BigQuery vs Firebolt comparison

"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."

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

"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."

"Using BigQuery's central repository brings dispersed information together, which facilitates exploring the data and gaining insights, and consequently, it improves operations, response time, and the business overall."

"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."

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

"The features I have found most valuable in BigQuery include the query cache, which is good and scales well; it handles huge amounts of data quite well, and the multi-regions feature works well."

"The interface is what I find particularly valuable."

"Firebolt is fast for analytical purposes. For example, we have analytical data in our data warehouse, and Firebolt can quickly query it to generate quick results."

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

"The product could benefit from improvements in user-friendliness, particularly in terms of the user interface."

"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."

"I rate BigQuery six out of 10 for affordability. It could be cheaper."

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

"It would be beneficial if BigQuery could be made more affordable."

"The process of migrating from Datastore to BigQuery should be improved."

"To be very specific, here in the Middle East, I'm based out of the UAE, and Google has a very narrow footprint, a very limited footprint here in the region."

"Firebolt's engine takes a long time to start because it needs to make engine calls."

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

"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."

"Price-wise, I think that is very reasonable."

"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."

"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."

"BigQuery pricing can increase quickly. It's a high-priced solution."

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