![]() ![]() In this case, the JSON string in the data key basically contains 16 rows of data, one for each forecasted day. create table demo_db.public.sample_dataįrom snowflake_sample_14_total Note that your result sets will be different from mine in this tutorial. Let’s also create a table with only that one record while we’re at it, so that we retrieve the same record throughout the tutorial, even across different Snowflake sessions, as the LIMIT function does not always return the same result. When we limit the rows in the query to a small. ![]() Let’s inspect the JSON first by retrieving a single row of data. I am facing an issue with handling large results from a query we pass into the snowflake connecter. There are several tables in the schema, but the table we’ll use is called DAILY_14_TOTAL. The data is contained in the schema WEATHER in the SNOWFLAKE_SAMPLE_DATA database. SNOWFLAKE JSON QUERY FREEJSON, XML).įor this tutorial, we will use the weather data sets which you get access to when you use the free trial, and which are stored in JSON format. One of my favourites I’ve encountered so far is the ability to easily query semi-structured data (e.g. I.e., I want the results of my query to look like: columnname1 valuegoeshere more values.for each row of data Each row will always have 'key': 'columnname1' and an associated value which can change. Heres how you can query a JSON column in. This gives the advantage of storing and querying unstructured data. What I also like are the many functionalities packed into its SQL dialect. Snowflake supports querying JSON columns. There’s a lot of things to like about Snowflake, such as its ease of use, scalability and performance. ![]()
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