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BigQuery vs Vertica comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 18, 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

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
Vertica
Ranking in Cloud Data Warehouse
11th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
86
Ranking in other categories
Data Warehouse (5th)
 

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 Vertica is 5.6%, up from 5.2% 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.
T Venkatesh - PeerSpot reviewer
Processes query faster through multiple systems simultaneously, but it could support different data types
We use the solution for various tasks, including preparing data marts and generating offers. It helps extract data based on rules from the policy team and provides insights to enhance business operations. We also analyze transactions to target customers and improve business performance The…

Quotes from Members

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

Pros

"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."
"BigQuery's querying capabilities are very optimized for large datasets."
"The feature called calibrating the capacity is valuable."
"BigQuery excels at structuring data, performing predictions, and conducting insightful analyses and it leverages machine learning and artificial intelligence capabilities, powered by Google's Duarte AI."
"The initial setup is simple."
"The solution is very useful nowadays for keeping a huge number of records."
"BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases."
"Even non-coders can review the data in BigQuery."
"Its analytics has enabled Pythian's clients to get the business insights as quick as they wanted. Its lower maintenance has also improved the ROI."
"The hardware usage and speed has been the most valuable feature of this solution. It is very fast and has saved us a lot of money."
"Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data."
"Vertica is easy to use and provides really high performance, stability, and scalability."
"I don't need any special hardware. I can use commodity hardware, which is nice to have in a commercial solution."
"Partition and join back to node are easy and simple for DBAs."
"Eighty percent of the ETL operations have improved since implementing this solution."
"DBAs don’t need to add a partition every month/quarter like with other DBs."
 

Cons

"There are some limitations in the query latency compared to what it was three years ago."
"The product could benefit from improvements in user-friendliness, particularly in terms of the user interface."
"The price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them."
"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."
"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."
"Some of the queries are complex and difficult to understand."
"The product’s performance could be much faster."
"It can be slower and more problematic compared to other platforms such as Snowflake."
"One feature, which has really benefited us, is the scalability offered by Vertica as it has enabled Pythian's clients to manage data with agility."
"Pricing could be more competitive."
"I have found that coding support could be simplified."
"In our company, we have faced difficulties in scaling the solution for certain use cases."
"Support is an area where it could get better."
"It should provide a GUI interface for data management and tuning."
"The integration of this solution with ODI could be improved."
"Vertica can improve automation and documentation. Additionally, the solution can be simplified."
 

Pricing and Cost Advice

"The pricing appears to be competitive for the intended usage scenarios we have in mind."
"One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte, but BigQuery charges based on how much data is inserted into the tables. BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models are different and it becomes a key consideration in the decision of which platform to use."
"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."
"The price is a bit high but the technology is worth it."
"BigQuery pricing can increase quickly. It's a high-priced solution."
"The pricing is good and there are no additional costs involved."
"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."
"The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements."
"It's difficult today to compete with open-source solutions. In these areas, there is a lot of competition and the price of this solution is a bit pricy."
"The price of Vertica is less expensive than some competitors, such as Teradata."
"Start with license per 1TB. Starting from hundreds of TB there is unlimited licensing to be considered. Move historical data to HDFS/S3 which are significantly cheaper or even free."
"The price could be cheaper and it is best to negotiate the price."
"Work with a vendor, if possible, and take advantage of more aggressive discounts at mid-fiscal year (April) and fiscal year-end (October).​"
"The price is reasonable. We use a pay per license model. Firstly, you need to buy a license. After that, you mainly pay the annual support fee of around 20% or 25%. I think their prices are quite reasonable."
"I think it's starting to get a little expensive. Open source products are starting to get more robust, so I think that's something that they need to start looking at in terms of licensing."
"The first TB is free and you can use all the Vertica features. After 1TB you have to pay for licensing. The product is worth it, but be aware of this condition, and plan. The compression ratio is explained in the documentation."
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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
20%
Computer Software Company
18%
Manufacturing Company
8%
Real Estate/Law Firm
4%
 

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...
What do you like most about Vertica?
Vertica is easy to use and provides really high performance, stability, and scalability.
What is your experience regarding pricing and costs for Vertica?
The solution is relatively cost-effective. Pricing and licensing are reasonable compared to other solutions.
What needs improvement with Vertica?
The product could improve by adding support for a wider variety of data types and enhancing features to better compete with other databases.
 

Comparisons

 

Also Known As

No data available
Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand
 

Overview

 

Sample Customers

Information Not Available
Cerner, Game Show Network Game, Guess by Marciano, Supercell, Etsy, Nascar, Empirix, adMarketplace, and Cardlytics.
Find out what your peers are saying about BigQuery vs. Vertica and other solutions. Updated: March 2025.
844,944 professionals have used our research since 2012.