We performed a comparison between BigQuery and Vertica based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."
"The setup is simple."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"When integrating their system into the cloud-based solutions, we were able to increase their efficiency and overall productivity twice compared with their on-premises option."
"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 is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights."
"One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions."
"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."
"Partition and join back to node are easy and simple for DBAs."
"The most valuable feature is Vertica's performance and the ease of using the database."
"I like the projection feature, which increases query performance."
"It maximizes cloud economics with Eon Mode by scaling cluster size to meet variable workload demands."
"We are also opening new areas of business and potential new revenue streams using Vertica's analytic functions, most notably geospatial, where we are able to run billions of comparisons of lat/long point locations against polygon and point/radius locations in seconds. "
"Speed and resiliency are probably the best parts of this product."
"The fast columnar store database structure allows our query times to be at least 10x faster than on any other database."
"The product’s performance could be much faster."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"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."
"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."
"I noticed recently it's more expensive now."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
"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."
"It needs integration with multiple clouds."
"The documentation of Vertica is an area with shortcomings where improvements are required."
"Vertica's native cloud support could be improved, and its installation could be made easier."
"It would be great if this were a managed service in AWS."
"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."
"Vertica can improve automation and documentation. Additionally, the solution can be simplified."
"The integration with AI has room for improvement."
"I think they need an easy client so that you can write queries easily, but it's not necessarily a weak point. I think some users would need them."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Vertica is ranked 6th in Cloud Data Warehouse with 83 reviews. BigQuery is rated 8.2, while Vertica is rated 8.2. The top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". On the other hand, the top reviewer of Vertica writes " A user-friendly tool that needs to improve its documentation part". BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Apache Hadoop and AWS Lake Formation, whereas Vertica is most compared with Snowflake, SQL Server, Amazon Redshift, Teradata and Oracle Database. See our BigQuery vs. Vertica report.
See our list of best Cloud Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.