We performed a comparison between BigQuery and Dremio 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 process is easy."
"The most valuable features of BigQuery is that it supports standard SQL and provides good performance."
"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."
"There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."
"The product’s most valuable feature is its ability to manage the database on the cloud."
"It has a well-structured suite of complimentary tools for data integration and so forth."
"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."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."
"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"We primarily use Dremio to create a data framework and a data queue."
"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."
"Dremio allows querying the files I have on my block storage or object storage."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"The solution hinges on Google patterns so continued improvement is important."
"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 product’s performance could be much faster."
"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."
"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."
"It shows errors sometimes."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."
"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Dremio is ranked 11th in Cloud Data Warehouse with 6 reviews. BigQuery is rated 8.2, while Dremio 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 Dremio writes "It enables you to manage changes more effectively than any other platform". BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and SAP Business Warehouse, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and AtScale Adaptive Analytics (A3). See our BigQuery vs. Dremio 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.
