BigQuery vs TIBCO Live Datamart comparison

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

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

"The integrated data storage features are good."

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

"The initial setup is simple."

"BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space."

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

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

"The solution has a powerful aggregating feature"

"You can create your own rules that include mathematic calculations."

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

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

"BigQuery should integrate with other tools, such as Cloud Logging and Local Studio, to enhance its capabilities further and enable powerful and innovative analyses."

"When I execute a query, the dashboard doesn't always present the output seamlessly."

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

"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 solution should reduce its pricing."

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

"Improvements need to be made on the load balancing side."

"The solution's setup could be quicker and easier."

"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 platform is inexpensive."

"The product’s pricing could be more flexible for end users."

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

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

"The solution's pricing is cheaper compared to other solutions."

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

"The solution might be considered a bit expensive because it competes with open-source products."