We performed a comparison between BigQuery and Oracle Autonomous Data Warehouse based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It's straightforward to set up."
"The initial setup is straightforward."
"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 initial setup process is easy."
"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 query tool is scalable and allows for petabytes of data."
"The feature called calibrating the capacity is valuable."
"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 is a very stable tool...It is an extremely scalable tool."
"One advantage is that if you already have an Oracle Database, it easily integrates with that."
"I loved the simplicity of loading the data and simply relying on the self-tuning capabilities of ADW."
"Oracle Autonomous Data Warehouse is used globally to deliver extreme performance on large Financial data sets."
"The solution integrates well with Power BI."
"The performance and scalability are awesome."
"A very good integration feature that restricts access to unauthorized people."
"It is an extremely scalable solution since you can dynamically change the resources as some other cloud solutions."
"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."
"We'd like to have more integrations with other technologies."
"It would be beneficial to integrate additional tools, particularly from a business intelligence perspective."
"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
"Some of the queries are complex and difficult to understand."
"The solution hinges on Google patterns so continued improvement is important."
"I noticed recently it's more expensive now."
"They should make the solution more user-friendly."
"The installation process is complex. Oracle can make the installation process better."
"Ease of connectivity could be improved."
"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."
"There is a need for more storage to be allocated, but over a period of time, it becomes impossible to reduce it after using it."
"I would like to see Application Express and Oracle R Enterprise fully supported, and I would like to see Oracle Data Mining supported as a front end."
"The initial setup was pretty complex. It was not easy."
"It doesn't work well when you have unstructured data or you need online analytics. It is not as nice as Hadoop in these aspects."
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BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Oracle Autonomous Data Warehouse is ranked 10th in Cloud Data Warehouse with 16 reviews. BigQuery is rated 8.2, while Oracle Autonomous Data Warehouse is rated 8.6. The top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". On the other hand, the top reviewer of Oracle Autonomous Data Warehouse writes "A tool for data warehousing that offers scalability, stability, and ease of setup". BigQuery is most compared with Snowflake, Teradata, Vertica, Apache Hadoop and AWS Lake Formation, whereas Oracle Autonomous Data Warehouse is most compared with Oracle Exadata, Snowflake, Microsoft Azure Synapse Analytics, Amazon Redshift and Teradata. See our BigQuery vs. Oracle Autonomous Data Warehouse report.
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