Verify that your table has been created by going to the Databases tab and then click on the SEA_DB link. Notice that we used a special column type called VARIANT that allows Snowflake to ingest semi-structured data without having to pre-define the schema: create table sea_json (col1 variant) Set the context appropriately within the worksheet: use role sysadmin Ĭreate a table called SEA_JSON that will be used for loading the JSON data file. The screenshot below provides a read-out of the sample JSON data file:įirst, via the Worksheet pane in the UI, create a database called SEA_DB that will be used for storing the unstructured data in our exercise: create database sea_db Tutorial Part 1: Loading and Querying JSON Data This folder can also be found at the bottom of the post. After going through this exercise, apart from learning how to query semi-structured data, I hope you can also get familiarized with the capital cities of each country! Our goal is to load the JSON and XML data into Snowflake to achieve the following final, end-state table:īefore we get into the tutorials, download the ZIP folder here to access the SEA_SAMPLE_JSON and the SEA_SAMPLE_XML sample data files we’ll be using in this post. The sample data used in this exercise features the 11 countries that comprise my home region of Southeast Asia, containing dimensions and attributes that describe each country. If you have primarily worked with relational data models in the past, this would be a great new skill to add to your toolkit. This hands-on tutorial will teach you how to load JSON and XML data into Snowflake and query it with ANSI-standard SQL. And because an increasing amount of mission-critical data being generated today is semi-structured, Snowflake makes it easy to surface those insights to drive strategic decision-making. Snowflake is unique and stands out from other data warehousing platforms in the market today because it was designed from the ground up to easily load and query semi-structured data such as JSON and XML without transformation. As such, many organizations have experienced challenges to bring together both their structured and semi-structured data to generate timely business insights. However, the flexibility and expressiveness of semi-structured data also created additional layers of complexity when the data needs to be analyzed. These semi-structured data formats such as JSON, XML, Avro and Parquet have become the de facto form in which this data is sent and stored from these applications because of their flexibility. This now includes data coming from sensors, IoT devices and applications which store data in a non-relational, quasi-structured format. Today, with the advent of the data and cloud-computing revolution, data now originates from a wide range of sources. Back then, relational data with clearly defined schemas was the norm since data sources were smaller in volume and had less variety in terms of format. This series takes you through various features and capabilities of Snowflake to address a wide range of business requirements, use cases and scenarios.Ĭonventional data warehouses of the past were designed many decades ago, built to store data in very predictable, rigid and structured formats.
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