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BigQuery vs Databricks 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
8.6
Organizations saved costs and improved performance with BigQuery, achieving significant returns despite an initial learning period.
Sentiment score
6.6
Organizations experience mixed returns from Databricks, with benefits from cost savings and efficiency, but challenges in initial migration.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
 

Customer Service

Sentiment score
7.1
Google BigQuery support is generally reliable and agile but lacks direct engagement compared to competitors like Teradata.
Sentiment score
7.2
Databricks customer service is generally effective with prompt responses, though some report issues mainly with third-party support channels.
rating the customer support at ten points out of ten
Whenever we reach out, they respond promptly.
 

Scalability Issues

Sentiment score
7.9
BigQuery excels in scalability and performance for large operations but may be costly for smaller businesses.
Sentiment score
7.4
Databricks is praised for efficient scalability and cloud compatibility, allowing easy resource adjustment across diverse projects and industries.
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
 

Stability Issues

Sentiment score
8.5
BigQuery is praised for stability, reliability, and performance but has minor glitches with room for improvement in some areas.
Sentiment score
7.7
Databricks is stable and efficient for large data, with minor issues during updates and occasional connectivity challenges.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
 

Room For Improvement

BigQuery's drawbacks include special character restrictions, high pricing, integration issues, and needed improvements in user interface and support.
Databricks users desire improved UI, enhanced data visualization, better integration, clearer error messages, robust support, and comprehensive documentation.
In general, if I know SQL and start playing around, it will start making sense.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
It would be beneficial to have utilities where code snippets are readily available.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
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.
 

Setup Cost

BigQuery's pricing is flexible, based on usage, with low storage costs, and customizable to enterprise needs within Google Cloud.
Databricks pricing depends on usage, with flexibility in licensing, and can vary in competitiveness compared to other solutions.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
 

Valuable Features

BigQuery provides scalable, fast, cost-effective data analytics with seamless GCP integration and supports complex queries and various data types.
Databricks provides a unified platform for data engineering, machine learning, seamless cloud integration, and robust data management capabilities.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
Databricks' capability to process data in parallel enhances data processing speed.
The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster.
 

Categories and Ranking

BigQuery
Ranking in Cloud Data Warehouse
4th
Average Rating
8.2
Reviews Sentiment
7.3
Number of Reviews
40
Ranking in other categories
No ranking in other categories
Databricks
Ranking in Cloud Data Warehouse
7th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
88
Ranking in other categories
Data Science Platforms (1st), Streaming Analytics (1st)
 

Mindshare comparison

As of April 2025, in the Cloud Data Warehouse category, the mindshare of BigQuery is 7.0%, down from 8.0% compared to the previous year. The mindshare of Databricks is 8.4%, up from 3.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

VikashKumar1 - PeerSpot reviewer
Easy to maintain and provides high availability
Since I used BigQuery over the GCP cloud environment, I'm not sure whether we can go through internal IDEAs like IntelliJ or DBeaver that we use to connect with databases. Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process. Sometimes, we face some issues, bugs, and defects. We must first connect with a VPN to check data issues while working from home. Then, it allows you to connect to the cloud. After logging into the cloud, it searches for the service we are looking for, and then we go to BigQuery. This is a long process. After that, we analyze the issues in a table. Instead, it would be very helpful if it could provide a tool that we can install on our MacBook or Windows system. Once we open this tool, we can connect directly to the BigQuery server and easily perform tasks.
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.
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Top Industries

By visitors reading reviews
Computer Software Company
17%
Financial Services Firm
15%
Manufacturing Company
11%
Retailer
8%
Financial Services Firm
17%
Computer Software Company
11%
Manufacturing Company
9%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about BigQuery?
The initial setup process is easy.
What is your experience regarding pricing and costs for BigQuery?
The price is perceived as expensive, rated at eight out of ten in terms of costliness. Still, it offers significant cost savings.
What needs improvement with BigQuery?
When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming. In general, if I know SQL and start playing around, it will star...
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

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

Overview

 

Sample Customers

Information Not Available
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Find out what your peers are saying about BigQuery vs. Databricks and other solutions. Updated: March 2025.
845,849 professionals have used our research since 2012.