Getting Started

Welcome to Trazadera Golden

Trazadera Golden is a Master Data Management platform that helps organizations create a single, accurate view of their critical business data. Whether you're managing customer information, product catalogs, or supplier records, Golden ensures you have clean, deduplicated, and trustworthy data.

What Problem Does Golden Solve?

Organizations often struggle with:

• Duplicate Records - The same customer appears multiple times with slightly different information

• Inconsistent Data - Email addresses, phone numbers, and addresses that don't match across systems

• Poor Data Quality - Missing fields, invalid formats, and outdated information

• Fragmented Systems - Data scattered across CRM, ERP, databases, and spreadsheets

Golden solves these problems by automatically finding duplicates, merging them into "Golden Records," and maintaining high-quality master data.

How Golden Works

Golden processes your data in four simple steps:

1. Connect Your Data

Import data from databases, files, APIs, or Kafka streams. Golden supports multiple data sources simultaneously.

2. Detect Duplicates

Golden uses intelligent algorithms to find records that represent the same real-world entity (person, product, location, etc.) even when data doesn't match exactly.

3. Create Golden Records

Duplicate records are merged into a single "Golden Record" - the best, most complete version of your data.

4. Synchronize & Monitor

Changes flow automatically to downstream systems. Quality scores help you track data health over time.

Key Concepts

Who Should Use This Documentation?

Business Users & Data Stewards

• Review and approve duplicate matches

• Monitor data quality metrics

• Manage master data governance

Start with: User Guide

Data Architects & System Integrators

• Design entity resolution strategies

• Configure deduplication rules

• Plan data integration workflows

Start with: Architect Guide

Software Developers

• Integrate Golden API into applications

• Automate data workflows

• Build custom data pipelines

Start with: Developer Guide | API Documentation

Common Use Cases

Customer Master Data Management

Consolidate customer records from multiple systems (CRM, e-commerce, support) into a single customer view.

What You'll Learn:

• Entities - Setting up customer deduplication

• Golden Records - Merging customer profiles

• Example: Customer Data Integration - End-to-end walkthrough

Product Information Management

Maintain accurate product catalogs with no duplicates and complete information.

What You'll Learn:

• Datasets - Defining product structures

• Data Quality - Quality monitoring

• Sinks - Synchronizing to external platforms

Data Integration & ETL

Clean, transform, and load data from legacy systems into modern data warehouses.

What You'll Learn:

• Pipelines - Data transformation workflows

• Transformations - Field mapping and normalization

• Tasks - Scheduling automated jobs

Getting Started Checklist

For new users, follow these steps:

Step 1: Understand the Basics

• Read Golden Records to understand how deduplication works

• Review Entities to learn about the core concept

Step 2: Set Up Authentication

• Configure your user account

• Understand Security and access control

Step 3: Try a Simple Workflow

• Follow Example: Customer Data Integration end-to-end

• Or try Example: Manual Deduplication Workflow

Step 4: Explore Advanced Features

• Tasks - Automate background processing

• Data Quality - Monitor and improve data health

• Pipelines - Build multi-stage transformations

Quick Links

Core Concepts

• Entities - The foundation of master data management

• Golden Records - Creating single sources of truth

• Resources - Building data integration workflows

• Tables - Data storage containers

Technical Guides

• Golden API - REST API overview

• Security - Authentication and authorization

• Tasks - Asynchronous processing

Tools & Integration

• Golden Command Line Client - CLI tool for automation

• Golden OpenAPI Library - Java client library

Best Practices

• Start small - Begin with a dataset of 100-1,000 records

• Review manually first - Check duplicate matches before automating

• Monitor quality scores - Track data health improvement over time

• Test before scale - Validate deduplication rules with test data

Next Steps

Choose your path based on your role:

Ready to begin? Choose your guide from the navigation menu and start building better data quality today.