BigQuery vs IBM Db2 Warehouse comparison

"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."

"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."

"BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes."

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

"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."

"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."

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

"The integrated data storage features are good."

"The solution is stable."

"IBM Db2 Warehouse is more resilient when dealing with business intelligence applications."

"The standout feature of IBM Db2 Warehouse, which is particularly valuable for large enterprises, is its ability to handle big data."

"The most valuable features of IBM Db2 Warehouse include its availability, easy integration, ability to deal with a large size of data, excellent performance with no delays, powerful security mechanisms, and effective support."

"IBM Db2 Warehouse is more resilient when dealing with business intelligence applications."

"Provides good security and reliability."

"It can be mounted on the cloud, which is a huge plus. If the client, for example, starts small with on-premise deployment and then it rapidly needs to grow, we can transfer this to the cloud easily."

"Some of the best features are stored procedures, parallelism, and different indexing strategies."

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

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

"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."

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

"The main challenges are in the areas of performance and cost optimizations."

"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."

"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."

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

"In terms of improvement, IBM Db2 Warehouse should be more scalable."

"Lacks sufficient documentation and particularly in Spanish."

"The biggest challenge anyone could have with Db2 Warehouse is their references or online resources and documentation. They are very, very, very limited on the web."

"It takes a long time to have a new version or release, similar to Oracle."

"The areas of the solution that is needing the most improvement are separating compute from storage, elasticity, which means scaling up and then retracting."

"The biggest problems we have is when the backup solution is failing or slow and we run out of log space, which has happened probably a couple of times in the last four years."

"There should be more material available for training and training should be free."

"There were some initial challenges with IBM Db2 Warehouse about eight months ago when I worked in this environment. When I coordinated with IBM support, they mentioned that the memory was insufficient for our needs. Our business environment required significantly more memory than the previous cluster could provide. Consequently, we have worked closely with on-site IBM technical personnel to address this issue."

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

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

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

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

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

"BigQuery is inexpensive."

"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."

"In a traditional on-prem database, in a data warehouse, the solution is probably on the expensive side."