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:

  • ❌ Incompatible formats for joins, filters, or aggregations

  • ❌ Hidden fields that require repeated manual extraction

  • ❌ Breaks in data exports when formats don’t match flat schema standards

  • ❌ Error-prone scripts to unpack different nesting depths

With Edilitics, flattening becomes visual and schema-aware:

  • ✅ Handles lists, dicts, structs, and objects

  • ✅ Supports recursive flattening (first-level only per operation)

  • ✅ Provides real-time previews of new columns

  • ✅ Lets you retain or drop original columns for traceability

How to Flatten Columns in Edilitics

  1. Select nested columns

    Choose one or more columns to flatten. Only columns with complex types (list, object, struct, dict) will appear.

  2. Retain or drop the original columns

    • Retain Original (default) – Keep the source nested column alongside flattened outputs

    • Drop Original – Remove the nested column after flattening to avoid redundancy

  3. Preview flattened structure

    Instantly preview the extracted fields as new top-level columns.

  4. 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

TypeDescriptionExample
ObjectGeneric container (e.g., JSON blob){"name": "John", "age": 30}
ListOrdered collection of items["apple", "banana"]
DictKey-value mapping where each key is unique{"key1": "val1", "key2": "val2"}
StructNested record-like grouping (commonly from Parquet, JSON, Avro, or APIs){"user": {"name": "Jane", "role": "admin"}}

Common Use Cases for Flattening

IndustryColumnNested ContentPurpose
RetailProductDetailsJSON object with color, sizeStandardize product attributes for category-level insights
HealthcarePatientInfoStruct with demographics, historySimplify patient views for operational and clinical workflows
FinanceTransactionDetailsMetadata, itemized chargesPrepare transaction logs for audit, billing, or aggregation
ManufacturingBatchDataNested test results and parametersFlatten batch-level testing metadata for quality control
EducationPerformanceDataScores, attendance, activitiesEnable 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_mathattendance_days_presentactivities_clubsactivities_sports
85150["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 size
FROM 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:

  • ✅ Schema-aware – Only eligible nested columns appear

  • ✅ Previewable – See extracted fields before executing

  • ✅ Undoable – Retain original column until final commit

  • ✅ 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:

Enterprise Support & Technical Assistance

For technical inquiries, implementation support, or enterprise-level assistance, our dedicated technical support team is available to ensure optimal deployment and utilization of Edilitics solutions. Please contact our enterprise support desk at support@edilitics.com. Our team of specialists will respond promptly to address your requirements.

« Find and Replace

Group By »

Unify Data. Automate Workflows. Accelerate Insights.

Eliminate silos, automate workflows, and turn raw data into business intelligence - all in one no-code platform.