Snowflake and BigQuery compete in the data warehousing category. Snowflake seems to have the upper hand due to its scalable architecture and advanced features like zero-copy cloning and time travel.
Features: Snowflake boasts a scalable architecture that allows for seamless scaling without downtime, supports various file formats like CSV and JSON, and includes features like zero-copy cloning and data time travel. BigQuery is integrated deeply within the Google ecosystem, offering swift query processing but with restrictions on specialized data types and transformations.
Room for Improvement: Snowflake requires better geo-spatial querying capabilities, simplification of the Snowpipe auto-ingest feature, and enhanced cost transparency. Additionally, its stored procedures are complex, and it lacks comprehensive built-in analytics tools. BigQuery struggles with handling special characters in datasets, lacks caching for external tables, needs self-optimization improvements, and presents a challenging learning curve.
Ease of Deployment and Customer Service: Snowflake offers consistent deployment across private and public clouds, providing flexible solutions but with initial customer support delays. BigQuery focuses on public cloud deployment within the Google Cloud, offering good customer service despite some delay due to limited support staffing. Deployment is facilitated by the integration within Google's services.
Pricing and ROI: Snowflake's credit-based, pay-as-you-go pricing model provides flexibility, though costs can be unpredictable and potentially high at scale. BigQuery's pricing is based on data processed, not time, providing a different cost structure. Both promise competitive ROI, but Snowflake tends to be more expensive in larger deployments.
rating the customer support at ten points out of ten
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
Snowflake is very scalable and has a dedicated team constantly improving the product.
Snowflake is very stable, especially when used with AWS.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
In general, if I know SQL and start playing around, it will start making sense.
Cost reduction is one area I would like Snowflake to improve.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
Snowflake's pricing is on the higher side.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
One key feature is the separation of compute and storage, which eliminates storage limitations.
BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
Snowflake is a cloud-based data warehousing solution for storing and processing data, generating reports and dashboards, and as a BI reporting source. It is used for optimizing costs and using financial data, as well as for migrating data from on-premises to the cloud. The solution is often used as a centralized data warehouse, combining data from multiple sources.
Snowflake has helped organizations improve query performance, store and process JSON and XML, consolidate multiple databases into one unified table, power company-wide dashboards, increase productivity, reduce processing time, and have easy maintenance with good technical support.
Its platform is made up of three components:
Snowflake has many valuable vital features. Some of the most useful ones include:
There are many benefits to implementing Snowflake. It helps optimize costs, reduce downtime, improve operational efficiency, and automate data replication for fast recovery, and it is built for high reliability and availability.
Below are quotes from interviews we conducted with users currently using the Snowflake solution:
Sreenivasan R., Director of Data Architecture and Engineering at Decision Minds, says, "Data sharing is a good feature. It is a majorly used feature. The elastic computing is another big feature. Separating computing and storage gives you flexibility. It doesn't require much DBA involvement because it doesn't need any performance tuning. We are not doing any performance tuning, and the entire burden of performance and SQL tuning is on Snowflake. Its usability is very good. I don't need to ramp up any user, and its onboarding is easier. You just onboard the user, and you are done with it. There are simple SQL and UI, and people are able to use this solution easily. Ease of use is a big thing in Snowflake."
A director of business operations at a logistics company mentions, "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."
A Solution Architect at a wholesaler/distributor comments, "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."
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.