

OpenText Analytics Database (Vertica) and BigQuery are key players in the data analytics and cloud data warehousing space. BigQuery holds an advantage with its serverless architecture that integrates smoothly with Google's ecosystem offering scalability and machine learning features.
Features: Vertica offers scalability and high performance with its clustering, columnar storage, and projections. It supports concurrent user access and employs an advanced query optimization strategy. BigQuery's serverless design simplifies big data handling without capacity planning and provides seamless integration with Google's ecosystem for efficient SQL querying and machine learning.
Room for Improvement: Vertica could improve workload management and provide more intuitive integration tools, while also refining configuration options for database management. BigQuery could enhance handling of special characters and offer caching support for external tables. Improving integration across platforms and management ease could further benefit BigQuery users.
Ease of Deployment and Customer Service: Vertica supports deployments in private, public, and hybrid clouds with robust on-premises integration, though its technical support varies in quality. BigQuery, primarily operating in the public cloud, is praised for straightforward deployment and solid customer service, though there is room for improved technical support.
Pricing and ROI: Vertica's storage-based pricing can be expensive without careful planning, yet users report a strong ROI due to performance. BigQuery's pay-as-you-go model is generally seen as cost-effective for large-scale data analysis despite potential cost escalation without query optimization, offering significant savings when utilized efficiently.
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.
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.
rating the customer support at ten points out of ten
I have been self-taught and I have been able to handle all my problems alone.
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.
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.
Overall, our experience with OpenText Analytics Database (Vertica) customer support has been good and reliable.
It is a 10 out of 10 in terms of scalability.
We have not seen problems with scaling.
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.
We have experienced easy horizontal scaling, consistent query performance as data grew, and the ability to handle large analytic workloads.
OpenText Analytics Database (Vertica) has very good scalability.
The scalability of OpenText Analytics Database (Vertica) is very strong.
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.
OpenText Analytics Database (Vertica) is very stable.
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.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
Smarter automatic projection management is needed with more intelligence, auto projection creation, automatic optimization, and reduced manual testing with better workload management.
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.
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.
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.
The pricing for OpenText Analytics Database (Vertica) is somewhat on the higher side for the license.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
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.
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.
Projection and columnar storage are the most valuable features because they dramatically improve query performance and reduce the need for index management.
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.
| Product | Mindshare (%) |
|---|---|
| BigQuery | 8.0% |
| OpenText Analytics Database (Vertica) | 5.5% |
| Other | 86.5% |

| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 9 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 23 |
| Large Enterprise | 42 |
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.
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?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.
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