BigQuery vs OpenText Analytics Database (Vertica) comparison

BigQuery vs. OpenText Analytics Database (Vertica)

April 2026

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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

4.9

BigQuery offers improved performance, cost savings, and intuitive features, with some users reporting up to 75% cost reductions.

Sentiment score

6.2

Organizations achieved financial and operational gains with Vertica, reducing costs and processing time while enhancing analytics and performance.

No quotes available

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I saved a lot of money because the storage was on a cheaper alternative and was not directly on OpenText Analytics Database (Vertica), but on S3.

Senior Software Engineer at a tech vendor with 5,001-10,000 employees

The time we used to take with our earlier databases has reduced to one-tenth of what was there earlier, which is a positive outcome that can be converted to financial metrics in terms of return on investment.

For more quotes and insights, download the OpenText Analytics Database (Vertica) report

Data Engineering Associate Manager at a tech vendor with 10,001+ employees

Customer Service

Sentiment score

7.2

Customers find Google BigQuery support effective but sometimes limited, relying on documentation, forums, and expert assistance for issues.

Sentiment score

7.1

OpenText Analytics Database's customer service is generally valued for expertise but has occasional response time and escalation issues.

rating the customer support at ten points out of ten

Sr. Team Lead - IT at InfoStretch

I have been self-taught and I have been able to handle all my problems alone.

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Chief Technical Lead at a consultancy with 201-500 employees

I would rate their customer service pretty good on a scale of one to 10, as they gave me access to the platform on a grant.

Mikah Sellers

Principal at Sgt Suds

Throughout this process, customer support was outstanding, and we had a person actively supporting us from the OpenText Analytics Database (Vertica) team for our use case.

Senior Software Engineer at a tech vendor with 5,001-10,000 employees

Overall, our experience with OpenText Analytics Database (Vertica) customer support has been good and reliable.

For more quotes and insights, download the OpenText Analytics Database (Vertica) report

consultant at tcs

Scalability Issues

Sentiment score

7.8

BigQuery offers excellent scalability and efficiency, supporting vast data seamlessly, though cost and integration challenges may arise.

Sentiment score

7.2

OpenText Vertica offers scalable, efficient data handling with robust performance, though pricing and node addition may challenge some users.

It is a 10 out of 10 in terms of scalability.

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Chief Technical Lead at a consultancy with 201-500 employees

We have not seen problems with scaling.

reviewer2807520

Director at a consultancy with 11-50 employees

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.

reviewer2641596

Expert Analyst at a healthcare company with 5,001-10,000 employees

We have experienced easy horizontal scaling, consistent query performance as data grew, and the ability to handle large analytic workloads.

consultant at tcs

OpenText Analytics Database (Vertica) has very good scalability.

For more quotes and insights, download the OpenText Analytics Database (Vertica) report

Data Engineering Associate Manager at a tech vendor with 10,001+ employees

OpenText Analytics Database (Vertica) can scale to a great extent.

Anuj Chourasia

Analytics Manager at a wholesaler/distributor with 10,001+ employees

Stability Issues

Sentiment score

8.3

BigQuery is stable and reliable, with efficient data handling, few bugs, and strong support, despite occasional slow queries.

Sentiment score

7.2

OpenText Analytics Database offers high stability and performance, with successful migrations and minimal clustering issues ensuring user satisfaction.

In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google.

reviewer2807520

Director at a consultancy with 11-50 employees

For more quotes and insights, download the BigQuery report

OpenText Analytics Database (Vertica) is very stable.

For more quotes and insights, download the OpenText Analytics Database (Vertica) report

Senior Software Engineer at a tech vendor with 5,001-10,000 employees

Room For Improvement

BigQuery struggles with cost, accessibility, scalability, and lacks data residency, needing better integration, performance, and machine learning features.

OpenText Analytics Database needs improvements in community, cloud features, SQL, integration, cost efficiency, machine learning, and developer tools.

Troubleshooting requires opening each pipeline individually, which is time-consuming.

Sr. Team Lead - IT at InfoStretch

In general, if I know SQL and start playing around, it will start making sense.

reviewer2641596

Expert Analyst at a healthcare company with 5,001-10,000 employees

BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.

For more quotes and insights, download the BigQuery report

Chief Technical Lead at a consultancy with 201-500 employees

Smarter automatic projection management is needed with more intelligence, auto projection creation, automatic optimization, and reduced manual testing with better workload management.

consultant at tcs

Projections could be made more dynamic, and if they could find a faster way to update, insert, and delete data, that would also be helpful.

Senior Software Engineer at a tech vendor with 5,001-10,000 employees

OpenText Analytics Database (Vertica) does not have a cloud-based UI that Snowflake has, which features a very good comprehensive GUI for querying and analyzing data.

For more quotes and insights, download the OpenText Analytics Database (Vertica) report

Data Engineering Associate Manager at a tech vendor with 10,001+ employees

Setup Cost

BigQuery uses a pay-as-you-go model, balancing affordability with strategic expense management for data storage and query execution.

OpenText Analytics Database offers flexible pricing, often cost-effective for its advanced features, despite being pricey vs. open-source solutions.

Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.

Chief Technical Lead at a consultancy with 201-500 employees

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

Sr. Team Lead - IT at InfoStretch

The pricing for OpenText Analytics Database (Vertica) is somewhat on the higher side for the license.

For more quotes and insights, download the OpenText Analytics Database (Vertica) report

Senior Software Engineer at a tech vendor with 5,001-10,000 employees

Valuable Features

BigQuery provides scalable, serverless data processing with fast query capabilities, seamless GCP integration, SQL support, and competitive pricing.

OpenText Analytics Database excels in fast query responses, scalability, and real-time insights, enhancing decision-making and operational efficiency.

It is really fast because it can process millions of rows in just a matter of one or two seconds.

reviewer2641596

Expert Analyst at a healthcare company with 5,001-10,000 employees

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

Sr. Team Lead - IT at InfoStretch

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.

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Chief Technical Lead at a consultancy with 201-500 employees

I can use it in Eon Mode in which I can store the data in cheaper storage such as Amazon S3 and have different compute nodes.

Senior Software Engineer at a tech vendor with 5,001-10,000 employees

Projection and columnar storage are the most valuable features because they dramatically improve query performance and reduce the need for index management.

consultant at tcs

The best features that OpenText Analytics Database (Vertica) offers are mainly the parallel processing, ETL capabilities, and the multi-cloud features which are very handy to use.

For more quotes and insights, download the OpenText Analytics Database (Vertica) report

Data Engineering Associate Manager at a tech vendor with 10,001+ employees

Categories and Ranking

BigQuery

Ranking in Cloud Data Warehouse

3rd

Average Rating

8.2

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

OpenText Analytics Database...

Ranking in Cloud Data Warehouse

11th

Average Rating

8.2

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Data Warehouse (5th)

Mindshare comparison

As of May 2026, in the Cloud Data Warehouse category, the mindshare of BigQuery is 7.4%, up from 6.8% compared to the previous year. The mindshare of OpenText Analytics Database (Vertica) is 5.6%, down from 5.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Warehouse Mindshare Distribution
Product	Mindshare (%)
BigQuery	7.4%
OpenText Analytics Database (Vertica)	5.6%
Other	87.0%

Cloud Data Warehouse

Featured Reviews

Handles large data sets efficiently and offers flexible data management capabilities

Chief Technical Lead at a consultancy with 201-500 employees

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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Data warehousing has transformed reporting performance and now delivers near real-time insights

consultant at tcs

OpenText Analytics Database (Vertica) is a very powerful analytic database, but like any platform, there are areas where it can improve to make daily work even smoother. Better cloud-native experience is one area for improvement. OpenText Analytics Database (Vertica) was originally designed as an on-premises analytic database and later moved to cloud. Improvement opportunities include more seamless cloud-native features such as auto-scaling, serverless options, and easier cluster management. Competitors such as Snowflake and BigQuery provide more fully managed experiences. Easier UI is another area for improvement. Most administration is currently done by SQL and command line tools. An improvement opportunity would be a more modern web UI for monitoring, workload management, and troubleshooting. Faster ecosystem and community growth is needed. In short, OpenText Analytics Database (Vertica) could improve in areas such as cloud-native capability, modern UI for administration, stronger real-time streaming integration, and growing its ecosystem and community. These enhancements would make it easier to manage and adopt compared to newer cloud-first analytic platforms. From a day-to-day operational perspective, there are a few areas where OpenText Analytics Database (Vertica) could improve to make our work smoother. Smarter automatic projection management is needed with more intelligence, auto projection creation, automatic optimization, and reduced manual testing with better workload management. Right now, monitoring queries often requires system tables and manual analysis. Troubleshooting slow queries takes time. A modern real-time dashboard showing query bottlenecks and resource users would enable quick detection. The impact could be faster issue resolution and less time spent debugging performance. Storage native interaction with modern data tools is also important. In short, from a day-to-day perspective, improvements in automatic projection optimization, better workload monitoring dashboard, easier schema evolution, and stronger modern tool integration would significantly reduce manual tuning effort and improve developer productivity. While OpenText Analytics Database (Vertica) is very powerful, these enhancements would make it more efficient for the analytics team.

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Manufacturing Company

10%

Computer Software Company

Media Company

Financial Services Firm

18%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	13
Midsize Enterprise	9
Large Enterprise	20

By reviewers
Company Size	Count
Small Business	29
Midsize Enterprise	23
Large Enterprise	43

Questions from the Community

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 ...

What needs improvement with BigQuery?

With what I have seen in BigQuery, I had some response times problems, but then it is an analytical database and not a transactional database, so it comes with eventual consistency. I cannot have e...

What is your primary use case for BigQuery?

We are mostly dealing with Google solutions such as BigQuery, NoSQL, SQL analytical database, secrets manager, and most of the serverless infrastructure as well, databases. I run SQL queries on Big...

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?

My experience with pricing, setup cost, and licensing is limited because the organization handled the licensing and pricing as well as the cost setup.

What needs improvement with Vertica?

OpenText Analytics Database (Vertica) is already doing great. There could be a community which could have been much more advanced and more people can be engaged so that any kind of questions, queri...

Oracle Autonomous Data Warehouse vs BigQuery

Comparisons

Compared 14% of the time

Snowflake vs BigQuery

Compared 10% of the time

Teradata vs BigQuery

Compared 10% of the time

Snowflake Analytics vs BigQuery

Compared 6% of the time

IBM Db2 Warehouse vs BigQuery

Compared 6% of the time

More BigQuery Competitors

Teradata vs OpenText Analytics Database (Vertica)

Compared 7% of the time

Snowflake vs OpenText Analytics Database (Vertica)

Compared 7% of the time

Oracle Exadata vs OpenText Analytics Database (Vertica)

Compared 5% of the time

SAP IQ vs OpenText Analytics Database (Vertica)

Compared 4% of the time

Oracle Database vs OpenText Analytics Database (Vertica)

Compared 4% of the time

More OpenText Analytics Database (Vertica) Competitors

Product Reports

Download BigQuery product report

BigQuery

May 2026

OpenText Analytics Database (Vertica)

April 2026

OpenText Analytics Database (Vertica) report

Download OpenText Analytics Database (Vertica) product report

Also Known As

Micro Focus Vertica, HPE Vertica, HPE Vertica on Demand

Overview

BigQuery is a powerful cloud-based data warehouse offering advanced SQL querying, seamless Google integration, and scalable handling of large datasets. Its serverless architecture and built-in AI capabilities facilitate efficient data processing and insights extraction.

BigQuery provides an efficient data analysis platform with low-latency performance and cost-effective on-demand pricing. Leveraging Google's cloud infrastructure for data storage, it offers robust security and high availability. While it excels in SQL support and caching features, it can improve on user accessibility, integration with diverse tools, and machine learning feature expansion. Making it more accessible for smaller entities through improved cost management and local data compliance is essential. Enhancements in query speed and intuitive interfaces can further optimize performance.

What features are offered by BigQuery?

User-friendly interface: Simplifies data interaction, making complex processes more manageable.
Serverless architecture: Eliminates the need for infrastructure management, improving scalability and efficiency.
Advanced SQL querying: Enables complex data retrieval and manipulation with ease.
Machine learning integration: Facilitates AI-driven insights directly from the platform.
Cost-effective pricing: On-demand pricing helps manage expenses by paying only for used resources.
Seamless Google integration: Works well with familiar Google services for a cohesive experience.
High availability and robust security: Ensures data is secure and accessible when needed.

What benefits should be considered when evaluating BigQuery?

Efficient data processing: Quickly handles large datasets, supporting fast data-driven decision-making.
Scalable infrastructure: Grows capacity alongside business needs without manual infrastructure adjustments.
Enhanced data analysis: SQL support and caching features improve analysis precision and speed.
AI and machine learning capabilities: Enable advanced data insights and predictive analytics.
Integration with existing tools: Ensures smooth workflow transitions with services such as Google Analytics and Tableau.

In industries like healthcare, finance, and marketing, BigQuery is extensively used for data storage, generating reports, and supporting ETL processes. Educational institutions leverage it for analytics, aligning seamlessly with Google Cloud for serverless infrastructure efficiencies.

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.