How do you manage nested data in data tools?

1 What is nested data?

Nested data is data that contains other data within it, forming a tree-like structure. For example, a JSON file can have an array of objects, each with its own properties and sub-objects. Nested data can represent complex relationships and attributes, but it can also make data analysis more difficult. To work with nested data, you need to understand its schema, or how the data is organized and structured.

2 Why is nested data challenging?

Nested data can be difficult to read and interpret, particularly when it has many levels and branches. Accessing and manipulating specific elements or values within nested data can also be challenging, especially when they have different names or types. Furthermore, it can be inefficient to store and process nested data with a lot of redundancy or variation. Finally, nested data may be incompatible with data tools that expect flat or relational data, such as spreadsheets, databases, or visualization tools.

3 How do you flatten nested data?

Flattening nested data can make it simpler to read, access, manipulate, store, and process; however, it can lead to a loss of information or context, an increase of complexity or size, or duplication or inconsistency. To flatten nested data, one can use manual extraction, parsing functions such as JSON.parse() or xml.etree.ElementTree in Python, or data transformation tools like Alteryx, KNIME, or Trifacta to automate the process and handle various types and sources of nested data. Although flattening nested data can be beneficial in some cases, it is important to consider the potential drawbacks before making any decisions.

4 How do you normalize nested data?

Normalizing nested data is another way to manage it, which can preserve the information and context of the nested elements and reduce complexity and size. However, this can result in a loss of flexibility or performance, an increase in dependency or complexity, or duplication or inconsistency. To normalize nested data, you can use manual extraction, such as creating and populating tables from the nested data using SQL commands or a graphical user interface. Additionally, parsing functions like pandas.read_json() or pandas.read_xml() in Python can convert nested data into data frames, which can then be converted into SQL tables using pandas.to_sql(). Data transformation tools like Alteryx, KNIME, or Trifacta can also automate the normalization process and handle different types and sources of nested data.

5 How do you query nested data?

Another way to manage nested data is to query it, or access and manipulate specific elements or values within the nested data using a query language. Querying nested data can allow you to work with the data without flattening or normalizing it, and perform complex operations and calculations on the data. However, querying nested data can also lead to compatibility issues, a steep learning curve, and duplication of values. To query nested data, you can use various methods and tools, such as native query languages, SQL extensions, and data analysis tools. For example, JSONPath or XPath can be used to query JSON or XML data directly using expressions and operators; PostgreSQL or MySQL can use JSON functions or XML functions in SQL tables; and RStudio, Jupyter Notebook, or Power BI can query JSON or XML data using libraries or connectors.

6 How do you visualize nested data?

Visualizing nested data can help you explore and understand the data, and communicate your findings and recommendations. However, this approach can also lead to a loss of detail or accuracy, an increase of complexity or clutter, or duplication or inconsistency. To visualize nested data, you can use manual extraction, parsing functions, or data visualization tools. Manual extraction involves copying and pasting values from the nested data into a spreadsheet or a visualization tool. Parsing functions like JSON.parse() or xml.etree.ElementTree in Python can convert nested data into native data structures that can be converted into charts or dashboards using libraries or modules. Data visualization tools such as Tableau, Power BI, or D3.js can also import, transform, and visualize JSON or XML data using connectors, functions, or scripts.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?