BigQuery vs Infobright DB comparison

"The product’s most valuable feature is its ability to manage the database on the cloud."

"We like the machine learning features and the high-performance database engine."

"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."

"This solution is highly scalable."

"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."

"Its integration with other tools like Atlan through a Google Chrome extension is highly beneficial."

"The solution is very useful nowadays for keeping a huge number of records."

"The feature called calibrating the capacity is valuable."

"It has very amazing smart grid query feature for very fast aggregate queries across millions of rows"

"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."

"Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process."

"We'd like to have more integrations with other technologies."

"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."

"Some of the queries are complex and difficult to understand."

"There are some limitations in the query latency compared to what it was three years ago."

"Sometimes, support specialists might not have enough experience or business understanding, which can be an issue."

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

"Only the data from the columns that reached 2GB will actually decrease. Other columns below 2GB in size do not leave the disk."

"I have tried my own setup using my Gmail ID, and I think it had a $300 limit for free for a new user. That's what Google is offering, and we can register and create a project."

"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."

"The platform is inexpensive."

"The price is a bit high but the technology is worth it."

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

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."

"The tool has competitive pricing."

"Our pricing was based on server instances and it was actually very cheap compared to Oracle. I guess you get what you pay for."