BigQuery vs IBM Db2 Warehouse on Cloud comparison

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

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

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

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

"One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions."

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

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

"The way that it scales will help a lot of customers that are stuck with Netezza boxes that can't grow any larger.​"

"The performance is okay as long as the volume of queries is not too high."

"It will be MPP, so performance should improve."

"It is stable when there is support from IBM."

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

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

"It would be helpful if they could provide some dashboards where you can easily view charts and information."

"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."

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

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

"The product’s performance could be much faster."

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

"Ultimately, the product itself has challenges and we are not currently satisfied with the support, either."

"There are some limitations in adding data files to table spaces, and improvements are needed for regional support."

"Containers get corrupted very easily. Restoring them using GPFS can result in a lot of issues."

"Right now, we are implementing on ESX VMware 6.0. Support for this platform is poor. Also, one of the backup/recovery options is broken and IBM is not addressing the issue."

"Tech support for dashDB is awful. We usually have tickets open for three to four weeks."

"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."

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

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

"The tool has competitive pricing."

"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."

"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."

"If your going to go with warehouse DB/dashDB, use the cloud or Sailfish version."