We performed a comparison between Amazon Redshift and BigQuery 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 most valuable feature is that the solution is fully embedded in the AWS stack."
"The solution has very competitive pricing."
"It's very easy to migrate from other databases to Redshift. There are migration tools dedicated for this purpose, enabling migration from other databases like MS SQL directly to Redshift. The majority of the scripts will be automatically transposed."
"The most valuable feature is the scalability, as it grows according to our needs."
"Though Amazon Redshift is good, it depends on what kind of business you're trying to do, what type of analytics you need, and how much data you have."
"The processing of data is very fast."
"Setup is easy. It's a fast solution with machine learning features, good integration, and a good API."
"The feature that we find most useful is the ability to do analytics on the fly."
"One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions."
"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."
"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 feature called calibrating the capacity is valuable."
"The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements."
"As a cloud solution, it's easy to set up."
"The most valuable features of BigQuery is that it supports standard SQL and provides good performance."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"When working with third-party services requires additional integrations and configurations, which can sometimes add more cost."
"Running parallel queries results in poor performance and this needs to be improved."
"It lacks a few features which can be very useful, such as stored procedures"
"The customer support could be more responsive."
"The refreshment rate of data reaching Redshift from other sources should be faster."
"The product must provide new indexes that support special data structures or data types like TEXT."
"The solution could improve in handling more data formats and more native support for RDF."
"In the next release, a pivot function would be a big help. It could save a lot of time creating a query or process to handle operations."
"The process of migrating from Datastore to BigQuery should be improved."
"The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"So our challenge in Yemen is convincing many people to go to cloud services."
"The solution should reduce its pricing."
"The product’s performance could be much faster."
"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."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
Amazon Redshift is ranked 4th in Cloud Data Warehouse with 61 reviews while BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews. Amazon Redshift is rated 7.8, while BigQuery is rated 8.2. The top reviewer of Amazon Redshift writes "Provides one place where we can store data, and allows us to easily connect to other services with AWS". On the other hand, the top reviewer of BigQuery writes "Expandable and easy to set up but needs more local data residency". Amazon Redshift is most compared with Teradata, Snowflake, AWS Lake Formation, Vertica and Microsoft Azure Synapse Analytics, whereas BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and Apache Hadoop. See our Amazon Redshift vs. BigQuery report.
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We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
