We performed a comparison between BigQuery and IBM Db2 Warehouse based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."When integrating their system into the cloud-based solutions, we were able to increase their efficiency and overall productivity twice compared with their on-premises option."
"It's straightforward to set up."
"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's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."
"The setup is simple."
"The product’s most valuable feature is its ability to manage the database on the cloud."
"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."
"I think it scales really well and as long as you take enough time to learn a little bit about it, it works really well."
"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."
"Provides good security and reliability."
"The standout feature of IBM Db2 Warehouse, which is particularly valuable for large enterprises, is its ability to handle big data."
"Some of the best features are stored procedures, parallelism, and different indexing strategies."
"The analytics engine is not bad at forecasting predictions."
"Some of the queries are complex and difficult to understand."
"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."
"They could enhance the platform's user accessibility."
"I noticed recently it's more expensive now."
"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."
"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."
"The process of migrating from Datastore to BigQuery should be improved."
"The solution should reduce its pricing."
"The areas of the solution that is needing the most improvement are separating compute from storage, elasticity, which means scaling up and then retracting."
"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."
"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 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."
"There should be more material available for training and training should be free."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while IBM Db2 Warehouse is ranked 14th in Data Warehouse with 8 reviews. BigQuery is rated 8.2, while IBM Db2 Warehouse is rated 7.6. The top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". On the other hand, the top reviewer of IBM Db2 Warehouse writes "Useful for ETL process and has good documentation ". BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and IBM Netezza Performance Server, whereas IBM Db2 Warehouse is most compared with Oracle Exadata, Snowflake, Amazon Redshift, Apache Hadoop and Teradata. See our BigQuery vs. IBM Db2 Warehouse report.
See our list of best Cloud Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
