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BigQuery vs Vertica comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

BigQuery
Ranking in Cloud Data Warehouse
5th
Average Rating
8.2
Reviews Sentiment
7.6
Number of Reviews
35
Ranking in other categories
No ranking in other categories
Vertica
Ranking in Cloud Data Warehouse
7th
Average Rating
8.2
Reviews Sentiment
7.6
Number of Reviews
86
Ranking in other categories
Data Warehouse (4th)
 

Mindshare comparison

As of November 2024, in the Cloud Data Warehouse category, the mindshare of BigQuery is 9.3%, up from 6.7% compared to the previous year. The mindshare of Vertica is 6.2%, up from 5.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse
 

Featured Reviews

Sathishkumar Jayaprakash - PeerSpot reviewer
Efficient large dataset handling with seamless service integration
BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases. It integrates well with other GCP products, and creating subscriptions in the UI is straightforward. The whole ecosystem of GCP products makes BigQuery beneficial for our data-handling tasks. Additionally, it is more cost-effective compared to alternatives like AWS.
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

"The query tool is scalable and allows for petabytes of data."
"It's straightforward to set up."
"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."
"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."
"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."
"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."
"The product's initial setup phase is extremely simple."
"Speed and resiliency are probably the best parts of this product."
"Integrated R and geospatial functions are helping us improve efficiency and explore new revenue streams. ​"
"For me, It's performance, scalability, low cost, and it's integrated into enterprise and big data environments."
"Partition and join back to node are easy and simple for DBAs."
"Vertica is easy to use and provides really high performance, stability, and scalability."
"It has improved my organization's functionality and performance."
"It maximizes cloud economics with Eon Mode by scaling cluster size to meet variable workload demands."
 

Cons

"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."
"The solution should reduce its pricing."
"The solution hinges on Google patterns so continued improvement is important."
"There are some limitations in the query latency compared to what it was three years ago."
"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."
"The processing capability can be an area of improvement."
"There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."
"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."
"Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint."
"Vertica's native cloud support could be improved, and its installation could be made easier."
"Vertica can improve automation and documentation. Additionally, the solution can be simplified."
"Pricing could be more competitive."
"Monitoring tools need to be lightweight. They should not take up heavy resources of the main server."
"Performance of management of metadata layer (database catalog) needs improvement. We still have to have smaller customers on PostgreSQL; Vertica cannot manage thousands of schemata."
"We are looking for a cheaper deployment for the solution. Although we did a lot of benchmarks, like Redshift. We tried Redshift, it didn't work. It didn't work out for us as well."
"In a future release, we would like to have artificial intelligence capabilities like neural networks. Customers are demanding this type of analytics."
 

Pricing and Cost Advice

"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 product’s pricing could be more flexible for end users."
"The pricing is good and there are no additional costs involved."
"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 pricing appears to be competitive for the intended usage scenarios we have in mind."
"The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera."
"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."
"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."
"It's free up to three nodes and 1TB, and then get in contact with their sales guys."
"The pricing for this solution is very reasonable compared to other vendors."
"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."
"It is fast to purchase through the AWS Marketplace."
"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."
"From a cost perspective, the software is less than most of its competitors."
"The price could be cheaper and it is best to negotiate the price."
"The pricing depends on the license model because there are several. It depends on the client, but it's cheaper than other solutions. I think it's cheap for all the functionality and robustness. It's not very expensive to deploy."
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Top Industries

By visitors reading reviews
Computer Software Company
16%
Financial Services Firm
14%
Manufacturing Company
12%
Retailer
7%
Computer Software Company
18%
Financial Services Firm
18%
Manufacturing Company
8%
University
5%
 

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 product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity.
What needs improvement with BigQuery?
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 dir...
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
 

Learn More

 

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: October 2024.
816,406 professionals have used our research since 2012.