Actian Ingres vs BigQuery comparison

"The deployment of our solution across a number of servers using Ingres .NET has meant that we can protect the database server behind a highly secure firewall and deploy the front end solutions on a normal web server."

"The most valuable features for us are reliability and performance."

"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 is straightforward."

"Even non-coders can review the data in BigQuery."

"The most valuable features of BigQuery is that it supports standard SQL and provides good performance."

"BigQuery excels at data analysis. It processes vast amounts of information using its advanced architecture and sophisticated querying capabilities, making it crucial for critical insights and safe for handling sensitive data."

"The product's most valuable features include its scalability and the ability to handle complex queries on large datasets."

"It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."

"The feature called calibrating the capacity is valuable."

"The ability to reset the log file without stopping the DBMS would be helpful for us."

"The seamless integration is a bit of an issue."

"We'd like to see more local data residency."

"When I execute a query, the dashboard doesn't always present the output seamlessly. Troubleshooting requires opening each pipeline individually, which is time-consuming."

"So our challenge in Yemen is convincing many people to go to cloud services."

"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."

"They could enhance the platform's user accessibility."

"The solution should reduce its pricing."

"The processing capability can be an area of improvement."

"The solution's pricing is cheaper compared to other solutions."

"The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera."

"The product’s pricing could be more flexible for end users."

"The pricing is good and there are no additional costs involved."

"The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements."

"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."

"Its cost structure operates on a pay-as-you-go model."

"One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte, but BigQuery charges based on how much data is inserted into the tables. BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models are different and it becomes a key consideration in the decision of which platform to use."