BigQuery vs Infobright DB comparison

"This solution is highly scalable."

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

"It's similar to a Hadoop cluster, except it's managed by Google."

"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."

"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."

"It has a well-structured suite of complimentary tools for data integration and so forth."

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

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

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

"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."

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

"The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms."

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

"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."

"There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard."

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

"BigQuery can be very expensive if not used properly. Introducing AI tools that would allow you to optimize the data extraction process is an area of serious improvement."

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

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

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

"The tool has competitive pricing."

"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 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 appears to be competitive for the intended usage scenarios we have in mind."

"BigQuery is inexpensive."

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

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