We performed a comparison between BigQuery and Snowflake Analytics 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 integrated data storage features are good."
"The most valuable features of BigQuery is that it supports standard SQL and provides good performance."
"We like the machine learning features and the high-performance database engine."
"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."
"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."
"The query tool is scalable and allows for petabytes of data."
"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."
"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 most valuable features of Snowflake for our data analytics are its time travel capability, allowing easy data recovery, and its automatic optimization of partitioning and clustering."
"One of the valuable features is the solution’s time travel capability. The solution is highly stable. The solution is highly scalable. The initial setup is straightforward, and the deployment process is quick and efficient. I recommend the solution. Overall, I rate it a perfect ten."
"The most valuable feature of Snowflake Analytics is its performance."
"It can run complex workloads with varied compute."
"Scalability-wise, I rate the solution a ten out of ten."
"I am impressed with the product's data-sharing feature."
"Scaling is very high – there's no problem for scaling purposes. The learning curve is very small. And there are a lot of advanced features like handling duplicates, security, data governance, data sharing, and data cloning."
"The advanced features like time travel, zero copy cloning and scalability have been most useful. Snowflake requires zero maintenance for Data Warehousing on the cloud system."
"The main challenges are in the areas of performance and cost optimizations."
"I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up."
"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."
"I rate BigQuery six out of 10 for affordability. It could be cheaper."
"The process of migrating from Datastore to BigQuery should be improved."
"They could enhance the platform's user accessibility."
"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'd like to see more local data residency."
"The platform's data governance space needs more capability."
"The product's cost is an area of concern where improvements are required."
"I cannot comment on the product's stability because we are still struggling with its performance."
"The scheduling of jobs requires improvement, particularly in terms of the user interface which currently lacks certain features found in comparable platforms."
"The tool should support EIM use cases. I guess the product is already working on it. I look forward to seeing inbuilt AI generative tools in the solution's future releases. The tool's price can be a little lower. The solution's on-premises support is also very limited. We have to rely on other support services to deploy it on-premises."
"We haven't seen any areas that are lacking."
"The solution’s interface is good but it could be improved."
"Snowflake's Snowpark is an area of concern where improvements are required."
BigQuery is ranked 5th in Cloud Data Warehouse with 31 reviews while Snowflake Analytics is ranked 6th in Cloud Data Warehouse with 30 reviews. BigQuery is rated 8.2, while Snowflake Analytics 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 Analytics writes "A scalable tool useful for data lake and data mining processes". BigQuery is most compared with Snowflake, Teradata, Oracle Autonomous Data Warehouse, Vertica and Apache Hadoop, whereas Snowflake Analytics is most compared with Azure Data Factory, Adobe Analytics, Mixpanel, Amplitude and Glassbox. See our BigQuery vs. Snowflake Analytics report.
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