BigQuery vs Oracle Autonomous Data Warehouse comparison

"It's straightforward to set up."

"BigQuery's querying capabilities are very optimized for large datasets."

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

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

"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."

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

"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."

"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."

"With Oracle Autonomous Data Warehouse, things are much simpler. Creating a structure, initializing the servers, extending the servers, those are all things that are very, very easy. That's the main reason we use it."

"The performance and scalability are awesome."

"Self-patching and runs machine-learning across its logs all the time"

"A very good integration feature that restricts access to unauthorized people."

"The solution is self-securing. All data is encrypted and security updates and patches are applied automatically both periodically and off-cycle."

"The product has self-repair features."

"The solution has a self-backup, so you don't need a DBA (database administrator) to do a backup."

"One advantage is that if you already have an Oracle Database, it easily integrates with that."

"It would be beneficial to integrate additional tools, particularly from a business intelligence perspective."

"BigQuery can be very expensive if not used properly. Introducing AI tools that would allow you to optimize the data extraction process is an area of serious improvement."

"The processing capability can be an area of improvement."

"When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming."

"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."

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

"There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."

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

"We are not satisfied with the technical support. Their understanding is lacking."

"Oracle Autonomous Data Warehouse is not available as an on-premises solution."

"I would like to see an on-premise solution in the future."

"Optimization should be better."

"The initial setup was pretty complex. It was not easy."

"A lot of the tools that were previously there have now been taken away."

"The solution could be improved by allowing for migration tools from other cloud services, including migration from Amazon Redshift, RDS, and Aurora."

"The setup is complex."

"I have tried my own setup using my Gmail ID, and I think it had a $300 limit for free for a new user. That's what Google is offering, and we can register and create a project."

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

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

"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."

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

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

"The platform is inexpensive."

"In terms of architecture and pricing structure, I feel it is a little bit costly compared to Azure. It's fine compared to RedShift, but compared to Azure, it's a bit pricey when you calculate for one TB storage plus around five hours of reporting with the frequency of 1TB data. The cost adds up, making Oracle a bit expensive."

"Oracle Autonomous Data Warehouse's pricing is fair and reasonable compared to the other cloud vendors."

"The licensing cost of the product can vary since you can integrate it very easily with other products or other cloud products...You pay as you use it, so it is not yearly or monthly payments to be made toward Oracle."

"You pay as you go, and you don't pay for services that you don't use."

"The cost is perfect with Oracle Universal credit."

"The solution's cost is reasonable."

"The solution is expensive."

"Cloud solutions are cheaper, but in the long run, they may not be much cheaper. They certainly have a lower initial cost. The licensing is yearly, and it is based on the size of the hardware and the number of users."