BigQuery vs Oracle Big Data Appliance 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."

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

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

"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."

"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 setup is simple."

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

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

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

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

"Some of the queries are complex and difficult to understand."

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

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

"I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in."

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

"The product’s performance could be much faster."

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

"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."

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

"The platform is inexpensive."

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

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

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

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

"The pricing is good and there are no additional costs involved."

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

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

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