Split Columns | Break Down Data Structures Without Code

Concatenated or complex fields often obscure critical details - making datasets harder to analyze, join, or report. The Split Columns operation in Edilitics solves this cleanly: users can divide a single column into multiple structured fields using delimiters or regex patterns - with no scripts required.

Built for precision, Edilitics' split operations offer delimiter flexibility, regex support, and full schema validation - helping you normalize messy fields with governed, no-code transformations.

Why Splitting Matters

  • Poorly structured columns often cause:

    • Hard-to-analyze datasets

    • Inconsistent field references

    • Broken joins, filters, and aggregations

    • Complex manual parsing in external tools

  • Edilitics solves this with:

    • Visual column splitting using delimiters or regex

    • Support for multi-column outputs

    • Schema validation and safe naming enforcement

    • Governed, reversible transformations

How to Split Columns in Edilitics

  • Select the Column to Split

    • Pick any eligible column from your table. Only columns with text-compatible data types are listed.

    • Columns already split or flattened will be excluded automatically.

  • Choose Your Split Method

    • Delimiter-Based Split

      • Space – Separate names, words, phrases

      • Tab (\t) – Handle structured exports

      • Pipe (|) – Split pipeline-separated fields

      • Hyphen (-) – Divide IDs, dates, or codes

      • Underscore (_) – Separate system-generated keys

      • Custom – Define any single-character delimiter

    • Regex-Based Split (Advanced)

      For complex cases, define a regular expression (regex) to split based on text patterns, not just static characters.

      Examples:

      • Split by multiple spaces: \s+
      • Split by dash OR underscore: [-_]
      • Extract account|type|date format: (\d+)\|([A-Z]+)\|(\d{4}-\d{2}-\d{2})

      Regex gives you flexible, pattern-driven control over splits.

  • Define New Column Names

    Assign clear names to each resulting column. Edilitics enforces naming conventions (no special characters, no leading numbers or underscores).

  • Submit the Operation

    After previewing the split results, submit the operation. The newly generated columns will appear alongside your original dataset.

Real-World Use Cases for Splitting

IndustryScenarioSplit MethodPurpose
RetailBreak down ProductCode like ELEC-LAPTOP-001HyphenEnable category/subcategory/item-level analytics
HealthcareSeparate DateOfBirth into Year, Month, DayHyphenPerform age group analysis or temporal reporting
FinanceParse TransactionID like 123456 | CREDIT | 2024-08-01RegexIsolate account, transaction type, and date for auditability
ManufacturingSplit BatchDetails like B001_LINEA_SHIFT1UnderscoreStreamline batch tracing and quality checks
EducationDivide FullName like John Doe into FirstName, LastNameRegexImprove student record organization and reporting

Manual Equivalent – SQL & Pandas Examples

SQL Example – Delimiter-Based Split (Redshift)


SELECT
SPLIT_PART(ProductCode, '-', 1) AS Category,
SPLIT_PART(ProductCode, '-', 2) AS Subcategory,
SPLIT_PART(ProductCode, '-', 3) AS Item
FROM sales_data;

SQL regex splits are complex and database-specific (e.g., REGEXP_SUBSTR), so Edilitics focuses on no-code and safe delimiter splits for SQL users.

Pandas Example – Delimiter and Regex Splits


import pandas as pd
# Sample delimiter-based split
df[['Category', 'Subcategory', 'Item']] = df['ProductCode'].str.split('-', expand=True)
# Sample regex-based split
df[['AccountNumber', 'TransactionType', 'TransactionDate']] = df['TransactionID'].str.extract(r'(\d+)\|([A-Z]+)\|(\d{4}-\d{2}-\d{2})')

Edilitics lets you configure both types with dropdowns or simple regex input - no scripting needed.

Governed, Safe, and Regex-Ready

Split Columns in Edilitics is:

  • Schema-validated – Only text-compatible columns are shown

  • Regex-capable – Pattern-driven parsing without manual coding

  • Naming enforced – Prevent invalid or duplicate columns

  • Non-destructive – Originals preserved until finalization

Whether you're preparing data for analytics, cleaning exports, or enhancing traceability, Split Columns makes normalization effortless and governed - helping teams move from messy fields to structured, usable datasets with no manual overhead.

Next: Continue Structuring Your Data

After splitting columns, strengthen your dataset with:

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.

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