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.
"Even non-coders can review the data in BigQuery."
"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."
"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."
"The integrated data storage features are good."
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"The product’s most valuable feature is its ability to manage the database on the cloud."
"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."
"The initial setup process is easy."
"It was relatively easy to use, and it was easy for people to convert to it."
"The syntax is advanced which reduces the time to write code."
"The adaptation to development languages is most valuable. Our developers can SQL code or something else. It has been convenient in that regard."
"It's ultra-fast at handling queries, which is what we find very convenient."
"Once you have finished your designs they can be easily imported to Snowflake and the information can be readily accessed without an IT expert."
"I like Snowflake's data exchange capabilities. It can exchange data with downstream systems and other vendor partners as well."
"It helped us to build MVP (minimum viable product) for our idea of building a data warehouse model for small businesses."
"The technical support is pretty good, particularly if you are a more technical user."
"There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."
"We'd like to have more integrations with other technologies."
"They could enhance the platform's user accessibility."
"It would be beneficial to integrate additional tools, particularly from a business intelligence perspective."
"The solution should reduce its pricing."
"The solution hinges on Google patterns so continued improvement is important."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"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."
"The user interface continues to be an issue, especially when we need to get data out of Snowflake. It's very easy to get data in, but it's not too easy to get it out or extract it."
"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."
"Some SQL language functions could be included."
"The product's performance could be improved."
"Snowflake can improve its machine learning and AI capabilities."
"If we can have a feature where the results can be moved to different tabs, so that I can compare the results with earlier queries before applying the changes, it would be great."
"If they could bring in some tools for data integration, it would be really great."
"The solution could improve the user interface and add functionality to the system."
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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