Exclusive Hands-On Course to Master Data Architecture and Build Scalable, Real-World Data Systems

Virtual Classroom Live

Data Architecture

This course provides an in-depth exploration of modern data architecture principles, design strategies, and real-world implementation practices. You'll gain expertise in designing efficient data models, integrating structured and unstructured data, optimizing schemas, and architecting both cloud-native and hybrid data platforms. You'll also build experience in real-time processing systems using tools like Kafka while mastering best practices in data governance, security, and quality. By course completion, you’ll confidently design end-to-end data architectures that support AI/ML workflows and enterprise-scale business needs. 

Familiarity with database systems, SQL, and data management required

Experience with at least one programming language (Python, Java, or Scala) 

Understanding of ETL processes, cloud platforms, and modern data architectures 

Awareness of big data tools (Hadoop, Spark) and governance principles 

Familiarity with data modeling techniques (star schema, snowflake schema) 

We’ve teamed up with Global Knowledge (GK)—a worldwide leader in professional IT training and certifications and part of the Skillsoft family to bring you targeted, high-impact, industry-relevant courses to help you advance your skills and achieve your career goals.

While you'll enroll and check out through Codecademy, your course experience—including instruction, materials, and support—will be delivered by Global Knowledge. Global Knowledge offers courses designed to help you sharpen your skills, boost your credentials, and move your career forward.

Do you build pipelines and scalable systems?

This course is ideal for data engineers who want to advance their ability to design and implement modern, end-to-end data architectures for real-world business and AI needs.

Are you a database administrator looking to modernize your approach?

Perfect for DBAs ready to move beyond traditional relational databases into hybrid, cloud-native, and real-time data solutions.

Do you manage enterprise-scale platforms?

Tailored for big data professionals working with tools like Hadoop and Spark who need to design systems that handle volume, velocity, and variety with architectural rigor.

Are you looking to understand data architecture more deeply?

Great for analysts and scientists seeking deeper knowledge of the systems, flows, and governance that power their data pipelines and models.

Do you design infrastructure for data-driven apps?

Built for cloud architects looking to integrate scalable data storage, processing, and security into AI/ML-ready cloud architectures.

Create and optimize entity-relationship diagrams (ERDs) for scalable and high-performing databases.

Solve practical data modeling problems and refine schemas for flexibility and performance.

Learn the foundational responsibilities and concepts that define modern data architecture roles.

Architect unified platforms that handle both traditional databases and unstructured big data.

Design scalable solutions using cloud services and hybrid infrastructure strategies.

Develop reliable data pipelines for batch and real-time ingestion, transformation, and loading.

Use tools like Apache Kafka to support real-time data streaming and low-latency processing.

Ensure your architecture supports data integrity, regulatory compliance, and secure access control.

Create data architectures that enable machine learning model development, deployment, and iteration.

Bring together all components to design complete, enterprise-scale data ecosystems that meet real business needs.

Key concepts

1

Modern Data Modeling and Schema Optimization

2

Cloud and Hybrid Data Platform Architecture

3

Data Integration and Real-Time Processing

4

Data Governance, Quality, and Security

5

Architecting Data Systems for AI/ML Workflows

Data Architecture

1

Introduction to Data Architecture 

2

Data Modeling Concepts & ERDs 

3

Relational vs Modern Data Warehouses 

4

NoSQL Databases Overview 

5

Data Lakes and Delta Lakes 

1

Cloud Data Architectures 

2

OLTP vs OLAP Workloads 

3

Lambda and Kappa Architectures 

4

ETL vs ELT Approaches 

5

Data Pipelines for AI/ML Applications 

1

Scalable Data Architectures for AI/ML Workflows 

2

Deployment & Optimization of AI/ML Data Systems 

3

Data Governance, Stewardship, and Quality Frameworks 

4

Data Security Policies, Access Controls, and Compliance Standards