One of the great strengths of Snowflake is the analysis of semi-structured data. You can load semi-structured data directly and unchanged into Snowflake.
Classic warehouses were not designed for semi-structured data. In Snowflake, they can be stored right next to structured data. Another big advantage is that you can evaluate the semi-structured data directly with SQL. No special additions or skills are required: SQL knowledge and a little basic understanding of the semi-structured data are completely sufficient.
In addition, the data can be combined very easily with structured data. This enables a simple evaluation taking into account both data worlds. Selecting the cloud data platform provider that best suits your business objectives is the first step in implementing a Snowflake data warehouse.
India Snowflake Implementation is an exciting solution with great potential. The solution has been on the market since 2015 and is therefore still relatively new. There are currently 12 new functions in preview mode. This means that the solution here is in constant development. The independence from its own data center makes the solution very attractive. As mentioned above, there is no need to buy or provide hardware. You can also shorten the implementation time of a data warehouse project. The following further conclusions can already be made after the first phase.
- The performance while working with the Snowflake test data was incredibly impressive. One can ask the critical question whether this performance is also achieved in project practice. But we are cautiously optimistic here. You can only finally judge this after relevant practical experience.
- The administration or construction of the solution could be done completely independently of an SQL developer. Snowflake offers various wizards which, for example, allow a user to upload data or to authorize new people for Snowflake. This enables even technically less experienced people to get started with the solution. The full potential of the solution only comes into play with SQL.
- Data visualization with Microsoft Power BI can only be done using an import mode or direct query. When talking about a big data solution, the maximum capacity of 1 million rows of the result set by Power BI for a direct query appears to be relatively tight. Even if you import large amounts of data into the model via import mode, the capacity limit of 1 GB can be reached very quickly. In a proof of concept, however, we were already able to implement Microsoft Azure Analysis Services as a layer between Snowflake and Power BI. This opens up new possibilities.
How does the Snowflake payment model work?
There are two ways to pay for using the Snowflake service.
- You can pay for the service according to the “on demand” principle.
- The alternative is called “pre-paid capacity”.
In the “Pre-paid Capacity,” the price of the credits is calculated at the time of the order. This depends on the size of the entire purchase. The greater the credit volume purchased, the cheaper the individual credits will be. Only a Snowflake seller can give binding price statements.