Introduction

In today’s data-driven world, designing scalable data architectures is crucial for organizations to manage and leverage their data effectively. This comprehensive training course focuses on the end-to-end design of cloud architectures, optimizing them for Big Data processing, real-time analytics, and predictive modeling using machine learning (ML) and artificial intelligence (AI). By the end of this course, participants will have the skills and knowledge to build robust, scalable data architectures that can handle expanding data volumes and future growth.

Objectives

• Understand the principles of scalable data architectures.
• Learn to design and implement data systems that can handle large volumes of data.
• Master techniques for real-time data processing and analytics.
• Develop strategies for data migration and disaster recovery.
• Gain hands-on experience with industry-leading tools and technologies.
• Prepare for future challenges in data management and analytics.

Who Should Attend

• Data Architects
• Database Administrators
• Business Intelligence Professionals
• Data Scientists
• IT Managers
• Cloud Architects
• Anyone involved in data management and analytics

Course Outline

Day 1: Introduction to Scalable Data Architectures

• Session 1: Fundamentals of Data Architecture
  • Overview of data architecture principles.
  • Importance of scalability in data systems.
• Session 2: Big Data Processing
  • Techniques for processing large datasets.
  • Introduction to Big Data technologies (e.g., Hadoop, Spark).
• Session 3: Real-Time Analytics
  • Concepts of real-time data processing.
  • Tools and technologies for real-time analytics.

Day 2: Designing for Scalability

• Session 1: Data Storage Solutions
  • Overview of scalable data storage options.
  • Designing data lakes and data warehouses.
• Session 2: Data Integration Techniques
  • Methods for integrating data from various sources.
  • ETL (Extract, Transform, Load) processes.
• Session 3: Data Modeling
  • Best practices for data modeling in scalable architectures.
  • Hands-on exercises in data modeling.

Day 3: Real-Time Data Processing and Analytics

• Session 1: Stream Processing Architectures
  • Importance of stream processing in real-time analytics.
  • Designing lambda and kappa architectures.
• Session 2: Machine Learning and AI Integration
  • Incorporating ML and AI into data architectures.
  • Choosing the right ML workflows for your enterprise.
• Session 3: Hands-On Labs
  • Practical exercises on real-time data processing and analytics.

Day 4: Data Migration and Disaster Recovery

• Session 1: Migration Strategies
  • Planning and executing data migration projects.
  • Tools and best practices for data migration.
• Session 2: Disaster Recovery Planning
  • Designing robust disaster recovery strategies.
  • Implementing backup and recovery solutions.
• Session 3: Case Studies
  • Real-world examples of successful data migration and disaster recovery.

Day 5: Advanced Topics and Future Trends

• Session 1: Advanced Data Architecture Techniques
  • Exploring advanced topics in data architecture.
  • Emerging trends and technologies.
• Session 2: Performance Optimization
  • Techniques for optimizing data architecture performance.
  • Monitoring and tuning data systems.
• Session 3: Capstone Project
  • Participants will work on a capstone project to apply the techniques and tools learned throughout the course.
  • Presentation and discussion of project outcomes.