Adobe Customer Journey Analytics SDR Generator

A production-ready Python tool that automates the creation of Solution Design Reference (SDR) documents from your Adobe Customer Journey Analytics (CJA) implementation.

What It Is

A Solution Design Reference is the essential documentation that bridges your business requirements and your analytics implementation. It catalogs every metric and dimension in your CJA Data View, serving as the single source of truth for what data you're collecting and how it's configured.

The Problem: Manually documenting CJA implementations is time-consuming, error-prone, and quickly becomes outdated. Teams waste hours exporting data, formatting spreadsheets, and cross-referencing configurations—only to repeat the process when things change.

The Solution: This tool connects directly to the CJA API, extracts your complete Data View configuration, validates data quality, and generates professionally formatted documentation in seconds. It also tracks changes between Data Views over time with built-in diff comparison and snapshot capabilities.

Origin: This project evolved from a Jupyter notebook proof-of-concept into a production-ready CLI. The notebook remains excellent for learning; this tool is for teams needing automation, change tracking, and enterprise-grade reliability.

How It Works

Connects to your CJA instance via the Adobe API
Extracts all metrics, dimensions, and configuration from your Data View(s)
Validates data quality with core automated checks (duplicates, required fields, null values, missing descriptions, empty datasets, invalid IDs)
Generates formatted documentation with color-coded quality indicators

Key Features

Category	Feature	Benefit
Performance	Parallel Batch Processing	Process multiple Data Views simultaneously (3-4x faster)
	Validation Caching	50-90% faster on repeated runs with intelligent result caching
	Optimized Validation	Single-pass DataFrame scanning (30-50% faster)
	Configurable Workers	Scale from 1-256 parallel workers based on your infrastructure
Quality	Core Validation Checks	Detect duplicates, missing fields, null values, invalid IDs, and empty datasets
	Severity Classification	CRITICAL, HIGH, MEDIUM, LOW with color-coded Excel formatting
	Quality Dashboard	Dedicated sheet with filtering, sorting, and actionable insights
Output	Multiple Formats	Excel, CSV, JSON, HTML, Markdown—or generate all at once
	Professional Excel	Up to 8 formatted sheets with conditional formatting, frozen headers, auto-filtering
	Segments Inventory	Document segment filters, complexity, and references with `--include-segments` (SDR + Snapshot Diff)
	Derived Field Inventory	Document derived field logic, complexity, and dependencies with `--include-derived` (SDR only)
	Calculated Metrics Inventory	Document calculated metric formulas and references with `--include-calculated` (SDR + Snapshot Diff)
	Inventory-Only Mode	Generate only inventory sheets without standard SDR with `--inventory-only`
	Stdout Support	Pipe JSON/CSV output directly to other tools with `--output -`
	Auto-Open Files	Open generated files immediately with `--open` flag
Reliability	Automatic Retry	Exponential backoff with jitter for transient network failures
	Continue-on-Error	Batch processing continues even if individual Data Views fail
	Pre-flight Validation	Validates config and connectivity before processing
	Circuit Breaker	Prevent cascading failures with automatic recovery
	API Auto-Tuning	Dynamic worker adjustment based on response times
	Shared Validation Cache	Cross-process cache sharing for batch operations
Comparison	Data View Diff	Compare two Data Views to identify added, removed, and modified components
	Snapshot Support	Save and compare against baseline snapshots for change tracking
	Snapshot-to-Snapshot	Compare two snapshot files directly without API calls
	Auto-Snapshot on Diff	Automatically save timestamped snapshots during comparisons for audit trails
	CI/CD Integration	Policy exit codes for automation (2=policy threshold exceeded, 3=diff warn threshold exceeded)
	GitHub Actions Step Summary	Automatically writes Markdown summaries to `GITHUB_STEP_SUMMARY` when available
	Smart Name Resolution	Fuzzy matching suggestions for typos, interactive disambiguation for duplicates
Git Integration	Version-Controlled Snapshots	Save SDR snapshots in Git-friendly format with auto-commit
	Audit Trail	Full history of every Data View configuration change
	Team Collaboration	Share snapshots via Git repositories with PR-based review workflows
Org-Wide Analysis	Component Distribution	Analyze metrics/dimensions across all data views with `--org-report`
	Similarity Matrix	Identify duplicate or near-duplicate data views via Jaccard similarity
	Data View Clustering	Group related data views using hierarchical clustering
	Governance Recommendations	Automated insights for standardization opportunities
	CI/CD Exit Codes	Threshold-based exit codes for governance automation
	Trending & Drift	Compare reports over time to detect changes
Multi-Org	Profile Management	Switch between Adobe Organizations with `--profile client-a`
	Interactive Profile Setup	Create profiles interactively with `--profile-add`
	Profile Testing	Validate credentials with `--profile-test` before use
Developer UX	Quick Stats Mode	Get metrics/dimensions count instantly with `--stats` (no full report)
	Connection & Dataset Discovery	`--list-connections` and `--list-datasets` for infrastructure inventory
	Discovery Inspection	Drill into a data view's metrics, dimensions, segments, and calculated metrics
	Machine-Readable Discovery	`--list-dataviews --format json` for scripting integration
	Dry-Run Mode	Test configuration without generating reports
	Color-Coded Output	Global color controls via `--no-color`, `NO_COLOR`, and `FORCE_COLOR`
	Enhanced Error Messages	Contextual error messages with actionable fix suggestions
	Comprehensive Logging	Timestamped logs with rotation for audit trails
Agent & Automation	AGENTS.md Contract	Machine-parseable tool contract for AI agents (Claude Code, LangChain, etc.)
	Python Orchestrator	Subprocess wrapper for programmatic automation (`scripts/orchestrator.py`)
	Shell Script Templates	Cron-ready weekly, daily, and quarterly automation examples
	GitHub Actions Template	Copy-paste workflow for scheduled SDR auditing
	Exit Code Conventions	Structured exit codes (0/1/2/3) for agent decision-making

Who It's For

Analytics Teams needing up-to-date implementation documentation
Consultants managing multiple client implementations
Data Governance teams requiring audit trails and quality tracking
DevOps Engineers automating CJA audits in CI/CD pipelines

Quick Start

1. Clone the Repository

# Clone the repository
git clone https://github.com/brian-a-au/cja_auto_sdr.git
cd cja_auto_sdr

2. Install Dependencies

macOS/Linux:

# Install uv package manager (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install project dependencies
uv sync

Windows (PowerShell):

# Install uv package manager
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Install project dependencies
uv sync

If uv doesn't work, use native Python instead (recommended for Windows):

python -m venv .venv
.venv\Scripts\activate
pip install -e .

Windows Users: If you encounter issues with uv run or NumPy import errors on Windows, we recommend using Python directly. See the Windows-Specific Issues section in the troubleshooting guide for detailed solutions.

Running commands: You have two equivalent options:

uv run cja_auto_sdr ... — works immediately on macOS/Linux, may have issues on Windows

cja_auto_sdr ... — after activating the venv: source .venv/bin/activate (Unix) or .venv\Scripts\activate (Windows)

This guide uses uv run. Windows users should activate the venv first (pip install -e . makes the command available). The Common Use Cases table omits the prefix for brevity.

3. Configure Credentials

Get your credentials from Adobe Developer Console (see QUICKSTART_GUIDE for detailed steps).

Important: Your Adobe Developer Console project must have both the CJA API and the AEP (Experience Platform) API added. The AEP API associates your service account with an Experience Platform product profile, which is required for CJA API authentication. See the Quickstart Guide for setup instructions.

Option A: Configuration File (Quickest)

Create a config.json file with your Adobe credentials:

# Copy the example template
cp config.json.example config.json

# Or generate a template (creates config.sample.json)
uv run cja_auto_sdr --sample-config

# Edit config.json with your credentials

Note: By default, the tool reads ./config.json from your current working directory. Use --config-file /path/to/config.json to load a file from a different location or filename.

{
  "org_id": "YOUR_ORG_ID@AdobeOrg",
  "client_id": "YOUR_CLIENT_ID",
  "secret": "YOUR_CLIENT_SECRET",
  "scopes": "your_scopes_from_developer_console"
}

Option B: Environment Variables (Recommended for CI/CD)

Use a .env file (copy from .env.example) or export directly:

ORG_ID=your_org_id@AdobeOrg
CLIENT_ID=your_client_id
SECRET=your_client_secret
SCOPES=your_scopes_from_developer_console

Note: Environment variables take precedence over config.json.

4. Verify Setup & Run

Interactive Mode (Recommended for First-Time Users):

# Launch interactive mode - walks through all options step by step
uv run cja_auto_sdr --interactive

Interactive mode guides you through data view selection, output format, and inventory options.

macOS/Linux (Direct Commands):

# Verify configuration and list available data views
uv run cja_auto_sdr --validate-config
uv run cja_auto_sdr --list-dataviews

# Generate SDR for a data view (by ID)
uv run cja_auto_sdr dv_YOUR_DATA_VIEW_ID

# Or by name (quotes recommended for names with spaces)
uv run cja_auto_sdr "Production Analytics"

Windows (if uv run doesn't work):

# Activate virtual environment first
.venv\Scripts\activate

# Verify configuration and list available data views
cja_auto_sdr --validate-config
cja_auto_sdr --list-dataviews

# Generate SDR for a data view (by ID or name)
cja_auto_sdr dv_YOUR_DATA_VIEW_ID
cja_auto_sdr "Production Analytics"

Tip: You can specify Data Views by name in addition to ID. If multiple Data Views share the same name, all matching views will be processed.

5. Review Output

Generated Excel file: CJA_DataView_[Name]_[ID]_SDR.xlsx
Logs: logs/ directory

Common Use Cases

Note: Commands below omit the uv run prefix for brevity:

macOS/Linux: Add uv run before each command (e.g., uv run cja_auto_sdr dv_12345)
Windows: Activate the venv first (.venv\Scripts\activate), then run commands directly

Task	Command
Getting Started
Interactive mode (guided)	`cja_auto_sdr --interactive`
List available data views	`cja_auto_sdr --list-dataviews`
SDR Generation
Single Data View (by ID)	`cja_auto_sdr dv_12345`
Single Data View (by name)	`cja_auto_sdr "Production Analytics"`
Generate and open file	`cja_auto_sdr dv_12345 --open`
Batch processing	`cja_auto_sdr dv_1 dv_2 dv_3`
Custom output location	`cja_auto_sdr dv_12345 --output-dir ./reports`
Skip validation (faster)	`cja_auto_sdr dv_12345 --skip-validation`
Metrics-only SDR output (skip dimensions)	`cja_auto_sdr dv_12345 --metrics-only`
Dimensions-only SDR output (skip metrics)	`cja_auto_sdr dv_12345 --dimensions-only`
Include segments inventory	`cja_auto_sdr dv_12345 --include-segments`
Include derived fields (SDR only)	`cja_auto_sdr dv_12345 --include-derived`
Include calculated metrics	`cja_auto_sdr dv_12345 --include-calculated`
Include all inventories	`cja_auto_sdr dv_12345 --include-all-inventory`
Inventory-only output	`cja_auto_sdr dv_12345 --include-segments --inventory-only`
Quick inventory stats	`cja_auto_sdr dv_12345 --include-all-inventory --inventory-summary`
Fail on quality issues >= HIGH	`cja_auto_sdr dv_12345 --fail-on-quality HIGH`
Standalone quality report	`cja_auto_sdr dv_12345 --quality-report json --output -`
Output Formats
Export as Excel (default)	`cja_auto_sdr dv_12345 --format excel`
Export as CSV	`cja_auto_sdr dv_12345 --format csv`
Export as JSON	`cja_auto_sdr dv_12345 --format json`
Export as HTML	`cja_auto_sdr dv_12345 --format html`
Export as Markdown	`cja_auto_sdr dv_12345 --format markdown`
Generate all formats	`cja_auto_sdr dv_12345 --format all`
Quick Stats & Discovery
Quick stats (no full report)	`cja_auto_sdr dv_12345 --stats`
Stats as JSON	`cja_auto_sdr dv_12345 --stats --format json`
List Data Views	`cja_auto_sdr --list-dataviews`
List Connections	`cja_auto_sdr --list-connections`
List Data Views with Datasets	`cja_auto_sdr --list-datasets`
Filter discovery results	`cja_auto_sdr --list-dataviews --filter "Prod.*"`
Sort discovery output	`cja_auto_sdr --list-dataviews --sort name`
List as JSON (for scripting)	`cja_auto_sdr --list-dataviews --format json`
Interactive Data View selection	`cja_auto_sdr --interactive`
Pipe to other tools	`cja_auto_sdr --list-dataviews --output - \| jq '.dataViews[]'`
Inspect a data view	`cja_auto_sdr --describe-dataview dv_abc123`
Inspect by name	`cja_auto_sdr --describe-dataview "Production Web Data"`
List metrics (with filter)	`cja_auto_sdr --list-metrics dv_abc123 --filter revenue`
List dimensions as CSV	`cja_auto_sdr --list-dimensions dv_abc123 --format csv --output dims.csv`
List segments	`cja_auto_sdr --list-segments dv_abc123`
List calculated metrics	`cja_auto_sdr --list-calculated-metrics dv_abc123 --format json`
Validate config only	`cja_auto_sdr --validate-config`
Diff Comparison (default: console output)
Compare two Data Views	`cja_auto_sdr --diff dv_1 dv_2`
Compare by name	`cja_auto_sdr --diff "Production" "Staging"`
Diff as Markdown	`cja_auto_sdr --diff dv_1 dv_2 --format markdown`
Diff as JSON	`cja_auto_sdr --diff dv_1 dv_2 --format json`
Diff metrics only	`cja_auto_sdr --diff dv_1 dv_2 --metrics-only`
Diff dimensions only	`cja_auto_sdr --diff dv_1 dv_2 --dimensions-only`
Save snapshot	`cja_auto_sdr dv_12345 --snapshot ./baseline.json`
Compare to snapshot	`cja_auto_sdr dv_12345 --diff-snapshot ./baseline.json`
Compare two snapshots	`cja_auto_sdr --compare-snapshots ./old.json ./new.json`
Auto-save snapshots	`cja_auto_sdr --diff dv_1 dv_2 --auto-snapshot`
With retention policy	`cja_auto_sdr --diff dv_1 dv_2 --auto-snapshot --keep-last 10`
Auto-prune snapshots (defaults)	`cja_auto_sdr --diff dv_1 dv_2 --auto-snapshot --auto-prune`
List saved snapshots	`cja_auto_sdr --list-snapshots`
Prune snapshots only	`cja_auto_sdr --prune-snapshots --keep-last 20`
Inventory Diff (same data view over time)
Snapshot with inventory	`cja_auto_sdr dv_12345 --snapshot ./baseline.json --include-all-inventory`
Compare with inventory	`cja_auto_sdr dv_12345 --diff-snapshot ./baseline.json --include-calculated`
Full inventory diff	`cja_auto_sdr dv_12345 --diff-snapshot ./baseline.json --include-calculated --include-segments`
Git Integration
Initialize Git repo	`cja_auto_sdr --git-init --git-dir ./sdr-snapshots`
Generate and commit	`cja_auto_sdr dv_12345 --git-commit`
Commit with custom message	`cja_auto_sdr dv_12345 --git-commit --git-message "Weekly audit"`
Commit and push	`cja_auto_sdr dv_12345 --git-commit --git-push`
Org-Wide Analysis
Analyze all data views	`cja_auto_sdr --org-report`
Filter by name pattern	`cja_auto_sdr --org-report --filter "Prod.*"`
Exclude patterns	`cja_auto_sdr --org-report --exclude "Test\|Dev"`
Limit analysis scope	`cja_auto_sdr --org-report --limit 10`
Include component names	`cja_auto_sdr --org-report --include-names`
Skip similarity matrix	`cja_auto_sdr --org-report --skip-similarity`
Export as Excel	`cja_auto_sdr --org-report --format excel`
Export as HTML	`cja_auto_sdr --org-report --format html`
Export as CSV	`cja_auto_sdr --org-report --format csv`
Export all formats	`cja_auto_sdr --org-report --format all`
Custom thresholds	`cja_auto_sdr --org-report --core-threshold 0.7 --overlap-threshold 0.9`
Overlap threshold note	Similarity reporting caps the effective threshold at 90% for governance checks (reports show configured vs. effective when higher)
Quick stats mode	`cja_auto_sdr --org-report --org-stats`
Cluster data views	`cja_auto_sdr --org-report --cluster --format excel`
CI/CD governance check	`cja_auto_sdr --org-report --duplicate-threshold 5 --fail-on-threshold`

Documentation

Guide	Description
Quick Reference	Single-page command cheat sheet
Extended Quick Start	Complete walkthrough from zero to first SDR
Installation Guide	Detailed setup instructions, authentication options
Configuration Guide	config.json, environment variables, Profile management
CLI Reference	Complete command-line options and examples
Shell Completion	Enable tab-completion for bash/zsh
Data Quality	Validation checks, severity levels, understanding issues
Inventory Overview	Unified guide to all component inventories
Derived Field Inventory	Derived field analysis, complexity scores, logic summaries
Segments Inventory	Segment filters, container types, definition summaries
Calculated Metrics Inventory	Calculated metric formulas, complexity, references
Performance	Optimization options, caching, batch processing
Troubleshooting	Common errors and solutions
Use Cases & Best Practices	Automation, scheduling, workflows
Output Formats	Format specifications and examples
Batch Processing	Multi-Data View processing guide
Data View Names	Using Data View names instead of IDs
Data View Comparison	Compare Data Views, snapshots, CI/CD integration
Git Integration	Version-controlled snapshots, audit trails, team collaboration
Org-Wide Analysis	Cross-data view component analysis, similarity detection, governance
Agent Automation	CI/CD pipelines, AI agent integration, scheduling patterns
Testing	Running and writing tests

Requirements

Python 3.14+
Adobe I/O integration with CJA and AEP API access
Network connectivity to Adobe APIs

Project Structure

High-level overview of the current repository layout (representative, not exhaustive):

cja_auto_sdr/
├── .github/
│   └── workflows/             # CI, lint, version-sync, and release checks
├── src/
│   └── cja_auto_sdr/          # Main package (src-layout)
│       ├── __init__.py        # Package init with version
│       ├── __main__.py        # Installed CLI entry point (`cja_auto_sdr`, `cja-auto-sdr`)
│       ├── generator.py       # Main SDR generation flow and legacy full CLI path
│       ├── api/               # API communication layer
│       │   ├── cache.py       # Validation result caching
│       │   ├── client.py      # CJA client initialization
│       │   ├── fetch.py       # Parallel API data fetching
│       │   ├── quality.py     # Data quality validation
│       │   ├── quality_policy.py # Quality gates and policy helpers
│       │   ├── resilience.py  # Retry, circuit breaker
│       │   ├── tuning.py      # API worker auto-tuning
│       │   └── validation.py  # Config & input validation
│       ├── cli/               # CLI parsing and interactive mode
│       │   ├── commands/      # Subcommand handlers
│       │   ├── execution.py   # Execution-context resolution
│       │   ├── interactive.py # Interactive data view selection
│       │   ├── main.py        # CLI entry orchestration
│       │   ├── mode_scoped_options.py # Option validation by command mode
│       │   ├── option_resolution.py # Shared option normalization
│       │   ├── parser.py      # Argument parser definitions
│       │   └── standalone_policy.py # Standalone command policy rules
│       ├── core/              # Shared core utilities
│       │   ├── colors.py      # ANSI color helpers
│       │   ├── config.py      # Configuration dataclasses
│       │   ├── config_validation.py # Config validation helpers
│       │   ├── constants.py   # Global constants
│       │   ├── credentials.py # Credential loading
│       │   ├── discovery_*.py # Discovery normalization/payload helpers
│       │   ├── error_policies.py # Error-handling policy helpers
│       │   ├── exceptions.py  # Custom exception hierarchy
│       │   ├── exit_codes.py  # Stable exit-code definitions
│       │   ├── json_io.py     # JSON read/write helpers
│       │   ├── lazy.py        # Lazy-loading helpers
│       │   ├── logging.py     # Log setup and formatting
│       │   ├── perf.py        # Lightweight performance helpers
│       │   ├── profiles.py    # Multi-org profile management
│       │   └── version.py     # Single-source version string
│       ├── data/              # Shared data package hooks
│       ├── diff/              # Data view comparison
│       │   ├── cli.py         # Diff CLI dispatch helpers
│       │   ├── commands.py    # Diff command orchestration
│       │   ├── comparator.py  # Diff logic and change detection
│       │   ├── git.py         # Git snapshot integration
│       │   ├── models.py      # Snapshot and diff data models
│       │   ├── snapshot.py    # Snapshot save/load/prune
│       │   └── writers.py     # Diff output formatters
│       ├── git/               # Git package compatibility surface
│       ├── inventory/         # Component inventory modules
│       │   ├── calculated_metrics.py
│       │   ├── derived_fields.py
│       │   ├── segments.py
│       │   ├── summary.py     # Inventory summary stats
│       │   └── utils.py       # Shared inventory helpers
│       ├── org/               # Org-wide analysis
│       │   ├── analyzer.py    # OrgComponentAnalyzer
│       │   ├── cache.py       # Report caching
│       │   ├── identifiers.py # Org/data-view identifier helpers
│       │   ├── models.py      # Data classes for org analysis
│       │   ├── snapshot_utils.py # Org snapshot helpers
│       │   ├── trending.py    # Historical trend analysis
│       │   └── writers/       # Org-report writers
│       ├── output/            # Output generation
│       │   ├── diff/          # Diff output implementations
│       │   ├── excel.py       # Excel formatting
│       │   ├── inventory/     # Inventory summary output helpers
│       │   ├── protocols.py   # OutputWriter protocol
│       │   ├── registry.py    # Format registry
│       │   ├── run_summary.py # Run summary output helpers
│       │   ├── sdr/           # SDR output assembly
│       │   └── writers/       # CSV, HTML, JSON, Markdown writers
│       └── pipeline/          # Processing pipeline
│           ├── batch.py       # Batch processor
│           ├── dry_run.py     # Dry-run mode
│           ├── models.py      # Pipeline data models
│           ├── single.py      # Single data view processing
│           └── workers.py     # Worker coordination
├── docs/                      # Documentation (20+ guides)
│   ├── QUICKSTART_GUIDE.md    # Getting started guide
│   ├── CONFIGURATION.md       # Profiles, config.json & env vars
│   ├── CLI_REFERENCE.md       # Command-line reference
│   ├── INVENTORY_OVERVIEW.md  # Unified inventory guide
│   ├── DIFF_COMPARISON.md     # Data view comparison guide
│   ├── GIT_INTEGRATION.md     # Git integration guide
│   ├── ORG_WIDE_ANALYSIS.md   # Org-wide report guide
│   └── ...                    # Additional guides
├── examples/                  # Automation and GitHub Actions examples
├── scripts/                   # Utility scripts
├── tests/                     # Test suite (7,783+ tests)
│   ├── category_rules.py      # File-based test-category rules
│   ├── conftest.py            # Pytest fixtures and auto-marking
│   ├── README.md              # Test inventory and execution guide
│   └── test_*.py              # 114 collected test modules
├── sample_outputs/            # Example output files
│   ├── excel/                 # Sample Excel SDR
│   ├── csv/                   # Sample CSV output
│   ├── json/                  # Sample JSON output
│   ├── html/                  # Sample HTML output
│   ├── markdown/              # Sample Markdown output
│   ├── diff/                  # Sample diff comparison outputs
│   └── git-snapshots/         # Sample Git integration snapshots
├── logs/                      # Generated log files
├── snapshots/                 # Saved Data View snapshots
├── sdr-snapshots/             # Additional SDR snapshot artifacts
├── pyproject.toml             # Project configuration and dependencies
├── uv.lock                    # Dependency lock file for reproducible builds
├── README.md                  # This file
├── CHANGELOG.md               # Version history and release notes
├── LICENSE                    # License file
├── config.json.example        # Config file template
├── .env.example               # Environment variable template
└── *.xlsx                     # Generated SDR files

License

See LICENSE for details.

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Adobe Customer Journey Analytics SDR Generator

What It Is

How It Works

Key Features

Who It's For

Quick Start

1. Clone the Repository

2. Install Dependencies

3. Configure Credentials

4. Verify Setup & Run

5. Review Output

Common Use Cases

Documentation

Requirements

Project Structure

License

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Name		Name	Last commit message	Last commit date
Latest commit History 978 Commits
.github/workflows		.github/workflows
docs		docs
examples		examples
sample_outputs		sample_outputs
scripts		scripts
src/cja_auto_sdr		src/cja_auto_sdr
tests		tests
tools		tools
.env.example		.env.example
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
config.json.example		config.json.example
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
uv.lock		uv.lock

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Adobe Customer Journey Analytics SDR Generator

What It Is

How It Works

Key Features

Who It's For

Quick Start

1. Clone the Repository

2. Install Dependencies

3. Configure Credentials

4. Verify Setup & Run

5. Review Output

Common Use Cases

Documentation

Requirements

Project Structure

License

Additional Resources

About

Topics

Resources

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Releases 56

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