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."The initial setup is straightforward."
"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."
"As a cloud solution, it's easy to set up."
"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 solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."
"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."
"Even non-coders can review the data in BigQuery."
"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."
"The solution integrates well with Power BI."
"The performance and scalability are awesome."
"The solution is self-securing. All data is encrypted and security updates and patches are applied automatically both periodically and off-cycle."
"It is a stable and scalable solution."
"I really like the auto-tuning, auto-scaling, and the automatic load balancing and query tuning in the system."
"It provides Transparent Data Encryption (TDE) capabilities by default to address data security issues."
"One advantage is that if you already have an Oracle Database, it easily integrates with that."
"Oracle Autonomous Data Warehouse is used globally to deliver extreme performance on large Financial data sets."
"We'd like to see more local data residency."
"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."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms."
"The processing capability can be an area of improvement."
"Some of the queries are complex and difficult to understand."
"We'd like to have more integrations with other technologies."
"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."
"It is very important the integration with other platforms be made to be as easy as it is with an on-premises deployment."
"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."
"Ease of connectivity could be improved."
"One of the major problem is creating custom tablespace. The ADB serverless option doesn't support custom tablespace creation, which could cause issues during on-premise database migration that requires specifically named tablespace. There should be an option to create customized tablespace."
"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 solution could be improved by allowing for migration tools from other cloud services, including migration from Amazon Redshift, RDS, and Aurora."
"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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