BigQuery vs Vertica comparison

Google and OpenText are both solutions in the Cloud Data Warehouse category. Google is ranked #4 with an average rating of 8.3, while OpenText is ranked #10 with an average rating of 8.1. Google holds a 7.3% mindshare in CDW, compared to OpenText’s 5.4% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 91% of OpenText users who would recommend it.

BigQuery

Read 40 BigQuery reviews

3,615 Views
2,899 Comparison Views

100% willing to recommend

Vertica

Read 86 Vertica reviews

2,551 Views
1,954 Comparison Views

91% willing to recommend

BigQuery

Vertica

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Executive SummaryUpdated on Dec 18, 2024

Vertica and BigQuery are popular competitors in the data analytics and storage sector. BigQuery's serverless architecture and cost model give it an edge in scalability and integration with Google services.

Features: Vertica offers scalable, fast query performance, supporting SQL and real-time data analysis with advanced data storage through projections. Its strong suit is concurrent query processing and data compression. BigQuery, known for its serverless architecture, facilitates easy scaling with a pay-per-use model and integrates seamlessly with other Google services. It supports large-scale data analysis using SQL.

Room for Improvement: Vertica needs enhancements in workload management and optimization for diverse storage, with a need for better documentation and developer tool support. BigQuery requires better handling of special characters, improved query caching for external tables, and regional data compliance. It also requires improvements in cost optimization and migration support from other systems.

Ease of Deployment and Customer Service: Vertica offers deployment flexibility with on-premises and hybrid cloud options, though its technical support feedback is inconsistent. BigQuery's focus is on public cloud deployment, appealing to cloud-native users, but customer support experiences vary, particularly in resolving complex issues.

Pricing and ROI: Vertica employs a data size-based pricing model with flexible options, appreciated for providing a strong ROI, especially in extensive analytical processes. BigQuery's pay-as-you-go model is cost-effective for scaling, but costs may rise if not managed properly, suggesting a need for better enterprise-level cost management.

To learn more, read our detailed BigQuery vs. Vertica Report (Updated: March 2025).

Buyer's Guide

BigQuery vs. Vertica

March 2025

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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

Ranking in other categories

No ranking in other categories

Vertica

Ranking in Cloud Data Warehouse

10th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Warehouse (5th)

Mindshare comparison

As of March 2025, in the Cloud Data Warehouse category, the mindshare of BigQuery is 7.3%, down from 7.9% compared to the previous year. The mindshare of Vertica is 5.4%, up from 5.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse

Featured Reviews

VikashKumar1

Tech lead at Ford Motor Company

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.

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T Venkatesh

Deputy Manager at ICICIBANK Ltd

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…

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Quotes from Members

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

Pros

"BigQuery's querying capabilities are very optimized for large datasets."

"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."

"The most valuable features of BigQuery is that it supports standard SQL and provides good performance."

"BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes."

"The setup is simple."

"The query tool is scalable and allows for petabytes of data."

"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."

"BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases."

More BigQuery pros

"It has improved my organization's functionality and performance."

"Any novice user can tune vertical queries with minimal training (or no training at all)."

"Vertica has a few features that I like. From an architecture standpoint, they have separated compute and storage. So you have low-cost object storage for primary storage and the ability to have several sub-clusters working off the same ObjectStore. So it provides workload isolation."

"Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data."

"The solution is quick, has good compression data, and is not expensive."

"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."

"The initial setup was straightforward."

"It maximize cloud economics for mission-critical big data analytical initiatives."

More Vertica pros

Cons

"Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process."

"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."

"It can be slower and more problematic compared to other platforms such as Snowflake."

"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

"The product’s performance could be much faster."

"There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard."

"The solution should reduce its pricing."

"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."

More BigQuery cons

"I believe the installation process could be streamlined."

"Vertica can improve automation and documentation. Additionally, the solution can be simplified."

"Vertica offers a platform-as-a-service version, but their software-as-a-service solution is only available on AWS. They need to get a SaaS version on Azure and GCP as fast as possible."

"If you do not utilize the tuning tools like projections, encoding, partitions, and statistics, then performance and scalability will suffer."

"The integration with AI has room for improvement."

"Very bad support, I would rate it two out of 10."

"The integration of this solution with ODI could be improved."

"In our company, we have faced difficulties in scaling the solution for certain use cases."

More Vertica cons

Pricing and Cost Advice

"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."

"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."

"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."

"BigQuery pricing can increase quickly. It's a high-priced solution."

"The pricing appears to be competitive for the intended usage scenarios we have in mind."

"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 product’s pricing could be more flexible for end users."

"The pricing is good and there are no additional costs involved."

More BigQuery pricing and cost advice

"The pricing could improve, it is a little expensive."

"It's an expensive product"

"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."

"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."

"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's free up to three nodes and 1TB, and then get in contact with their sales guys."

"The solution is free and we pay for the storage."

"It is fast to purchase through the AWS Marketplace."

More Vertica pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

17%

Financial Services Firm

14%

Manufacturing Company

11%

Retailer

Financial Services Firm

19%

Computer Software Company

18%

Manufacturing Company

University

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.

See all answers

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.

See all answers

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...

See all answers

What do you like most about Vertica?

Vertica is easy to use and provides really high performance, stability, and scalability.

See all answers

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.

See all answers

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.

See all answers

Comparisons

Snowflake vs BigQuery

Compared 30% of the time

Oracle Autonomous Data Warehouse vs BigQuery

Compared 16% of the time

Teradata vs BigQuery

Compared 14% of the time

Apache Hadoop vs BigQuery

Compared 7% of the time

Oracle Exadata vs BigQuery

Compared 7% of the time

More BigQuery Competitors

SQL Server vs Vertica

Compared 13% of the time

Teradata vs Vertica

Compared 12% of the time

Snowflake vs Vertica

Compared 12% of the time

Oracle Exadata vs Vertica

Compared 8% of the time

Amazon Redshift vs Vertica

Compared 7% of the time

More Vertica Competitors

Product Reports

Buyer's Guide

BigQuery

March 2025

Download BigQuery product report

Buyer's Guide

Vertica

March 2025

Download Vertica product report

Also Known As

No data available

Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand

Overview

BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

Google

Vertica is a deploy-anywhere SQL database created for elasticity, speed, and advanced analytics. Vertica enables today’s busy teams to modernize their data warehouses, democratize data and analytics to enable increased access, and deploy analytics in a hybrid cloud environment. Additionally, Vertica merges how companies power their analytics by providing a scalable, open, and elastic database with numerous intuitive features.

In today’s marketplace, organizations are experiencing continued robust growth of data volumes, and citizen data scientists’ broader use of analytics is causing many companies to re-visit and re-examine their systems in order to match the demands of an aggressive marketplace. Analytics are continually swiftly evolving. New data from social media, blogs, IoT sources, data streams, gas and electrical grids, and mobile networks is being constantly gathered in extensive data sets. This presents organizations with a new opportunity to become more data driven, and they must be able to manage the new data growth and identify the trends and sequences that can lead to both improved business opportunities and continued repeat business from their clients.

Vertica Benefits:

Vertica has many valuable key benefits. Some of its most useful benefits include:

Efficiency: Vertica provides robust compression and intuitive impressions. This results in users requiring significantly less storage and hardware than other comparable data analytics solutions. The progressive Vertica architecture results in queries that are 10-50 times faster than other platforms while providing more storage data per server.
Integration: Each new iteration of Vertica is tested and certified with the latest ETL and visualization tools. It actively supports Java Database Connectivity (JDBC), Open Database Connectivity (ODBC), and popular SQL providers. All these solutions and most leading BI and visualization tools interact seamlessly, making Vertica overall a very cost-effective solution and solid business investment.
Cloud flexibility: With Vertica, users do not have to get locked into a single cloud vendor. Users are able to take complete advantage of the current infrastructure that is already in place. Vertica seamlessly integrates with popular public clouds, including Google Cloud Platform (GCP), Azure, AWS, Alibaba, VMware clouds, and more. It also provides for easy portability across on-premise and multi-cloud environments and data lakes. Vertica designs a robust flexible platform for running a company’s analytical and computing workloads, which allows applications to run simultaneously on numerous environments in a hybrid cloud infrastructure. Vertica is able to seamlessly use public clouds and private data centers, and it grants the flexibility to switch in an instant.
Security: Vertica offers dynamic end-to-end security with support for partner solutions and industry-standard protocols such as Apache Sentry, AWS IAM, Kerberos, LDAP, and more. Vertica utilizes an intuitive layered security model that provides multiple security authentication authorization mechanisms. Vertica will also maintain an audit trail, natively exported to other security domains for analysis and persistence.

Reviews from Real Users

“I am using Vertica for aggregations and dashboards. The most valuable feature of Vertica is the ability to receive large aggregations at a very quick pace. The use case of subclusters is very good.” - Bijal S., Group Chief Technology Officer at Netcore Solutions

“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.” - Munkhsaikhan B., Project Lead - Digital Transformation Unit at Bodi Electronics LLC

OpenText

Sample Customers

Information Not Available

Cerner, Game Show Network Game, Guess by Marciano, Supercell, Etsy, Nascar, Empirix, adMarketplace, and Cardlytics.

Buyer's Guide

BigQuery vs. Vertica

March 2025

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