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.
"It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions."
"It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."
"The interface is what I find particularly valuable."
"We like the machine learning features and the high-performance database engine."
"It has a well-structured suite of complimentary tools for data integration and so forth."
"When integrating their system into the cloud-based solutions, we were able to increase their efficiency and overall productivity twice compared with their on-premises option."
"The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."
"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 initial setup is very simple."
"I like the idea that you can assign roles and responsibilities, limiting access to data."
"The most valuable features are the clustering, LS50, being able to change the size, the pay per use feature, the flexibility with many different sources and analytic applications."
"The solution speeds up the process of onboarding."
"The most valuable feature has been the Snowflake data sharing and dynamic data masking."
"It is a highly scalable solution. There is no limit on storage or computing."
"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 initial setup is straightforward. You just need to follow the documentation."
"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."
"So our challenge in Yemen is convincing many people to go to cloud services."
"For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options."
"We'd like to have more integrations with other technologies."
"As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations."
"We'd like to see more local data residency."
"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."
"It's not that flexible when compared to Oracle."
"To ensure the proper functioning of Snowflake as an MDS, it relies heavily on other partner tools."
"There are some challenges with loading unstructured data and integrating some message queues or brokers. In one project, we had a problem connecting to one of the message queues and we had to take a different route altogether on Microsoft Azure."
"Snowflake can improve its machine learning and AI capabilities."
"The solution needs more connectors."
"It would be helpful if Snowflake could create good reports instead of using Power BI reports."
"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."
"Snowflake could improve migration. It should be made easier. It would be beneficial if it could offer some OLTP features. One of our customers was using Oracle for both data warehousing and OLTP workloads, and they were able to migrate their data warehousing workloads to Snowflake without major issues. However, for some of their OLTP requirements, such as needing a response time of fewer than 10 milliseconds for certain queries, Snowflake is currently unable to provide that."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Snowflake is ranked 1st in Cloud Data Warehouse with 94 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 Oracle Autonomous Data Warehouse. See our BigQuery vs. Snowflake report.
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