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Pipeline Use Cases

Have a data problem? Find your solution here.

“I want to…” — Search by what you need to do, not by technical category.

📊 Clean messy API data

Problem: API returns too many fields, weird names, missing values

Solution:

  1. Filter Fields — keep only the fields you need
  2. Rename Keys — fix weird field names (camelCase, snake_case, etc.)
  3. Clean JSON — remove nulls, empty strings, trim whitespace

Utilities: Remove Fields, Rename Keys, Clean JSON

Result: Clean, focused data with consistent naming

🔍 Extract specific fields from large JSON

Problem: 100-field object, only need 5

Solution:

  1. Pick Fields — filter by path or name
  2. Optional: Flatten — if you need to flatten nested structures first

Utilities: Pick Fields, Flatten / Nest

Result: Focused data with only what you need

🔄 Convert spreadsheet data to JSON

Problem: Have CSV, need JSON objects

Solution:

  1. CSV to JSON — converts CSV to array of JSON objects
  2. Optional: Clean JSON — remove nulls, trim strings, fix types

Utilities: CSV to JSON, Clean JSON

Result: Properly typed JSON objects ready for your application

⚖️ Compare two JSON files

Problem: Need to see what changed between versions

Solution:

  1. Diff — compares two JSON inputs
  2. Choose mode: changed-only, added-only, removed-only, or full-annotated

Utilities: Diff

Result: Clear visualization of what changed, added, or removed

🎯 Filter data based on conditions

Problem: Only want items that match criteria

Solution:

  1. Filter — by value, key, or JavaScript expression

Examples:

  • Keep users where age > 18
  • Keep products where price < 100
  • Keep logs where level === "error"

Utilities: Filter

Result: Only the data that matches your criteria

📦 Flatten nested structures

Problem: Deep nesting makes data hard to use

Solution:

  1. Flatten / Nest — convert between flat and nested structures
  2. Control depth and formatting with delimiter options

Utilities: Flatten / Nest

Result: Data structure that matches your needs

🔐 Remove sensitive data

Problem: Need to share data but protect privacy

Solution:

  1. Redact — emails, phones, SSNs, credit cards, custom patterns
  2. Choose mask style: full, partial, or hash

Utilities: Redact

Result: Safe-to-share data with sensitive info protected

📊 Aggregate and summarize

Problem: Need stats, counts, sums from array data

Solution:

  1. Aggregate — sum, average, count, min, max, stats, unique, flatten

Examples:

  • Total revenue from all orders
  • Average order value
  • Count of unique users
  • Min/max prices

Utilities: Aggregate

Result: Statistical summaries and insights from your data

🗺️ Transform field names

Problem: Field names don’t match your system

Solution:

  1. Rename Keys — case transforms (camelCase, snake_case, kebab-case) or custom mappings

Utilities: Rename Keys

Result: Consistent naming that matches your application’s conventions

🔗 Combine multiple data sources

Problem: Data split across files/objects

Solution:

  1. Deep Merge — combine two JSON objects
  2. Choose strategy: merge, replace, or keep existing

Utilities: Deep Merge

Result: Single unified data source

📝 Generate test data

Problem: Need sample JSON for development

Solution:

  1. Generate Mock Data — create realistic test data
  2. Specify field types, counts, and patterns

Utilities: Generate Mock Data

Result: Realistic test data for development and testing

🌍 Add localization

Problem: Need to support multiple languages

Solution:

  1. Add Localization — add translated fields for multiple languages

Utilities: Add Localization

Result: Data ready for international audiences

📋 Parse log files

Problem: Console logs in text format

Solution:

  1. Parse Console Log — convert plain text logs to structured JSON
  2. Parse NDS Log — parse NDS-specific log formats

Utilities: Parse Console Log, Parse NDS Log

Result: Structured JSON you can query and analyze

🔢 Transform data types

Problem: Values are wrong type (strings instead of numbers)

Solution:

  1. Format Values — convert types (toString, toNumber, uppercase, lowercase, etc.)

Utilities: Format Values

Result: Properly typed data ready for your application

🔄 Replace values systematically

Problem: Need to replace specific values across entire dataset

Solution:

  1. Map Values — replace values using lookup table
  2. Choose behavior: keep unmatched, remove unmatched, or default value

Utilities: Map Values

Result: Consistent values across your dataset

📐 Normalize data structure

Problem: Arrays have inconsistent fields

Solution:

  1. Normalize — ensure consistent structure across array items
  2. Fill missing fields, remove extra fields, sort keys

Utilities: Normalize

Result: Consistent, predictable data structure

✨ Clean up messy data

Problem: JSON has issues but you want to keep most of it

Solution:

  1. Clean JSON — remove nulls, empty strings, trim strings, sort keys
  2. Multiple cleanup options in one utility

Utilities: Clean JSON

Result: Clean data without losing structure

🗑️ Remove unnecessary data

Problem: Too much data, need to reduce size

Solution:

  1. Truncate — limit string lengths, array lengths, remove large blobs
  2. Choose collapse mode for truncated content

Utilities: Truncate

Result: Smaller, more manageable datasets

📋 Combine multiple operations

Problem: Need to do several transformations in sequence

Solution: Chain multiple utilities in a pipeline!

Example: Clean API data

  1. CSV to JSON → get data in
  2. Clean JSON → fix formatting
  3. Filter Fields → keep only what you need
  4. Rename Keys → fix naming
  5. Redact → protect sensitive info

Result: Complex transformations become simple, visual, and debuggable

🔍 Find the right utility

Not sure which utility does what you need?

💡 Tip: Start with your problem, not the utility name. This page organizes solutions by real-world problems, not technical categories.

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