![]() Snowflake functionality makes it possible to process semi-structured data. The process of loading data into a database can be a cumbersome task but with Snowflake, this can be done easily. By using :: notation, we define the end data type of the values being retrieved. The command works a lot like JavaScript, except we use : notation to retrieve the category for each row. Snowflake is extremely powerful when it comes to querying semi-structured data. Click on load data above it.Ĭheck that the data was properly loaded (SELECT from COLORS). Click on your database and then find your way to the table. You can accomplish the same thing by using Snowflake UI under the database tab. PUT file:///sample.json COPY INTO "TEST_DATABASE"."TEST_SCHEMA"."COLORS" FROM FILE_FORMAT = '"TEST_DATABASE"."TEST_SCHEMA"."JSON_FILE_FORMAT"' ON_ERROR = 'ABORT_STATEMENT' I’ll be using the Snowflake UI to do it under the database tab. You can run this command from the Snowflake CLI client. PUT command fetches data from local storage to snowflake internal stages. You can use below command to list files in stages: LIST & COPY Command CREATE STAGE IF NOT EXISTS JSON_STAGE FILE_FORMAT = JSON_FILE_FORMAT In this article, we have used a Snowflake internal stage and created a dedicated stage for semi-structured load. If you do not have any cloud platform, Snowflake provides space to store data into its cloud environment called – “Snowflake Internal stage”. In order to copy the data to a Snowflake table, we need data files in the cloud environment. If we did not strip the outer array, our entire dataset would be loaded into a single row in the destination table. The STRIP_OUTER_ARRAY array option removes the outer set of square brackets when loading the data, separating the initial array into multiple lines. The above file format is specific to JSON. STRIP_NULL_VALUES = FALSE IGNORE_UTF8_ERRORS = FALSE To load the JSON object into a Snowflake table, file format is one of the mandatory objects in snowflake: CREATE FILE FORMAT JSON_FILE_FORMAT Object CHRISTMAS_REC is created with one column TEST_DATA that holds the object of JSON data. We’ll be using the variant object to load data into a Snowflake table. In snowflake, to process the semi-structured data, we have the following data types: In order to create JSON data, we need an object to hold the data and it should be capable enough to hold the semi-structured data. CREATE DATABASE IF NOT EXISTS TEST_DATABASE In such a case you can use the existing schema. This step is optional if you already have access to the existing schema. CREATE DATABASE IF NOT EXISTS TEST_DATABASE Ĭreate a new schema under TEST_DATABASE object to have ease of access. You can use the existing one if you have already created it earlier. We have created a new database object to load and process semi-structured data as shown below. Data can be queried using SQL SELECT statements that reference JSON elements by their paths. JSON data can be loaded directly into the table columns with type VARIANT, a universal type that can be used to store values of any type. JSON is the most widely used and industry standard due to its data format and ease of use. Snowflake supports semi-structured data in the form of JSON, Avro, ORC, Parquet, and XML. One of Snowflake’s unique features is its native support for semi-structured data. Snowflake can load JSON data directly into table columns with type VARIANT, a universal type that can be used to store values of any type. Semi-structured data is data that does not conform to a specific schema, such as JSON data. One of Snowflake’s unique feature is its native support for semi-structured data. ![]() What is Snowflake’s semi-structured data? It also provides an example of querying semi-structured data using Snowflake’s SQL SELECT statements. ![]() ![]() It covers the process of loading JSON data into a Snowflake table, including creating a database object, schema, table, file format, and stage. In today’s article we’ll go over Snowflake’s support for semi-structured data in the form of JSON, Avro, ORC, Parquet, and XML.
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