Flatten Nested Columns | Unpack Complex Structures into Clean, Tabular Data
Nested data structures—lists, structs, dictionaries—can be rich in detail but difficult to analyze, query, or visualize directly. Traditional platforms require JSON parsing logic, iterative scripting, or recursive flattening functions to access deeply nested values.
Edilitics eliminates this friction with a governed, no-code Flatten operation. With support for complex types like lists, dicts, and structs, users can easily convert nested columns into fully flattened, analysis-ready tables—no code, no edge cases.
Why Flattening Matters
Nested structures slow down every part of the data workflow:
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❌ Incompatible formats for joins, filters, or aggregations
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❌ Hidden fields that require repeated manual extraction
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❌ Breaks in data exports when formats don’t match flat schema standards
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❌ Error-prone scripts to unpack different nesting depths
With Edilitics, flattening becomes visual and schema-aware:
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✅ Handles lists, dicts, structs, and objects
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✅ Supports recursive flattening (first-level only per operation)
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✅ Provides real-time previews of new columns
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✅ Lets you retain or drop original columns for traceability
How to Flatten Columns in Edilitics
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Select nested columns
Choose one or more columns to flatten. Only columns with complex types (list, object, struct, dict) will appear.
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Retain or drop the original columns
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Retain Original (default) – Keep the source nested column alongside flattened outputs
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Drop Original – Remove the nested column after flattening to avoid redundancy
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Preview flattened structure
Instantly preview the extracted fields as new top-level columns.
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Run the flattening operation
Flattening applies only to the first level of nesting. You can repeat this process iteratively for deeper structures.
Supported Data Types
| Type | Description | Example |
|---|---|---|
| Object | Generic container (e.g., JSON blob) | {"name": "John", "age": 30} |
| List | Ordered collection of items | ["apple", "banana"] |
| Dict | Key-value mapping where each key is unique | {"key1": "val1", "key2": "val2"} |
| Struct | Nested record-like grouping (commonly from Parquet, JSON, Avro, or APIs) | {"user": {"name": "Jane", "role": "admin"}} |
Common Use Cases for Flattening
| Industry | Column | Nested Content | Purpose |
|---|---|---|---|
| Retail | ProductDetails | JSON object with color, size | Standardize product attributes for category-level insights |
| Healthcare | PatientInfo | Struct with demographics, history | Simplify patient views for operational and clinical workflows |
| Finance | TransactionDetails | Metadata, itemized charges | Prepare transaction logs for audit, billing, or aggregation |
| Manufacturing | BatchData | Nested test results and parameters | Flatten batch-level testing metadata for quality control |
| Education | PerformanceData | Scores, attendance, activities | Enable holistic student reporting across multiple engagement types |
Sample Data Transformation Example
Before Flattening (PerformanceData Column)
{ "PerformanceData": { "scores": { "math": 85 }, "attendance": { "days_present": 150}, "activities": { "clubs": ["science_club", "math_club"], "sports": ["soccer"] } }}
After Flattening (First Level)
| scores_math | attendance_days_present | activities_clubs | activities_sports |
|---|---|---|---|
| 85 | 150 | ["science_club", "math_club"] | ["soccer"] |
✅ You can now filter, group, and analyze directly—without scripting.
Manual Equivalent – SQL & Pandas Examples
SQL Example – Redshift (JSON field extraction)
SELECT json_extract_path_text(ProductDetails, 'color') AS color, json_extract_path_text(ProductDetails, 'size') AS sizeFROM product_catalog;
Pandas Example – Flattening JSON column
flattened_df = pd.json_normalize(df['ProductDetails'])df = df.join(flattened_df)
With Edilitics, all this logic is abstracted behind a dropdown—no JSONPath expressions, no parsing errors.
Governed, Repeatable, First-Level Flattening
All flatten operations in Edilitics are:
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✅ Schema-aware – Only eligible nested columns appear
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✅ Previewable – See extracted fields before executing
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✅ Undoable – Retain original column until final commit
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✅ Recursive-ready – Reapply flattening to new nested fields if needed
Flattening is no longer a manual, error-prone task. With Edilitics, you can turn complex JSON, list, and struct fields into fully structured tables—one level at a time. Whether you're preparing logs, patient records, or student performance data, the Flatten operation ensures your datasets are clean, usable, and ready for downstream workflows.
Next: Prepare and Join Structured Data
After flattening, your new tabular structure is ready for advanced operations:
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