Publically launched in Oct 2014 for Cloud Data Warehouse, Snowflake data could have been introduced by 6,250 companies in about five years and is positioned as a leader in the 2019 Gartner Magic Quadrant’s “Data Management Solutions for Analytics” space. As a cloud service that combines overwhelming speed and ease of use, it is currently the most popular product in this genre. In particular, it has strengths in many concurrent processes, which are considered to be the weaknesses of cloud-based data warehouses, and it also has a high affinity with BI/analytics products.
Snowflake has petabyte-scale performance and can achieve up to 200 times faster speed. You can use it without degrading performance even if you connect to the same data at the same time. The data warehouse is automatically tuned, which can significantly reduce management man-hours.
Easy sizing change function including auto scale
In auto-scale mode, compute nodes are automatically added up to a user-defined maximum as the number of concurrent runs increases. When the number of concurrent executions decreases, the added compute node is automatically suspended, and the user can scale out and in without any operation.
It is possible to change the size of the compute node (XS, S, M, L, etc.) immediately from the screen operation without stopping the execution process.
Optimal Cost Design
It has an auto-resume function that enables automatic shutdown when not in use, and a function that caches queries for 24 hours, enabling optimal cost design such as not imposing a processing load on the compute node when the same query occurs.
In addition, regarding the cost, it is a model that charges only for what is used by up / down by auto-scale mode and sizing.
It automatically prepares the necessary resources at the necessary timing, so there is no wasteful cost.
- No need for over-provisioning
- Automatically scale up / down
- No capacity plan required
Since Snowflake guarantees a large number of connections with ETL products, BI products, and AI products, it is possible to use it with confidence in cooperation with existing products and products that will be used in the future.
Multi Platform Cloud Support
Snowflake outsourcing support cloud platforms such as AWS, GCP, and Azure. In the recent Big Data environment, data migration between clouds is extremely difficult, so it is possible to flexibly use Big Data by being able to support multiple clouds.
For example, if you already have a Big Data environment such as a data lake in the AWS environment, you can select the same AWS as the data lake in Snowflake so that Big Data data movement will not go out and will be smooth. For customers who have the following issues in existing data warehouse operation, Snowflake Outsourcing will support utilization proposals, introduction, operation, migration, etc. when using Snowflake. By having sufficient knowledge to build the environment around the data warehouse such as BI and ETL, we will provide support services that make the best use of the analysis platform using Snowflake.