based on feature analysis."
"The tool is free."
"I think the tool is cheap because you don't need to pay anything for using the tool. However, when connecting and analyzing other data sources, you should consider where to analyze this information because Google Data Studio doesn't handle much data well. You must also consider how you will connect this data to it. In this case, you might need to hire DevOps or a data team to help with these issues."
report
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
Computer Software Company
12%
Financial Services Firm
11%
Manufacturing Company
10%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business25
Midsize Enterprise12
Large Enterprise56
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise3
Large Enterprise5
 

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 Data Studio?
The company dashboard is useful because we could share it via a link as a reminder for everyone to check it weekly. We observed the progress of our portfolio from last week to the current week, all...
What needs improvement with Google Data Studio?
We are aware that there are areas in Looker Studio that could be improved. Out of the box, if you don't utilize a template or create a template, which can take significant time, it's not the most u...
What is your primary use case for Google Data Studio?
Regarding the product, I was doing research for a client in the LMS industry. I was seeing what else was out there and why certain things were pulling for certain reasons. I wouldn't say I'm techni...
 

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
Data Studio
 

Overview

 

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Genesys, Shueisha
Find out what your peers are saying about Snowflake Computing, Microsoft, Google and others in Cloud Data Warehouse. Updated: August 2025.
867,349 professionals have used our research since 2012.