This QuickStart discusses and demonstrates how a business user can easily handle columns of data that are stored in the JSON format.

JSON, which stands for JavaScript Object Notation, has become a universally accepted standard for data interchange on the web due to its simplicity, readability, and language-independent nature.

Whether you're receiving data from a web API, reading from a file, or working with local data, knowing how to parse JSON is crucial in modern software development

Parsing JSON data refers to the process of taking a piece of data (typically a string) in JSON format and converting it into a format that your programming language can understand and manipulate.

This allows programs to extract values, iterate through arrays, and read key-value pairs from objects.

Parsing JSON is not something the typical business user tackles and can be a source of frustration when they are presented data in this way. This results in requests to the development team to parse the data into a more comfortable, columnar format prior to making it available to users. This takes some time and must be done using code or other ETL (extract, transform, load) tool the developers have available.

There is a much better way; using Sigma to directly (and easily) parse the data.

Target Audience

Anyone who is trying to parse JSON data in the fastest way possible, to enable analytics applications.


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What You'll Learn

How to use Sigma to parse simple and nested JSON data.

Let's assume that we got a sample database that provides a list of all the US States, but the data came to us in a single JSON column.

Login to Sigma and create a new Workbook and Page.

Rename the Workbook to Working with JSON Data and rename the page to US Capitals.

We will use a table from the Sigma Sample Database.

Click to add a new table:


Select the Sigma Sample Database.

Expand the FUN schema and then USA_NAMES.


Only select the State Json Field and click the Select button:

We now have a table with the single column of Json data.

Now comes the awesome part!

Click the State Json Field column's drop arrow and select Extract Columns:

We are presented with a list of the column data that Sigma was able to automatically parse from the JSON data.

Click to select the fields STATE NAME, CAPITAL, LAT, LONG and click the Confirm button:

We don't really need to see the JSON column anymore so hide that.

We now have a clean table that we can use for our map:

Click to add a Child Element / Visualization:

Configure the new visualization as shown below:

Granted, this exercise was pretty simple, but it should be clear now that parsing JSON data is something any business user of Sigma can confidently do on their own.


JSON data is not always "flat" as we saw in the previous example. In fact, it is very common to "nest" data elements inside a JSON file. In this case, the data is structured in a hierarchical manner, with "data inside data".

This can make extraction that much more problematic for a business user.

For example, a non-nested JSON object make look like this:

  "name": "John",
  "age": 30,
  "city": "New York"

Where a nested JSON object is presented in a hierarchical format:

In this example, we want to send customers an offer on their birthday, but an ETL job merged their personal information into the sales table as a JSON object, and on top of that, the birthday detail is nested in the JSON object too!

Let's take a look at how Sigma handles this JSON nesting challenge.

Create a new Page in the same Workbook and rename it Nested JSON.

Create a new Table using the Sigma Sample Database and choose the RETAIL PLUGS ELECTRONICS / PLUGS_ELECTRONICS_HANDS_ON_LAB_DATA table.

We will only need the columns Customer Name and Cust Json. Click Select when done:

As before, click the drop arrow on the Cust Json column and select Extract columns.

We want to select the following columns to support our use case:

Hide the Cust Json column as we don't need to see that anymore.

A point to understand is that the parsed columns can be filtered except for the nested columns. If we want to filter those (BIRTHDAY_MONTH and BIRTHDAY_DAY), we need to cast them as text so that we can then filter out the rows with no record of the customer's birthday (null values).

For the BIRTHDAY_MONTH column, set the formula to:


Now we are able to filter out the null rows, using a filter on this column:

We could keep going on the use case, focusing on how we can add more value to it and not being blocked by nested JSON.

If you are interested in reading more about how Sigma handles JSON, please click here.


In this lab we learned how to leverage flat and nested JSON using Sigma's unique user interface. Business users are able to extract columns from JSON objects directly, without having to ask or wait for development resources.

Additional Resource Links

Be sure to check out all the latest developments at Sigma's First Friday Feature page!

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