Json snowflake query11/13/2023 To access the command line on Windows, you'll need the PowerShell application. If you don't have the experience to dive into other solutions such as Node.js or Python, the chances are you've still used the command-line before, making jq an ideal tool for what we want to achieve. You can use it to slice and filter and map and transform structured data with the same ease that sed, awk, grep and friends let you play with text." Jq, in its own words, is "a lightweight and flexible command-line JSON processor. Here's an overview of some of your options to get you started on the right path. There are a wide variety of techniques you can use to split up JSON files. ![]() You now know the why, so it's time for the how! Removes the outer array structure and loads the records into separate table rows. Snowflake instead recommends that you enable the STRIP_OUTER_ARRAY file format option for the COPY INTO command. You need to be aware of this size limit on your rows when you load your newly split files into Snowflake. The 16 MB size limit is a critical point to remember when you think of your monstrous JSON file. This should make it your default choice when you look to import and operate on semi-structured data within Snowflake. In effect, it can hold up to 16 MB of any data type that Snowflake supports. Why? – The VARIANT data type is described by Snowflake as "a tagged universal type, which can store values of any other type, including OBJECT and ARRAY, up to a maximum size of 16 MB compressed." You'll likely end up using the VARIANT data type more often though. JSON can be stored inside Snowflake in a few different ways. When loading data into Snowflake, it's recommended to split large files into multiple smaller files - between 10MB and 100MB in size - for faster loads. Here are the key points you need to consider ahead of loading it into Snowflake. Now that you understand at a high-level how you _could _optimise your warehouse, it's time to look at your data.
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