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."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."
"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."
"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 feature called calibrating the capacity is valuable."
"The setup is simple."
"We like the machine learning features and the high-performance database engine."
"It's similar to a Hadoop cluster, except it's managed by Google."
"The most valuable feature of Teradata is the quick processing of large data."
"It has a solid set of tools and consulting services."
"Cuts time to process huge amounts of data with efficient analytical queries."
"Teradata features high productivity and reliability because it has several redundancy options, so the system is always up and running."
"Designing the database is easy."
"Teradata can be easily used in ETL mode transformations, so there is no need for expensive and inconvenient ETL tools"
"Improved performance of ETL procedures, reporting."
"Viewpoint, the detailed query logs and performance statistics are valuable features."
"We'd like to have more integrations with other technologies."
"So our challenge in Yemen is convincing many people to go to cloud services."
"The price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them."
"They could enhance the platform's user accessibility."
"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."
"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."
"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."
"Teradata hardly supports unstructured data or semi-structured data"
"The increasing volumes of data demand more and more performance."
"I would like to see more integration with many different types of data."
"The current operational approach needs improvement."
"The cloud is the new challenge and the new opportunity."
"Limited interest and success in some areas make us hesitate about upgrading."
"Teradata's UI could be more user-friendly."
"The solution is stable. However, there are times when we are using large amounts of data and we can see some latency issues."
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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