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 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."
"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."
"The integrated data storage features are good."
"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."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"The solution is self-securing. All data is encrypted and security updates and patches are applied automatically both periodically and off-cycle."
"The product is easy to use."
"The analytics have been very good. We've found them to be quite useful."
"I loved the simplicity of loading the data and simply relying on the self-tuning capabilities of ADW."
"It is a stable and scalable solution."
"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 primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms."
"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."
"The solution should reduce its pricing."
"The initial setup could be improved making it easier to deploy."
"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."
"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."
"The process of migrating from Datastore to BigQuery should be improved."
"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."
"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 installation process is complex. Oracle can make the installation process better."
"I would like to see an on-premise solution in the future."
"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 initial setup was pretty complex. It was not easy."
"My main suggestion for Oracle is the configuration and key values that come for JSON files. When we create a table, especially if you see in our RedShift or some other stuff, if I create a table on top of a JSON file with multiple array columns or superset columns, those column values create some difficulty in Oracle."
"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."
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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 Snowflake, Oracle Exadata, Microsoft Azure Synapse Analytics, Amazon Redshift and Teradata. See our BigQuery vs. Oracle Autonomous Data Warehouse report.
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