Snowflake JSON

The speed with which Snowflake can consume both structured and unstructured data is among its major advantages. You will learn how to interact with JSON files in Snowflake in this blog, as well as how to simply query the JSON data and transfer it into a Snowflake table.

What is Snowflake?

Snowflake is among the most enterprise-ready cloud data warehouses that offer ease of use without compromising on functionalities. It automatically adjusts upwards and downwards to achieve the ideal performance-to-cost ratio. The distinguishing feature of Snowflake is that it isolates computation from storage. This is important since practically all other databases, including Redshift, merge the two. As a result, you can scale maximum workload and pay the associated costs.

With Snowflake, you could scale your computation separately and save all your data in one location. For instance, if your report has fairly few sophisticated queries but you need near-real-time data loads for a range of processes, you can create a sizable Snowflake warehouse for the data load and size it down a bit once it's finished - all on a real-time basis. This reduces costs without compromising the objectives of your solution.

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What is JSON?

JavaScript Object Notation is a text-based, schema-less representation of structured information that uses ordered lists and key-value pairs. Even though JSON is a derivative of JavaScript, it is supported by libraries or natively in most popular programming languages. Data is frequently, but not always, exchanged between web clients and web servers using JSON.

Rules for JSON Syntax:

: Data can be found in key or value pairs

Commas separate the Data

Curly braces are used for objects ({})

Square brackets are used for arrays ([])

JSON values can look like this:

  • A number
  • A Boolean
  • A JSON object
  • An array
  • A string 
  • Null

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JSON Example:

{

    "firstName": "Keanu",

    "lastName": "Reeves",

    "age": 40,

    "children": [],

    "spouse": null,

    "address": {

        "street": "225 San Mateo County",

        "city": "San Francisco",

        "state": "California",

        "postalCode": "94016"

    },

    "phoneNumbers": [

        {

            "type": "mobile",

            "number": "314 298-4456"

        },

        {

            "type": "fax",

            "number": "754 222-5326"

        }

    ]

}

What is Snowflake Jason?

In addition to JSON, Snowflake also supports semi-structured data in XML, Parquet, Avro, ORC, and other formats natively. As a result, JSON data may be effectively stored in Snowflake and accessed via SQL.

You can simply import JSON data into relational tables using Snowflake JSON. Consequently, without needing to perform any changes, you may query this data with SQL and link that to another structured data. This enables businesses to streamline their data pipelines and quicken the rate at which the data is made available for evaluation.

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Working with Snowflake JSON

Prerequisites

  • The directions presuppose that you are already acquainted with Snowflake.
  • To use the commands in these examples, you must have a Snowflake account.
  • Additionally, you must correctly install and set up the SnowSQL command-line program. Here are the installation instructions for this.

First Step: Making Your Table

You must build a table with a separate column structured as a variant data type to deal with Snowflake's JSON data. A common data type called a variant enables you to import semi-structured data into Snowflake.

create or replace table json_table (v variant);

Second step: Employ dot notation

Once imported, this data can be easily queried using dot notation. Dot notation is almost identical to a SQL Select statement with conventional data types in that it only provides a path to the location of the value you want to return. Querying a list inside an array is a more challenging task, that's where flatten instructions come in help.

Third Step: Flatten data

A variant, array, and an object column's lateral view are created by the table function FLATTEN. In this step, the function is used to generate two tables with various flattening levels.

So, that sums it up! Semi-structured data is simple to manipulate if you learn how to utilize Snowflake's dot notation and flatten commands. You can expand on our fundamental SQL knowledge to create pristine, well-organized tables and views that users and BI tools may use.

How To Query JSON Data in Snowflake?

Step one: Log in to your account

Quickly log into your Snowflake account. 

Step two: Choose Database

To choose the database, we shall be using the "use" statement

Syntax:

JSON choose data

Example:

use database example_db;

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Step three: Make File Format for JSON

Create a named file format to specify a collection of staged data that can be accessed or loaded into Snowflake tables.

Syntax:

make file format for json

Example:

file format for json example

Step four: Make an Internal stage

Here, we'll build an internal stage called JSON temp int stage with the JSON type of file.

Syntax:

Example:

internal stage for json

Step five: Make Table in Snowflake using Create Statement

Here, we'll use the New statement to make a temporary table as seen in the example below. In the existing or given schema, it either substitutes an existing table or establishes a new one.

Syntax:

table in snowflake using json

Example:

example for json statement

Step six: Load JSON file to internal stage

Here, we'll upload the JSON data file from your computer to the Snowflake's staging area, as displayed below.

Example:

Json file to internal storage

Step 7: Copy the data into Target Table

As seen below, we will now load the JSON data that was previously imported into the internal stage into the target table.

Example:

data into create table

Step 8: Querying the data directly

By executing the select query displayed below, we will here confirm that the data entered into the target database is accurate.

Example:

quering the data directly

Step 9: Querying the JSON object

Here, we're going to use the select statement to query the JSON object, as displayed below.

Example:

quering the json objects

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Conclusion

As you'll see, the VARIANT data type gives native support for querying Snowflake JSON with no need to conduct time-consuming transforms or in-depth structure analysis. The performance of querying Snowflake JSON data is identical to that of all common relational data types.

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Gayathri
Gayathri
Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.

Snowflake JSON FAQ'S

Snowflake was created with tools that make it simple to retrieve JSON data and give you the option to mix it with structured data! With Snowflake, you can simply learn how to use SQL to query JSON data and link it to conventional tabular data in relational tables.

It checks a JSON document's validity. The output is NULL if the input string contains a JSON document that is valid or is NULL (i.e. no error). The output string has the error code if the input cannot be converted to a valid JSON value.

Instead of standardising data across several tables, each with a distinct and defined structure, as in a relational database, a JSON document database is a type of nonrelational database that is meant for storing and querying data as JSON documents.

Information is frequently, but not always, sent between web clients and web servers using JSON. JSON has proliferated on the web during the past 1.5 decades. Almost all publicly accessible web services nowadays use it as their preferred format, and private web services often do too.