![]() ![]() Instead of supplying an object path for the COPY command, you supply the name of a JSON-formatted text file that explicitly lists the files to be loaded. The name is a string in double quotation marks. At least it does with python, but I am sure it works that way with other languages, etc… in snaplogic. ![]() Until recently, extracting data from JSON in Redshift was extremely cumbersome. If you do that, and treat it within the snaplogic designer GUI as if it is a normal value, you get the quoting that you mention. This tutorial shows you a new, easier way of working with JSON in Redshift. If you have an OBJECT that contains other objects, that can be arrays that contain arrays, etc… It will handle it in a way similar to any tool made to handle such things.īTW DON’T bother making the output variable in your script different. You don’t have to name it differently, use punctuation, etc… It will just KNOW, based on how the variable is defined in your script And if you pull the value into an appropriately defined field in your script, and modify it, you can pass the changed value out, as if you had done nothing with it… Any parts that came in, under that variable, that you never changed, will go out unchanged.Looker doesn't have a native JSON field type. Amazon Redshift database tutorial for Redshift JSON function jsonextractpathtext to parse and extract attributes from JSON string stored in table column. ![]() You'll want to use JSON parsing functions in SQL, like json_extract_path in Postgres and JSON_EXTRACT in BigQuery to extract the JSON and put it into a type that Looker can accept, like a string. NOTE: in BigQuery a JSON path must start with a $ followed by the index position and the string to parse, like: JSON_EXTRACT($.temperature_alerts, '$.description') There are a few Community articles where we have examples of doing this. Import people, objects, and relationships from an Amazon Redshift database. Part of AWS Collective 0 I'm attempting to parse out a json column with multiple nodes of data in the same chunk of json from a table in a relational database. Here is a cool open-source Python script which uses the Looker API to automatically detect JSON fields in the underlying database table and generates LookML for them (this is specific to Snowflake connections and may require adjusting to fit your use case). This reverse ETL integration makes sure that people in your workspace reflect. ![]()
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