JSON file handling in Snowflake is a powerful feature that enables seamless data loading and unloading operations for semi-structured data. Snowflake natively supports JSON format, treating it as a VARIANT data type that can store up to 16 MB of data per row. When loading JSON files, you can use th…JSON file handling in Snowflake is a powerful feature that enables seamless data loading and unloading operations for semi-structured data. Snowflake natively supports JSON format, treating it as a VARIANT data type that can store up to 16 MB of data per row. When loading JSON files, you can use the COPY INTO command with the FILE_FORMAT option specifying TYPE = JSON. Key parameters include STRIP_OUTER_ARRAY, which removes the outer brackets from JSON arrays, and STRIP_NULL_VALUES, which eliminates null value pairs from the data. For loading, JSON files can be staged in internal stages (user, table, or named stages) or external stages (AWS S3, Azure Blob, or Google Cloud Storage). The PUT command uploads local JSON files to internal stages, while external stages reference cloud storage locations. During the load process, Snowflake can automatically detect and handle nested JSON structures. You can query JSON data using dot notation or bracket notation to access nested elements, and the FLATTEN function helps transform nested arrays into relational rows. For unloading JSON data, the COPY INTO location command exports table data to JSON format. You can customize the output using FILE_FORMAT options and specify compression types like GZIP or BZIP2 to reduce storage costs and transfer times. Best practices include validating JSON structure before loading using VALIDATION_MODE parameter, which checks data for errors before actual ingestion. The ON_ERROR option controls behavior when encountering malformed records - options include CONTINUE, SKIP_FILE, or ABORT_STATEMENT. Snowflake also supports querying staged JSON files before loading using the SELECT statement with the dollar sign notation, allowing you to preview and transform data during the loading process. This capability makes data exploration and schema inference straightforward for JSON datasets.
JSON File Handling in Snowflake
Why JSON File Handling is Important
JSON (JavaScript Object Notation) is one of the most common data formats used in modern applications, APIs, and data pipelines. Understanding how Snowflake handles JSON files is crucial for data engineers and analysts who need to load semi-structured data into Snowflake for analysis. The SnowPro Core exam tests your knowledge of loading, querying, and transforming JSON data effectively.
What is JSON File Handling in Snowflake?
JSON file handling refers to the processes and features Snowflake provides to: - Load JSON data from files into tables - Store JSON data in the VARIANT data type - Query and extract values from JSON structures - Transform JSON data into relational formats
Snowflake natively supports semi-structured data, making it exceptionally powerful for working with JSON compared to traditional databases.
How JSON File Handling Works
1. File Format Specification Create a file format for JSON data:
CREATE FILE FORMAT json_format TYPE = 'JSON' STRIP_OUTER_ARRAY = TRUE STRIP_NULL_VALUES = FALSE;
2. Loading JSON Data Use the COPY INTO command to load JSON files:
COPY INTO my_table FROM @my_stage/data.json FILE_FORMAT = json_format;
3. Key File Format Options for JSON: - STRIP_OUTER_ARRAY: Removes the outer array brackets, loading each element as a separate row - STRIP_NULL_VALUES: Removes null values from the loaded data - COMPRESSION: Specifies compression type (AUTO, GZIP, BZ2, etc.) - ALLOW_DUPLICATE: Allows duplicate keys in JSON objects - ENABLE_OCTAL: Enables parsing of octal numbers
4. Querying JSON Data Access JSON elements using colon notation and bracket notation:
SELECT json_column:name::STRING AS name, json_column:address.city::STRING AS city, json_column['phone-number']::STRING AS phone FROM my_table;
5. Flattening JSON Arrays Use FLATTEN to convert arrays into rows:
SELECT f.value:item_name::STRING AS item FROM my_table,LATERAL FLATTEN(input => json_column:items) f;
Important Functions for JSON Handling
- PARSE_JSON(): Converts a string to VARIANT - TRY_PARSE_JSON(): Safe version that returns NULL on error - TO_JSON(): Converts VARIANT to JSON string - GET(): Extracts a value from an object or array - GET_PATH(): Extracts a value using a path expression - OBJECT_KEYS(): Returns an array of keys from a JSON object - ARRAY_SIZE(): Returns the size of a JSON array
Exam Tips: Answering Questions on JSON File Handling
Tip 1: Remember that JSON data is stored in the VARIANT data type, which can hold up to 16 MB of data per value.
Tip 2: Know the difference between colon notation (for simple keys) and bracket notation (for keys with special characters or spaces).
Tip 3: STRIP_OUTER_ARRAY = TRUE is commonly used when your JSON file contains an array of objects and you want each object as a separate row.
Tip 4: When casting JSON values, always use the double-colon (::) notation to convert VARIANT to specific data types like STRING, NUMBER, or TIMESTAMP.
Tip 5: FLATTEN is essential for working with nested arrays - expect questions about lateral joins with FLATTEN.
Tip 6: The INFER_SCHEMA function can automatically detect the schema of JSON files in a stage before loading.
Tip 7: Remember that Snowflake automatically compresses VARIANT data, making storage efficient for JSON documents.
Tip 8: For exam scenarios involving error handling during JSON loads, know that ON_ERROR options (CONTINUE, SKIP_FILE, ABORT_STATEMENT) apply to JSON loading just like other file types.