In JSON, semi-structured data — data that originates from various sources and devices including mobile phones, web browsers, servers, and IoT devices — is collected as messages called "events," organized logically into batches, and then fed to a data platform via a data pipeline.
It can be used in many applications but is especially common for transferring data between servers and web applications or web-connected devices. This is because those applications can often only receive data as text, and JSON is text-based.
In comparison to flat files like CSVs, which use relational columns and rows, JSON files store data in nested objects and arrays which contain values themselves. This structure is highly adaptable; the columns within the data source don't limit adding new data to the collection.
Due to its simple design, flexibility, and ease of use and understanding, it is the standard data format commonly used for web and mobile applications — sending data from a server to a client to be displayed on a web page and vice versa.
JSON exists as a string (a JSON string), which is ideal for transmitting data across networks.
It plays a critical role in the exchange of data between web servers and apps. Nearly 3-and-a-half billion people on social media spend an average of two-plus hours a day on Google, Facebook, Twitter, Instagram, and LinkedIn — all of which rely on JSON for their APIs.
JSON makes data transferring easy, which is why it's so popular among data-heavy social media apps.
JSON vs XML
JSON has become the standard format for collecting and storing semi-structured data sets that originate from IoT devices, mobile devices and the web. In the not so recent past, semi-structured storage and analysis required specific JSON databases.
But cloud data platforms like Snowflake offer native support to load and query semi-structured data, including JSON and other formats, making these databases unnecessary. That means no more loading semi-structured data into enabled JSON databases, parsing JSON, and then moving it into relational database tables.
Analytics for JSON
Most databases and data stores only support a single format. But Snowflake supports JSON and other semi-structured data natively alongside relational data. With Snowflake, users can choose to "flatten" nested objects into a relational table or store objects and arrays in their native format within Snowflake's Variant data type. Semi-structured data can be manipulated with ANSI-standard SQL with the addition of dot notation.
Using Snowflake for Semi-Structured Data
Knowing how to manage and analyze your organization's proliferation of semi-structured data is critical for gaining valuable insights. One of Snowflake's critical differentiators is its ability to natively ingest semi-structured data such as JSON and Parquet, store it efficiently, and then access it quickly using simple extensions to standard SQL.
Snowpark is a developer framework for Snowflake that allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single platform using SQL, Python, Java, and Scala. Using Snowpark, data teams can effortlessly transform raw data into modeled formats regardless of the type, including JSON, Parquet, and XML.
With Snowflake, users can:
- Ingest semi-structured data without transformation
- Either flatten semi-structured, nested data formats into SQL tables or leave them in their native formats
- Run SQL-based queries across both structured and semi-structured data types
Snowflake gives you a fast path to the enterprise endgame: the real ability to quickly and easily load semi-structured data into a modern cloud data platform and make it available for immediate analysis.