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Consequently, we adjusted our processes to use Matillion Data Productivity Cloud only for extraction and ingestion, while Snowflake handled all transformations and jobs.
Snowflake undoubtedly cost us more money and provided almost no value to us.
We experience savings of about 15%.
They communicate effectively and respond quickly to all inquiries.
We sought this documentation multiple times but faced difficulty in obtaining it.
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.
Depending on the nature of data sets, volume, and mixture of different data, the scalability could be improved as manual code writing is still required.
The autoscale process works well, allowing the system to start another node automatically if the first machine reaches 80% capacity.
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 highly stable and performs well even with large data sets exceeding terabytes, maintaining stability throughout.
Snowflake is very stable, especially when used with AWS.
Snowflake as a SaaS offering means that maintenance isn't an issue for me.
Connections to BigQuery for extracting information are complex.
The main areas for improvement are AI features and scalability.
A specific example is when they told us they fully support Iceberg, and then they have over three thousand words of bullet point text with a link to reference articles with all the limitations of their Iceberg support.
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.
Matillion Data Productivity Cloud offers discounts and special deals, especially when dealing with high-volume clients or fewer existing clients in specific regions, like Spain.
The pricing is moderate, neither expensive nor cheap.
When it comes to cloud support, the setup cost is very cheap compared to other platforms, such as Oracle or PostgreSQL, which typically require higher costs.
Snowflake's pricing is on the higher side.
Snowflake lacks transparency in estimating resource usage.
The predefined connectors eliminate the need to write code for connectivity.
Matillion Data Productivity Cloud is effective for ingest functions, particularly when moving information to Snowflake and performing many transformations.
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 has contributed to significant cost savings.
| Product | Mindshare (%) |
|---|---|
| Matillion Data Productivity Cloud | 4.6% |
| AWS Glue | 7.4% |
| AWS Database Migration Service | 7.2% |
| Other | 80.8% |
| Product | Mindshare (%) |
|---|---|
| Snowflake | 15.4% |
| Databricks | 9.3% |
| Teradata | 9.2% |
| Other | 66.1% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 9 |
| Large Enterprise | 12 |
| Company Size | Count |
|---|---|
| Small Business | 31 |
| Midsize Enterprise | 20 |
| Large Enterprise | 61 |
Matillion Data Productivity Cloud offers a user-friendly platform for seamless integration and dynamic data handling, favored for simplifying ETL processes with minimal coding and ensuring robust performance in complex data tasks.
Matillion Data Productivity Cloud integrates effortlessly with platforms like AWS, Snowflake, and SQL databases, providing tools for efficient data migration, transformation, and cloud warehousing. It supports large datasets with swift management, making it valued for its graphical interface that eases ETL processes for non-technical users. Automation features ensure scalability and dynamic data handling across diverse sources, while security and cost-effectiveness enhance its appeal. Enhancements in database connectivity, interface design, and multi-environment support would refine user experience, with growing demands for real-time data capture, SAP connectivity, and frequent API updates.
What are the most important features?In industries like finance, healthcare, and retail, Matillion Data Productivity Cloud is implemented for transforming data operations. Companies leverage it for its speed in data processing and integration capability, facilitating rapid adaptation to data-driven insights crucial in these sectors.
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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