

Snowflake Analytics and BigQuery compete in the cloud-based data warehousing and analytics sector. Snowflake seems to have an advantage with its scalability and robust cloud flexibility, while BigQuery stands out for its cost efficiency and rapid processing capabilities.
Features: Snowflake Analytics is notable for its automated infrastructure management, time travel, and secure data warehousing. It offers high scalability and flexibility across AWS, Azure, and Google Cloud. BigQuery excels in cost-efficient storage, rapid data processing, and seamless integration with Google's AI and machine learning tools.
Room for Improvement: Snowflake users have expressed a desire for better integration with machine learning tools and improved cost transparency. Enhancements in on-premises solutions are also needed. BigQuery users face challenges with character restrictions, caching inconsistencies, and a complex setup process, and they also seek improvements in cost optimization and local data residency.
Ease of Deployment and Customer Service: Both Snowflake and BigQuery operate on public cloud platforms. Snowflake is praised for its responsive support but may have delays. BigQuery's customer service is seen positively, with organizations often managing their own support, and it offers strong documentation supported by Google's infrastructure.
Pricing and ROI: Snowflake's pricing model is flexible, aligning costs with usage by separating compute and storage, though users find it expensive. BigQuery is considered more affordable due to its cost-effective storage and execution models, but its pricing can escalate if not managed carefully. Snowflake provides time savings contributing to ROI, while BigQuery's pay-as-you-go model is budget-friendly.
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.
The Snowflake Analytics documentation is excellent.
Recently we had a two-day session where the Snowflake Analytics team provided a demo on Cortex AI and its features.
The technical support for Snowflake Analytics is excellent based on what I have heard from others.
It is a 10 out of 10 in terms of scalability.
We have not seen problems with scaling.
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.
Storage is unlimited because they use S3 if it is AWS, so storage has no limit.
It supports both horizontal and vertical scaling effectively.
Maintaining security and data governance becomes easier with an entire data lake in place, and the scalability improves performance.
In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google.
Snowflake Analytics has been stable and reliable in my experience.
Snowflake Analytics is stable, scoring around eight point five to nine out of ten.
The Power BI team raised tickets for both Power BI and Snowflake Analytics, and their responses were very good.
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.
AIML-based SQL prompt and query generation could be an area for enhancement.
If it offered flexibility similar to Oracle and supported more heterogeneous data sources and database connectivity, it would be even better.
I would prefer Snowflake Analytics to improve their support response times, as sometimes the responses we receive are not very prompt and ticket assignments may not be timely.
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 charges per query, which amounts to a very minor cost, such as $0.015 per query.
Snowflake is better and cheaper than Redshift and other cloud warehousing systems.
Snowflake Analytics is quite economical.
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.
Running a considerable query on Microsoft SQL Server may take up to thirty minutes or an hour, while Snowflake executes the same query in less than three minutes.
Snowflake Analytics supports data security with a single sign-on feature and complies with framework regulations, which is highly beneficial.
It is a data offering where I can see data lineage, data governance, and data security.
| Product | Mindshare (%) |
|---|---|
| BigQuery | 7.4% |
| Snowflake Analytics | 3.2% |
| Other | 89.4% |


| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 9 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 13 |
| Large Enterprise | 21 |
BigQuery is a powerful cloud-based data warehouse offering advanced SQL querying, seamless Google integration, and scalable handling of large datasets. Its serverless architecture and built-in AI capabilities facilitate efficient data processing and insights extraction.
BigQuery provides an efficient data analysis platform with low-latency performance and cost-effective on-demand pricing. Leveraging Google's cloud infrastructure for data storage, it offers robust security and high availability. While it excels in SQL support and caching features, it can improve on user accessibility, integration with diverse tools, and machine learning feature expansion. Making it more accessible for smaller entities through improved cost management and local data compliance is essential. Enhancements in query speed and intuitive interfaces can further optimize performance.
What features are offered by BigQuery?In industries like healthcare, finance, and marketing, BigQuery is extensively used for data storage, generating reports, and supporting ETL processes. Educational institutions leverage it for analytics, aligning seamlessly with Google Cloud for serverless infrastructure efficiencies.
Snowflake Analytics offers advanced capabilities in data warehousing and cloud data migration, with support for machine learning and business intelligence tasks. Its scalable architecture supports large data volumes while enhancing cost efficiency through decoupled computation and storage.
As a flexible, managed environment, Snowflake Analytics enhances data sharing and integration across multiple cloud platforms. It allows seamless data pipeline creation, supports advanced analytics, and facilitates reporting and visualization. Despite facing integration challenges with legacy systems and complex queries, Snowflake's continuous improvements aim to address these issues, making it a reliable choice for organizations transitioning to the cloud.
What features define Snowflake Analytics?Enterprises across industries utilize Snowflake Analytics for its robust data handling and cloud integration capabilities. It serves sectors in need of efficient data warehousing, real-time analytics, and machine learning support, making it suitable for cloud migration and enhancing business intelligence operations.
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