BigQuery vs IBM Db2 Warehouse comparison

"BigQuery has a very nice interface that you can easily learn if you know SQL."

"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."

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

"The most valuable features of BigQuery is that it supports standard SQL and provides good performance."

"The setup is simple."

"This solution is highly scalable."

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

"The product’s most valuable feature is its ability to manage the database on the cloud."

"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."

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

"The solution is stable."

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

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

"It can scale effectively as long as resources are available."

"The analytics engine is not bad at forecasting predictions."

"We would like to be able to calibrate the solution to run on top of a raw file."

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

"Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process."

"The solution hinges on Google patterns so continued improvement is important."

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

"The price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them."

"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

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

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

"Responding to support tickets takes a lot of time. It can range from two to four days, even for critical issues."

"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."

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

"Lacks sufficient documentation and particularly in Spanish."

"IBM Db2 Warehouse needs to improve its interface."

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

"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."

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

"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 tool has competitive pricing."

"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."

"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."

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

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