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When unloading data in Snowflake, it is essential to differentiate between empty strings and NULL values to preserve data integrity. The parameters FIELD_OPTIONALLY_ENCLOSED_BY and EMPTY_FIELD_AS_NULL are used together to address this:

FIELD_OPTIONALLY_ENCLOSED_BY: This parameter specifies the character used to enclose fields, which can differentiate between empty strings (as enclosed fields) and NULLs.

EMPTY_FIELD_AS_NULL: By setting this parameter, Snowflake interprets empty fields as NULL values when unloading data, ensuring accurate representation of NULLs versus empty strings.

These parameters are crucial when exporting data for systems that need explicit differentiation between NULL and empty string values.

The COPY INTO <location> command in Snowflake allows you to export or unload data from a Snowflake table into an external stage (e.g., an Amazon S3 bucket) in a specific format like JSON, CSV, or Parquet. When you want to unload data as JSON using a SELECT statement, Snowflake provides built-in functions to format the data properly into JSON structures. Among the options provided:

TO_JSON:

Converts a variant, object, or array data type into its JSON text representation.

Commonly used when exporting JSON-formatted data with COPY INTO.

This is the correct function for generating JSON-formatted output while unloading.

Example Usage:

sql

CopyEdit

COPY INTO @my_stage/unload_data.json

FROM (SELECT TO_JSON(data_column) FROM my_table)

FILE_FORMAT = (TYPE = JSON);

Why the Other Options Are Incorrect:

TO_OBJECT: This function does not exist in Snowflake.

PARSE_JSON: This is used to parse a string into a variant data type representing a JSON object but is not used for exporting or creating JSON.

OBJECT_CONSTRUCT: Creates a JSON object from key-value pairs but does not convert data into JSON format for unloading.

SnowPro References:

COPY INTO Command Documentation

TO_JSON Function

Working with JSON Data in Snowflake

Snowflake's governance features vary across different editions, but the OBJECT_DEPENDENCIES view is supported by all Snowflake editions. This feature is part of Snowflake's Information Schema and is designed to help users understand the dependencies between various objects in their Snowflake environment.

The OBJECT_DEPENDENCIES view provides a way to query and analyze the relationships and dependencies among different database objects, such as tables, views, and stored procedures. This is crucial for governance, as it allows administrators and data engineers to assess the impact of changes, understand object relationships, and ensure proper management of data assets.

Object tags, masking policies, and row access policies are more advanced features that offer fine-grained data governance capabilities such as tagging objects for classification, dynamically masking sensitive data based on user roles, and controlling row-level access to data. These features may have varying levels of support across different Snowflake editions, with some features being exclusive to higher-tier editions.

The SHOW ORGANIZATION ACCOUNTS HISTORY command lists all accounts within the Snowflake organization, including dropped accounts that have not yet been permanently deleted.

Why Other Options Are Incorrect:

A. SHOW MANAGED ACCOUNTS: Invalid command.

B. SHOW ORGANIZATION ACCOUNTS: Shows active accounts, not historical ones.

D. SHOW ORGANIZATION ACCOUNTS LIKE 'myaccounts%': Filters active accounts only, not dropped ones.

References:

SHOW ORGANIZATION ACCOUNTS HISTORY Documentation