BigQuery vs Oracle Autonomous Data Warehouse comparison

"The query tool is scalable and allows for petabytes of data."

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

"It has a well-structured suite of complimentary tools for data integration and so forth."

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

"The initial setup is straightforward."

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

"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."

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

"I really like the auto-tuning, auto-scaling, and the automatic load balancing and query tuning in the system."

"It is a stable and scalable solution."

"I loved the simplicity of loading the data and simply relying on the self-tuning capabilities of ADW."

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

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

"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."

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

"It is a very stable tool...It is an extremely scalable tool."

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

"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."

"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."

"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."

"The solution hinges on Google patterns so continued improvement is important."

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

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

"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."

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

"Ease of connectivity could be improved."

"Optimization should be better."

"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."

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

"It is very important the integration with other platforms be made to be as easy as it is with an on-premises deployment."

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

"The setup is complex."

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

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

"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 operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

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

"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."

"BigQuery is inexpensive."

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

"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."

"On a scale from one to ten, where one is a low price and ten is a high price, I rate the pricing an eight."

"We pay approximately $70,000 per month. The cost includes maintenance and support."

"The solution is expensive."

"The cost is perfect with Oracle Universal credit."

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

"The solution's cost is reasonable."

"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."