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."It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"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."
"As a cloud solution, it's easy to set up."
"BigQuery excels at structuring data, performing predictions, and conducting insightful analyses and it leverages machine learning and artificial intelligence capabilities, powered by Google's Duarte AI."
"The initial setup process is easy."
"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."
"We like the machine learning features and the high-performance database engine."
"Allows us to take volumes and process them at a very high speed."
"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."
"Its projections and encoding are excellent tools for tuning large volumes."
"Vertica is easy to use and provides really high performance, stability, and scalability."
"Vertica is a columnar database, this support our developments in analytics, advanced analytics, and ETL process with large sets of data."
"Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data."
"Vertica is a columnar database where the query performance is extremely fast and it can be used for real-time integrations for API and other applications. The solution requires zero maintenance which is helpful."
"Its analytics has enabled Pythian's clients to get the business insights as quick as they wanted. Its lower maintenance has also improved the ROI."
"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"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 would be beneficial to integrate additional tools, particularly from a business intelligence perspective."
"The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms."
"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 main challenges are in the areas of performance and cost optimizations."
"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."
"Vertica can improve automation and documentation. Additionally, the solution can be simplified."
"Support is an area where it could get better."
"They could improve the integration and some of the features in the cloud version."
"Documentation has become much better, but can always use some improvement."
"In my opinion, Vertica's documentation could be improved. Currently, there is not enough documentation available to gain a comprehensive understanding of the platform."
"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."
"The integration with AI has room for improvement."
"The geospatial functionality could be designed better."
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.
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