Semi-structured data refers to data that does not conform to a rigid tabular format but contains tags, markers, or other organizational elements that separate semantic elements. Common examples include JSON, XML, and nested data structures. In Power BI, converting semi-structured data to tables is …Semi-structured data refers to data that does not conform to a rigid tabular format but contains tags, markers, or other organizational elements that separate semantic elements. Common examples include JSON, XML, and nested data structures. In Power BI, converting semi-structured data to tables is essential for effective analysis and visualization.
When working with JSON data in Power BI, you use Power Query Editor to transform the data. After connecting to a JSON source, Power BI initially loads the data as records or lists. To convert these into usable tables, you employ several transformation techniques.
The first step typically involves using the 'To Table' function, which converts lists into table format. For nested JSON objects, you expand columns by clicking the expand icon next to column headers, allowing you to select which nested fields to include in your final table.
For XML data, Power BI provides similar functionality through the XML connector. The data loads hierarchically, and you use expansion operations to flatten the structure into rows and columns suitable for analysis.
Key Power Query functions for this conversion include Table.FromRecords, which transforms a list of records into a table, and Table.ExpandRecordColumn, which expands nested record columns into separate columns. The Table.ExpandListColumn function handles lists within columns by creating new rows for each list item.
When dealing with complex nested structures, you may need to perform multiple expansion operations and potentially merge queries to achieve the desired flat table structure. It is important to consider data types during this process and apply appropriate type conversions.
Best practices include renaming columns for clarity, removing unnecessary columns early in the transformation process to improve performance, and documenting your transformation steps. These conversions enable you to leverage Power BI full analytical capabilities on data that originally existed in hierarchical or nested formats.
Convert Semi-Structured Data to Tables in Power BI
Why It Is Important
Semi-structured data, such as JSON and XML files, is increasingly common in modern data environments. APIs, web services, and NoSQL databases frequently deliver data in these formats. As a Power BI Data Analyst, you must be able to transform this data into tabular format for analysis and visualization. The PL-300 exam tests your ability to handle these real-world scenarios effectively.
What Is Semi-Structured Data?
Semi-structured data falls between structured data (like SQL tables) and unstructured data (like text documents). It has some organizational properties but does not conform to rigid table schemas. Common examples include:
• JSON (JavaScript Object Notation) - Nested key-value pairs and arrays • XML (Extensible Markup Language) - Hierarchical tagged elements • Nested tables within Power Query - Records and lists embedded in columns
How It Works in Power BI
Step 1: Import the Data Use Get Data and select JSON, XML, or Web connector to bring semi-structured data into Power Query Editor.
Step 2: Identify Nested Elements Look for columns containing Record, List, or Table values. These indicate nested structures that need expansion.
Step 3: Expand Records Click the expand icon next to Record columns to extract individual fields. Select which columns to include in your final table.
Step 4: Expand Lists Use Expand to New Rows to create separate rows for each list item, or Extract Values to combine list items into a single delimited value.
Step 5: Parse JSON/XML Text If data arrives as text, use Json.Document() or Xml.Document() functions to parse it into navigable structures.
Step 6: Drill Down and Convert Right-click on elements to drill down through hierarchies. Use To Table option to convert lists into proper table format with index columns.
Key Power Query Functions
• Json.Document() - Parses JSON text into records/lists • Xml.Document() - Parses XML text into table structure • Table.ExpandRecordColumn() - Expands record columns • Table.ExpandListColumn() - Expands list columns to rows • Record.ToTable() - Converts a record to a two-column table
Exam Tips: Answering Questions on Convert Semi-Structured Data to Tables
1. Know the difference between Record, List, and Table - Records contain named fields, Lists contain indexed items, Tables have rows and columns. Questions often test this understanding.
2. Understand expansion options - Expanding to new rows versus extracting values produces different results. Choose based on whether you need separate rows or concatenated text.
3. Remember the hierarchy - JSON and XML often have multiple nesting levels. You may need to expand several times to reach the data you need.
4. Watch for List columns requiring conversion - Before expanding a list, you sometimes need to convert it to a table first using the To Table transformation.
5. Practice with real JSON - Understand how curly braces {} indicate objects/records and square brackets [] indicate arrays/lists.
6. Know when to use custom columns - Complex nested structures may require M code expressions to navigate properly.
7. Consider performance - Expanding large nested datasets can increase row counts significantly. Questions may ask about query folding implications.
8. Read scenarios carefully - Exam questions often describe API responses or file contents. Identify whether the source is JSON, XML, or already parsed.