Snowflake and BigQuery compete in the cloud-based data warehousing category. User feedback suggests Snowflake has an edge due to its superior performance and feature set, despite its higher cost. BigQuery, however, is valued for cost-effectiveness and managing large datasets with ease.
Features: Snowflake supports scalability, performance, and flexibility with unique features such as time travel, easy scaling, and support for multiple file formats. BigQuery offers impressive storage capabilities, quick query handling, and integrates machine learning features.
Room for Improvement: Snowflake users seek better geospatial queries, an improved interface, and enhanced machine learning support. BigQuery needs improvements in handling special characters, better caching for external data, and greater cost optimization.
Ease of Deployment and Customer Service: Both are deployed on public clouds; however, Snowflake's support is known to be more responsive with comprehensive documentation. BigQuery's support is generally satisfactory, although there are occasional delays.
Pricing and ROI: Snowflake is viewed as complex and expensive, raising concerns about cost transparency despite flexibility advantages. BigQuery has a more straightforward pricing model that's affordable but has potential cost increases if not optimized. Snowflake offers a promising ROI but can be hard to evaluate due to its comprehensive nature.
rating the customer support at ten points out of ten
I have been self-taught and I have been able to handle all my problems alone.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
The technical support from Snowflake is very good, nice, and efficient.
It is a 10 out of 10 in terms of scalability.
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.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Snowflake is very scalable and has a dedicated team constantly improving the product.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
Snowflake is very stable, especially when used with AWS.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
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.
Enhancements in user experience for data observability and quality checks would be beneficial, as these tasks currently require SQL coding, which might be challenging for some users.
Cost reduction is one area I would like Snowflake to improve.
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data.
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.
The scalability options it provides, addressing issues without tying workloads into one virtual machine, enhance functionality.
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."
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