json

Pivoting JSON Data in SQL Server

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JSON has become a common format for data exchange, and SQL Server’s built-in JSON support makes it straightforward to work with JSON data directly in your queries. But what happens when you need to pivot JSON data – transforming nested structures or array elements into a columnar format for reporting or analysis?

SQL Server provides functions like OPENJSON(), JSON_VALUE(), and JSON_QUERY() that let you extract and manipulate JSON data. Combined with standard pivoting techniques, you can reshape JSON data into whatever format you need. This can be particularly useful when you’re receiving JSON from APIs, storing semi-structured data, or working with configuration data that doesn’t fit neatly into traditional tables.

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How to Convert JSON to Rows and Columns in SQL Server

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Modern applications often exchange information in JSON, and that data often ends up in SQL Server. While JSON’s flexible structure makes it ideal for storing dynamic or nested data, it doesn’t fit neatly into traditional relational tables. The good news is that SQL Server includes a good selection of JSON functions that let you parse, query, and transform JSON content into structured rows and columns. This means that you can work with your JSON data just like any other table.

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Using JSON_MODIFY() to Update Nested JSON in SQL Server

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Working with JSON data in SQL Server has become quite common as more applications are relying heavily on semi-structured data. When your JSON includes nested objects or arrays, it’s useful to know how to make precise updates without rewriting the entire document. Fortunately, SQL Server makes this relatively simple with the JSON_MODIFY() function. This function lets you update specific values inside JSON text stored in a table, even deep within the structure.

In this article, we’ll walk through an example of how to use SQL Server’s JSON_MODIFY() function to update nested JSON.

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How to Export SQL Server Query Results as JSON with FOR JSON AUTO

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SQL Server makes it surprisingly simple to generate JSON directly from a query. If you’ve ever had to send data to a web service, feed an API, or just save query results in a structured text format, JSON is a natural choice. Instead of writing complex formatting logic in your application, you can let SQL Server do the heavy lifting with the FOR JSON clause. And you can use the AUTO keyword to have the JSON formatted automatically by SQL Server.

In this article, we’ll look at how FOR JSON AUTO works, along with some examples to demonstrate its usage.

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7 Ways to Extract Data from JSON in DuckDB

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Most DuckDB distributions come with the JSON extension, and this extension is loaded upon first use. That means we can go ahead and run queries against JSON data right out of the box. One common task we’ll face when working with JSON is extracting data from within the JSON documents. This can include extracting scalar values, or extracting nested JSON from within the outer document.

DuckDB provides us with multiple ways to extract such data. The option we use will largely depend on our use case. Either way, here are seven options for extracting data from JSON documents in DuckDB.

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OPENJSON() vs JSON_VALUE() When Parsing JSON in SQL Server

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Working with JSON in SQL Server often comes down to choosing the right function for the job. Two of the most common options are OPENJSON() and JSON_VALUE(). Both are designed to pull data out of JSON documents, but they work in very different ways and are suited to different scenarios. Knowing when to use each one can save time and simplify your queries.

This article breaks down how OPENJSON() and JSON_VALUE() handle JSON parsing, what makes them different, and the situations where one might be a better fit than the other. Whether you are cleaning up nested JSON or just extracting a single value, understanding these functions will help you work more effectively with JSON data in SQL Server.

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Convert a Table to a JSON Document with JSON_GROUP_OBJECT() in DuckDB

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In DuckDB, the json_group_object() function is a convenient way to aggregate data into JSON objects by pairing keys and values across rows within groups.

The function is especially useful when we’re transforming tabular data into a more hierarchical or nested JSON structure for web APIs, reporting, or downstream processing. It helps pivot rows into a single JSON object, making the data more compact and easier to consume in applications that require JSON formats.

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