We performed a comparison between BigQuery and Teradata 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."We like the machine learning features and the high-performance database engine."
"The initial setup is straightforward."
"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."
"It has a well-structured suite of complimentary tools for data integration and so forth."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"It's similar to a Hadoop cluster, except it's managed by Google."
"The initial setup process is easy."
"The most valuable features are the large volume of data and the structuring of the data to optimize it and get very optimal data warehouse solutions for customers."
"Teradata's capabilities enhance data management efficiency, support scalability, and contribute to faster query performance."
"Things have started moving faster in my company, such as data retrieval happens more quickly."
"It's a pre-configured appliance that requires very little in terms of setting-up."
"I found all parts --loading, transformation, processing & querying work in parallel, and end-to-end-- to be valuable."
"It's very mature from a technology perspective."
"It has reduced a lot of reworking on maintaining indexes, partitions, etc."
"We really enjoy the FastLoad, TPump, and MultiLoad features."
"It would be beneficial to integrate additional tools, particularly from a business intelligence perspective."
"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."
"We'd like to see more local data residency."
"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."
"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."
"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."
"We'd like to have more integrations with other technologies."
"We would like to be able to calibrate the solution to run on top of a raw file."
"Query language and its functionality are rather limited, compared to Oracle or even SQL Server. However, it is possible to perform any kind of logic in it (though some workarounds may be required)."
"I'm not sure about the unstructured data management capabilities. It could be improved."
"Data synchronization to the DR site."
"Sometimes the large injestion takes days to load data, and some of our stored procedures take two to three days."
"It would help to make scaling easier with a reduced cost. "
"The capability to implement it with comparable performance across various private cloud environments, ensuring adaptability to different infrastructure setups would be beneficial."
"The solution needs improvement in its stability, support and pricing."
"It's primarily designed for big projects and therefore, the pricing is pretty high. It's not suitable for smaller companies."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Teradata is ranked 3rd in Data Warehouse with 54 reviews. BigQuery is rated 8.2, while Teradata is rated 8.2. 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 Teradata writes "Offers seamless integration capabilities and performance optimization features, including extensive indexing and advanced tuning capabilities". BigQuery is most compared with Snowflake, Oracle Autonomous Data Warehouse, Vertica, Apache Hadoop and AWS Lake Formation, whereas Teradata is most compared with SQL Server, Snowflake, Oracle Exadata, MySQL and Amazon Redshift. See our BigQuery vs. Teradata report.
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