We compared Snowflake and BigQuery based on our user's reviews in several parameters.
Snowflake is praised for its high performance, scalability, and ease of use, as well as its excellent customer service and reasonable pricing. On the other hand, BigQuery stands out for its robust scalability, efficient performance, seamless integration, and positive ROI. BigQuery users have also highlighted exceptional customer service and transparent pricing, while suggesting areas for improvement in optimization, performance, and integrations.
Features: Snowflake's most valuable features lie in its high performance, scalability, and ease of use. Users appreciate its ability to handle large volumes of data efficiently, with seamless scalability and a user-friendly interface. On the other hand, BigQuery is known for its robust scalability and efficient performance. It also offers seamless integration with other Google Cloud services, flexibility in handling large datasets, and a user-friendly interface.
Pricing and ROI: Snowflake's setup cost is appreciated for its reasonable and competitive pricing, straightforward process, and flexible licensing terms. In comparison, BigQuery boasts a minimal setup cost, enabling a quick and hassle-free implementation process, with a fair and transparent pricing structure. Both products accommodate various user needs and requirements., The user reviews indicate that Snowflake's ROI has been positive. BigQuery's ROI, on the other hand, has led to significant cost savings, improved data analysis capabilities, faster query speed, enhanced efficiency, increased productivity, better decision-making processes, and positive business growth.
Room for Improvement: Snowflake could benefit from enhancements to enhance user experience and functionality. User feedback for BigQuery suggests the need for better optimization and performance when handling larger datasets. Improving query execution time, enhancing reliability and stability, expanding integrations and supporting more data sources, simplifying the user interface, and providing intuitive documentation have been recommended for BigQuery to enhance user experience.
Deployment and customer support: The reviews indicate that for Snowflake, it is necessary to evaluate deployment and setup durations separately, considering different amounts of time spent on each phase, while for BigQuery, both deployment and setup durations should be taken into account depending on the context mentioned by users. No specific user quotes were provided for BigQuery., Snowflake's customer service has received positive feedback for its promptness, effectiveness, and expertise in resolving issues. Customers appreciate their responsiveness and willingness to address concerns. In comparison, BigQuery's customer service is highly praised for its responsiveness, helpfulness, and expertise in explaining and solving queries. Overall, both companies offer exceptional customer service and support.
The summary above is based on 71 interviews we conducted recently with Snowflake and BigQuery users. To access the review's full transcripts, download our report.
"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."
"It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."
"The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness."
"Even non-coders can review the data in BigQuery."
"The integrated data storage features are good."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"We like the machine learning features and the high-performance database engine."
"The interface is what I find particularly valuable."
"The Time Travel feature is helpful for accessing historical data and the ability to clone external tables is useful."
"The overall ecosystem was easy to manage. Given that we weren't a very highly technical group, it was preferable to other things we looked at because it could do all of the cloud tunings. It can tune your data warehouse to an appropriate size for controlled billing, resume and sleep functions, and all such things. It was much more simple than doing native Azure or AWS development. It was stable, and their support was also perfect. It was also very easy to deploy. It was one of those rare times where they did exactly what they said they could do."
"It requires no maintenance on our part. They handle all that. The speed is phenomenal. The pricing isn't really anything more than what you would be paying for a SQL server license or another tool to execute the same thing. We have zero maintenance on our side to do anything and the speed at which it performs queries and loads the data is amazing. It handles unstructured data extremely well, too. So, if the data is in a JSON array or an XML, it handles that super well."
"The ability to share the data and the ability to scale up and down easily are the most valuable features. The concept of data sharing and data plumbing made it very easy to provide and share data. The ability to refresh your Dev or QA just by doing a clone is also valuable. It has the dynamic scale up and scale down feature. Development and deployment are much easier as compared to other platforms where you have to go through a lot of stuff. With a tool like DBT, you can do modeling and transformation within a single tool and deploy to Snowflake. It provides continuous deployment and continuous integration abilities. There is a separation of storage and compute, so you only get charged for your usage. You only pay for what you use. When we share the data downstream with business partners, we can specifically create compute for them, and we can charge back the business."
"The tool is very easy to use. The solution’s desktop features are also very easy to use. Also, the product’s SQL-based connectivity is also good. It can connect with any tool."
"The best thing about Snowflake is its flexibility in changing warehouse sizes or computational power."
"From a data warehouse perspective, it's an excellent all-round solution. It's very complete."
"Time travel is one feature that really helps us out."
"I noticed recently it's more expensive now."
"The processing capability can be an area of improvement."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"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."
"We'd like to see more local data residency."
"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."
"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."
"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."
"The scheduling system can definitely be better because we had to use external airflow for that. There should be orchestration for the scheduling system. Snowflake currently does not support machine learning, so it is just storage. They also need some alternatives for SQL Query. There should also be support for Spark in different languages such as Python."
"There is a need for improvements in the documentation, this would allow more people to switch over to this solution."
"Snowflake has to improve their spatial parts since it doesn't have much in terms of geo-spatial queries."
"The solution could use a little bit more UI."
"The cost is a bit high."
"I think that Snowflake could improve its user interface. The current one is not interactive."
"The solution should offer an on-premises version also. We have some requirements where we would prefer to use it as a template."
"If you go with one cloud provider, you can't switch."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 92 reviews. BigQuery is rated 8.2, while Snowflake is rated 8.4. 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 Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". BigQuery is most compared with Teradata, Oracle Autonomous Data Warehouse, Vertica, Apache Hadoop and AWS Lake Formation, whereas Snowflake is most compared with Azure Data Factory, Teradata, Vertica, AWS Lake Formation and Amazon EMR. See our BigQuery vs. Snowflake report.
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