BigQuery vs Oracle Big Data Appliance comparison

"It is stable and reliable; there are no bugs or glitches."

"The interface is what I find particularly valuable."

"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."

"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."

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

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

"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."

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

"Because Big Data Appliance allows me to have a single source of truth, it means I have clean data that can be monetized and leveraged to gain more insights with real-time reports from the dashboard."

"The most valuable feature is that it can be used in conjunction with Spark memory processing, and our processing time has gone to 20 minutes from what used to be two or three hours on the mainframe."

"This is a comprehensive solution that is easy to deploy."

"Big Data responds to my needs."

"We could work on large unstructured datasets from different parts of the world on Facebook and Twitter to get better insights."

"The best feature of Oracle Big Data Appliance, from a price perspective for the storage, is because they have technology called the ZFS technology; it offers huge storage with a decent price."

"The best thing about the product is that the end-user can build the reports by themselves without really knowing anything about databases."

"The best thing about the product is that the end-user can build the reports by themselves without really knowing anything about databases."

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

"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."

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

"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."

"There are integration challenges, particularly with performance when exporting data to BigQuery from other tools like Qualitics."

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

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

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

"We need to display the results on our website. I think there is another product that joins with it to give you the possibility."

"The product should be simplified for the average user."

"We had to pursue several performance tuning measures to bring up the system to scale."

"From a technical perspective, Big Data Appliance could be improved with more innovation in the AI and machine-learning parts instead of relying on Cloudera."

"It seems like the deployment of repositories has become more difficult in later versions of the product rather than easier."

"It seems like the deployment of repositories has become more difficult in later versions of the product rather than easier."

"The product should be simplified for the average user."

"From a technical perspective, Big Data Appliance could be improved with more innovation in the AI and machine-learning parts instead of relying on Cloudera."

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

"The tool has competitive pricing."

"Price-wise, I think that is very reasonable."

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

"BigQuery is inexpensive."

"The platform is inexpensive."

"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."

"Oracle's prices are too high compared to others in the market."