

Snowflake and DATPROF compete in the data management and processing software category. Snowflake seems to have the upper hand due to its advanced scalability and integration with various cloud platforms.
Features: Snowflake offers exceptional scalability, advanced distributed architecture, and multi-formatted data accessibility. Its fully managed infrastructure provides seamless upgrades without user intervention. DATPROF excels in data privacy control with strong masking and subsetting features, along with capabilities in generating and anonymizing test data to address compliance needs.
Room for Improvement: Snowflake could enhance its spatial component integration and improve the Snowpipe auto-ingest feature. There's also a need for better pricing transparency and enhanced analytical functions. DATPROF could benefit from stronger API integration, support for more data formats, and improved reusability in template implementations to streamline setup processes.
Ease of Deployment and Customer Service: Snowflake supports multi-cloud deployment, offering flexibility, while DATPROF is primarily an on-premises solution. Snowflake provides responsive technical support but lacks SLAs, whereas DATPROF offers strong, though not as immediate, support. Snowflake's comprehensive documentation aids in self-service deployments, compared to DATPROF’s consultant-led approach.
Pricing and ROI: Snowflake operates on a pay-as-you-go model, which can be complex but is cost-effective for larger enterprises justifying its expenses with scalability and performance. DATPROF presents a straightforward pricing model, though with room for more flexible licensing. It offers good value in data masking and testing environments despite its lower cost.
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.
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.
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.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
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 | 5.3% |
| DATPROF | 5.2% |
| Other | 89.5% |


| Company Size | Count |
|---|---|
| Small Business | 2 |
| Large Enterprise | 8 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 20 |
| Large Enterprise | 58 |
DATPROF offers test data management solutions known for subsetting, easy maintenance, intelligent data modeling, and data protection features, suitable for businesses requiring efficient data handling and compliance across operations.
DATPROF is renowned for capabilities including collaboration without heavy supplier reliance, synthetic data generation, and data masking across applications. Its features allow flexible customization, integration with systems, and scalable masking. Users benefit from its intuitive design and efficient data management while seeking improvements in orchestration features, enhancing discovery-to-masking integration, and expanding APIs. Addressing requirements like template reusability, empty database functionality, and mainframe platform compatibility are focus areas. Suggestions for better synthetic data generation and mainframe alliances highlight future growth potential.
What are DATPROF's most valuable features?In industries requiring stringent data protection such as finance and healthcare, DATPROF is crucial for managing test databases, meeting CI/CD targets, and ensuring GDPR compliance. Teams leverage DATPROF for transitioning to cloud environments, masking sensitive data, and creating synthetic data, thereby supporting diverse IT environments.
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.
We monitor all AI Synthetic Data 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.