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 stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"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."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"As a cloud solution, it's easy to set up."
"It's similar to a Hadoop cluster, except it's managed by Google."
"BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights."
"The initial setup is straightforward."
"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."
"The solution is self-securing. All data is encrypted and security updates and patches are applied automatically both periodically and off-cycle."
"I really like the auto-tuning, auto-scaling, and the automatic load balancing and query tuning in the system."
"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."
"The performance and scalability are awesome."
"The solution integrates well with Power BI."
"The product is easy to use."
"It is a very stable tool...It is an extremely scalable tool."
"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 initial setup could be improved making it easier to deploy."
"The process of migrating from Datastore to BigQuery should be improved."
"The main challenges are in the areas of performance and cost optimizations."
"It would be beneficial to integrate additional tools, particularly from a business intelligence perspective."
"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."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"There are some limitations in the query latency compared to what it was three years ago."
"They should make the solution more user-friendly."
"A lot of the tools that were previously there have now been taken away."
"The initial setup was pretty complex. It was not easy."
"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."
"The installation process is complex. Oracle can make the installation process better."
"An improvement for us would be the inclusion of support for an internal IP, so we could use it directly with the VCN in Oracle Cloud."
"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."
"The solution lacks visibility options."
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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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