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BigQuery vs OpenText Analytics Database (Vertica) comparison

Google and OpenText are both solutions in the Cloud Data Warehouse category. Google is ranked #3 with an average rating of 8.5, while OpenText is ranked #11 with an average rating of 8.0. Google holds a 7.7% mindshare in CDW, compared to OpenText’s 6.1% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 91% of OpenText users who would recommend it.
BigQuery
Read 41 BigQuery reviews
  • 2,946 Views
  • 2,563 Comparison Views
100% willing to recommend
OpenText Analytics Database...
Read 86 OpenText Analytics Database (Vertica) reviews
  • 4,228 Views
  • 1,903 Comparison Views
91% willing to recommend
 

Comparison Buyer's Guide

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Executive SummaryUpdated on Sep 21, 2025

OpenText Analytics Database (Vertica) and BigQuery are leading players in the data analytics platform category. Vertica is particularly strong in pricing and support, while BigQuery has an edge in extensive features, with users' choice dependent on specific needs.

Features: Vertica is recognized for quick data loading, efficient scalability, and support for concurrent users, making it suitable for OLAP operations. It offers advanced analytics and fast query processing. BigQuery stands out for its serverless architecture, real-time data processing, and seamless integration with Google's ecosystem, providing instant query results and extensive machine learning integration, which is ideal for dynamic data workloads.

Room for Improvement: Vertica lacks transactional support and efficient query optimization, and it would benefit from improved stability and documentation. BigQuery could enhance its handling of special characters in migrations and improve cache options for external tables, with room for better integration with Google Cloud services and more affordable machine learning capabilities.

Ease of Deployment and Customer Service: Vertica can be deployed across private, on-premises, hybrid, and public clouds, whereas BigQuery is primarily a public cloud solution. Vertica’s technical support varies in quality, whereas BigQuery benefits from Google’s vast cloud infrastructure, generally providing good customer service and support access.

Pricing and ROI: Vertica offers flexible pricing based on data size, which is cost-effective and contributes to a favorable ROI. BigQuery's pay-as-you-go model is competitive for storage and offers savings through Google Cloud Platform integrations, although compute costs can add up. Both platforms show promising ROI, but selection depends on budget and strategy considerations.

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Buyer's Guide
BigQuery vs. OpenText Analytics Database (Vertica)
September 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:
 

ROI

Sentiment score
7.4
Organizations experienced improved performance and cost savings after adopting BigQuery, achieving a 75% cost reduction and efficient data management.
Sentiment score
6.9
Users report significant ROI from OpenText Analytics Database due to enhanced analytics, performance, and data accessibility, despite its cost.
 

Customer Service

Sentiment score
7.2
Customers generally find BigQuery support helpful, but integration challenges and resource availability need improvement despite positive responsiveness.
Sentiment score
7.0
OpenText Analytics Database customer service is praised for knowledgeable support, though some users report slow response and escalation needs.
I have been self-taught and I have been able to handle all my problems alone.
rating the customer support at ten points out of ten
For more quotes and insights, download the BigQuery report
No quotes available
For more quotes and insights, download the OpenText Analytics Database (Vertica) report
 

Scalability Issues

Sentiment score
8.0
BigQuery offers impressive scalability and efficiency for large data, but may be costly and present integration challenges for smaller users.
Sentiment score
7.1
OpenText Analytics Database (Vertica) excels at scaling large data volumes but faces challenges with schema modifications and node additions.
It is a 10 out of 10 in terms of scalability.
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.
For more quotes and insights, download the BigQuery report
No quotes available
For more quotes and insights, download the OpenText Analytics Database (Vertica) report
 

Stability Issues

Sentiment score
8.5
BigQuery is highly stable and reliable for cloud data analytics, efficiently handling large volumes with minor issues.
Sentiment score
7.1
OpenText Analytics Database (Vertica) is stable and robust, with minor query and network issues noted by users.
 

Room For Improvement

BigQuery users face challenges with migration, integration, cost, scaling, user interfaces, and call for better machine learning capabilities.
OpenText Analytics Database needs better workload management, documentation, SQL optimization, cloud support, machine learning, and cost efficiency.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
In general, if I know SQL and start playing around, it will start making sense.
For more quotes and insights, download the BigQuery report
No quotes available
For more quotes and insights, download the OpenText Analytics Database (Vertica) report
Luís Silva - PeerSpot reviewer
JR
reviewer2641596 - PeerSpot reviewer
 

Setup Cost

BigQuery offers flexible, pay-as-you-go pricing based on data usage, with low storage costs and adaptable enterprise plans.
OpenText Analytics Database offers flexible pricing based on storage, with competitive costs, community edition, and negotiable licensing options.
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
For more quotes and insights, download the BigQuery report
No quotes available
For more quotes and insights, download the OpenText Analytics Database (Vertica) report
 

Valuable Features

BigQuery excels in scalability, performance, cost-efficiency, and integration with Google products, making it ideal for complex data analyses.
OpenText Analytics Database offers rapid data processing, scalability, ease of use, and cost efficiency with advanced analytics capabilities.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
For more quotes and insights, download the BigQuery report
No quotes available
For more quotes and insights, download the OpenText Analytics Database (Vertica) report
reviewer2641596 - PeerSpot reviewerLuís Silva - PeerSpot reviewer
JR
 

Categories and Ranking

BigQuery
Ranking in Cloud Data Warehouse
3rd
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
41
Ranking in other categories
No ranking in other categories
OpenText Analytics Database...
Ranking in Cloud Data Warehouse
11th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
86
Ranking in other categories
Data Warehouse (7th)
 

Mindshare comparison

As of September 2025, in the Cloud Data Warehouse category, the mindshare of BigQuery is 7.7%, down from 7.9% compared to the previous year. The mindshare of OpenText Analytics Database (Vertica) is 6.1%, up from 5.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Market Share Distribution
ProductMarket Share (%)
BigQuery7.7%
OpenText Analytics Database (Vertica)6.1%
Other86.2%
Cloud Data Warehouse
 

Featured Reviews

Luís Silva - PeerSpot reviewer
Handles large data sets efficiently and offers flexible data management capabilities
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features. The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.
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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…
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Top Industries

By visitors reading reviews
Computer Software Company
16%
Financial Services Firm
14%
Manufacturing Company
11%
Retailer
7%
Financial Services Firm
18%
Computer Software Company
16%
Manufacturing Company
8%
Comms Service Provider
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business11
Midsize Enterprise9
Large Enterprise20
By reviewers
Company SizeCount
Small Business29
Midsize Enterprise23
Large Enterprise38
 

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?
I believe the cost of BigQuery is competitive versus the alternatives in the market, but it can become expensive if the tool is not used properly. It is on a per-consumption basis, the billing, so ...
See all answers
What needs improvement with BigQuery?
I have not used BigQuery for AI and machine learning projects myself. I know how to use it, and I can see where it would be useful, but so far, in my projects, I have not included a BigQuery compon...
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
 

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Compared 12% of the time
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Product Reports

Buyer's Guide
BigQuery
September 2025
BigQuery reportDownload BigQuery product report
Buyer's Guide
OpenText Analytics Database (Vertica)
September 2025
OpenText Analytics Database (Vertica) reportDownload OpenText Analytics Database (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

OpenText Analytics Database Vertica is known for its fast data loading and efficient query processing, providing scalability and user-friendliness with a low cost per TB. It supports large data volumes with OLAP, clustering, and parallel ingestion capabilities.

OpenText Analytics Database Vertica is designed to handle substantial data volumes with a focus on speed and efficient storage through its columnar architecture. It offers advanced performance features like workload isolation and compression, ensuring flexibility and high availability. The database is optimized for scalable data management, supporting data scientists and analysts with real-time reporting and analytics. Its architecture is built to facilitate hybrid deployments on-premises or within cloud environments, integrating seamlessly with business intelligence tools like Tableau. However, challenges such as improved transactional capabilities, optimized delete processes, and better real-time loading need addressing.

What features define OpenText Analytics Database Vertica?
  • Fast Data Loading: Quickly ingests large data volumes.
  • Columnar Storage: Optimizes storage efficiency and speed.
  • OLAP Support: Provides robust analytics capabilities.
  • Advanced Compression: Enhances storage efficiency and performance.
  • Parallel Data Ingestion: Enables faster data processing.
  • Fault Tolerance: Ensures high availability and reliability.
What benefits should users expect?
  • Cost Efficiency: Low cost per TB is advantageous.
  • Scalability: Accommodates growing data needs seamlessly.
  • Integration Flexibility: Works well with BI tools like Tableau.
  • Real-Time Reporting: Delivers immediate insights for decision-making.

OpenText Analytics Database Vertica's implementation spans industries such as finance, healthcare, and telecommunications. It serves as a central data warehouse offering scalable management, high-speed processing, and geospatial functions. Companies benefit from its capacity to integrate machine learning and operational reporting, enhancing analytical capabilities.

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. OpenText Analytics Database (Vertica)
September 2025
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