BigQuery vs Infobright DB comparison

"The integrated data storage features are good."

"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 initial setup is straightforward."

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

"This solution is highly scalable."

"BigQuery's querying capabilities are very optimized for large datasets."

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

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

"It can be slower and more problematic compared to other platforms such as Snowflake."

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

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

"When I execute a query, the dashboard doesn't always present the output seamlessly."

"The initial setup could be improved making it easier to deploy."

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

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

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

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

"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."

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

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

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

"The platform is inexpensive."

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

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

"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."

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