Description

Introduction

Data architects play a critical role in creating scalable, reusable, and maintainable ETL frameworks. This course focuses on how to leverage Matillion’s modular design, parameterization, and job orchestration features to develop robust ETL frameworks that support data governance, reduce duplication, and enable team collaboration across projects.

Prerequisites

• Strong understanding of data modeling and ETL concepts

• Experience using Matillion ETL

• Familiarity with data warehouses such as Snowflake, Redshift, or BigQuery

• Basic knowledge of scripting (SQL, Python) and DevOps practices is a plus

Table of Contents

1. Role of a Data Architect in ETL Frameworks
    1.1 Defining ETL Standards and Architecture
    1.2 Aligning with Business and Technical Goals
    1.3 Framework Components Overview

2. Designing Modular ETL Jobs
    2.1 Creating Reusable Orchestration and Transformation Jobs
    2.2 Job Linking Strategies
    2.3 Organizing Projects for Scalability

3. Implementing Parameterization and Job Variables
    3.1 Using Grid Variables and Environment Variables
    3.2 Dynamic Table and File Names
    3.3 Passing Parameters Between Jobs

4. Metadata-Driven ETL Frameworks
    4.1 Centralizing Configuration in Metadata Tables
    4.2 Looping Through Metadata Using Iterator Components
    4.3 Building Frameworks for Multi-Table Loads

5. Data Quality and Validation Layers
    5.1 Creating Data Quality Checks as Sub-jobs
    5.2 Logging Errors and Failures
    5.3 Designing Reconciliation Reports

6. Frameworks for Incremental and Full Loads
    6.1 Handling Change Data Capture (CDC)
    6.2 Designing Jobs for Time-Based Ingestion
    6.3 Maintaining Audit and Control Columns

7. Standardized Logging and Monitoring
    7.1 Building a Central Logging Framework
    7.2 Tracking Job Run History and Performance
    7.3 Integrating Logs with External Monitoring Tools

8. Job Scheduling and Dependency Management
    8.1 Using Matillion’s Task Scheduler
    8.2 Managing Job Dependencies and Triggers
    8.3 Handling Failures and Retry Logic

9. Framework Deployment and Version Control
    9.1 Exporting and Importing Frameworks
    9.2 Managing Version Control with Git
    9.3 Promoting Frameworks Across Environments

10. Governance, Documentation, and Best Practices
    10.1 Documenting Framework Design
    10.2 Enforcing Naming and Design Standards
    10.3 Ensuring Compliance and Audit Readiness

By designing reusable ETL frameworks in Matillion, data architects can standardize development, accelerate project delivery, and improve data reliability across teams. These frameworks lay the foundation for sustainable data pipelines that adapt easily to future requirements and evolving architectures.