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An Architect for a multi-national transportation company has a system that is used to check the weather conditions along vehicle routes. The data is provided to drivers. The weather information is delivered regularly by a third-party company and this information is generated as JSON structure. Then the data is loaded into Snowflake in a column with a VARIANT data type. This table is directly queried to deliver the statistics to the drivers with minimum time lapse. A single entry includes (but is not limited to): - Weather condition; cloudy, sunny, rainy, etc. - Degree - Longitude and latitude - Timeframe - Location address - Wind The table holds more than 10 years' worth of data in order to deliver the statistics from different years and locations. The amount of data on the table increases every day. The drivers report that they are not receiving the weather statistics for their locations in time. What can the Architect do to deliver the statistics to the drivers faster?
Correct Answer: B
To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake's search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates. References: The solution is supported by Snowflake documentation that details how search optimization can enhance query performance for semi-structured data1.