

Snowflake and BigQuery are leading cloud-based data warehousing solutions. While both offer strong features, Snowflake's scalability and performance give it an edge, yet BigQuery's machine learning integration enhances its appeal.
Features: Snowflake's strengths lie in its scalability, distributed architecture, and support for multi-format data access. It efficiently processes data, accommodating high storage capacity needs. BigQuery is celebrated for its speed and performance, with robust machine learning tools and SQL/NoSQL query handling, allowing efficient data management and innovation in machine learning.
Room for Improvement: Snowflake users seek improvements in spatial components, real-time analytics, integration options, pricing transparency, and user interface. BigQuery could enhance its integration flexibility, UI, query caching for external tables, and support for large-scale data transformations, also seeking cost reductions.
Ease of Deployment and Customer Service: Both Snowflake and BigQuery are easily deployed on public cloud services. Snowflake's customer service is responsive and expert, with occasional delay complaints. BigQuery offers robust service but faces minor criticisms regarding response times and localized data centers. Deployment is straightforward for both.
Pricing and ROI: Snowflake implements a flexible pricing model based on storage and processing, which some find complex. It offers cost efficiency through credit-based models. BigQuery, perceived as cost-effective for storage, charges based on data usage, potentially increasing costs for extensive data use. Users value both platforms, despite Snowflake's higher costs compared to BigQuery.
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 would rate their customer service pretty good on a scale of one to 10, as they gave me access to the platform on a grant.
I received great support in migrating data to Snowflake, with quick responses and innovative solutions.
I am satisfied with the work of technical support from Snowflake; they are responsive and helpful.
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.
Snowflake is very scalable and has a dedicated team constantly improving the product.
The billing doubles with size increase, but processing does not necessarily speed up accordingly.
Recently, Snowflake has introduced streaming capabilities, real-time and dynamic tables, along with various connectors.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
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.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
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.
What things you are going with to ask the support and how we manage the relationship matters a lot.
If more connectors were brought in and more visibility features were added, particularly around cost tracking in the FinOps area, it would be beneficial.
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.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
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.
We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.
I have used the Snowflake Zero-Copy Cloning feature in the past while prototyping data in lower environments. This feature is helpful as it saves a lot of time during the data replication process.
Snowflake is a data lake on the cloud where all processing happens in memory, resulting in very fast query responses.
| Product | Market Share (%) |
|---|---|
| Snowflake | 16.1% |
| BigQuery | 7.7% |
| Other | 76.2% |


| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 9 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 20 |
| Large Enterprise | 57 |
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 provides a modern data warehousing solution with features designed for seamless integration, scalability, and consumption-based pricing. It handles large datasets efficiently, making it a market leader for businesses migrating to the cloud.
Snowflake offers a flexible architecture that separates storage and compute resources, supporting efficient ETL jobs. Known for scalability and ease of use, it features built-in time zone conversion and robust data sharing capabilities. Its enhanced security, performance, and ability to handle semi-structured data are notable. Users suggest improvements in UI, pricing, on-premises integration, and data science functions, while calling for better transaction performance and machine learning capabilities. Users benefit from effective SQL querying, real-time analytics, and sharing options, supporting comprehensive data analysis with tools like Tableau and Power BI.
What are Snowflake's Key Features?
What Benefits Should You Look for?
In industries like finance, healthcare, and retail, Snowflake's flexible data warehousing and analytics capabilities facilitate cloud migration, streamline data storage, and allow organizations to consolidate data from multiple sources for advanced insights and AI-driven strategies. Its integration with analytics tools supports comprehensive data analysis and reporting tasks.
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