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."I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."
"The setup is simple."
"The initial setup is straightforward."
"The most valuable features of BigQuery is that it supports standard SQL and provides good performance."
"The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."
"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."
"The initial setup process is easy."
"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."
"I appreciate the flexibility offered by Vertica's projections. It allows for modifying the primary projection without altering the tables, which helps to optimize queries without the need to modify the underlying data."
"We are able to integrate our Vertica data warehouse with Tableau to create numerous reports quickly and efficiently."
"The extensibility and efficiency provided by their C++ SDK."
"I have found the solution to be scalable."
"Initiate on one node, and the RPM propagates automatically to all other nodes. "
"Any novice user can tune vertical queries with minimal training (or no training at all)."
"I don't need any special hardware. I can use commodity hardware, which is nice to have in a commercial solution."
"Vertica is a great product because customers can compress and code data. The infrastructure that data warehouse solutions need is a commodity server so that customers don't have to invest in infrastructure."
"It would be helpful if they could provide some dashboards where you can easily view charts and information."
"The initial setup could be improved making it easier to deploy."
"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."
"The processing capability can be an area of improvement."
"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."
"The solution hinges on Google patterns so continued improvement is important."
"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."
"The integration with AI has room for improvement."
"In a future release, we would like to have artificial intelligence capabilities like neural networks. Customers are demanding this type of analytics."
"Promotion/marketing must be improved, even though it is a very useful product at very good price, it is not as "popular" as it should be."
"In my opinion, Vertica's documentation could be improved. Currently, there is not enough documentation available to gain a comprehensive understanding of the platform."
"There are a lot of limitations within this product and it makes things extremely hard for developers. It lacks Stored Procedure, packages, and triggers like other RDBMs."
"Suboptimal projection design causes queries to not scale linearly."
"Limitations in group by projections is where I would like to see an 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.
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