Introduction

In the era of big data and machine learning, an efficient data infrastructure is a non-negotiable for competitive advantage. This intensive training course is meticulously designed for data professionals and cloud engineers seeking to master the art of building scalable, reproducible, and robust data platforms using modern cloud-native principles. We delve deep into the synergy between data engineering pipelines and Infrastructure as Code (IaC), with a specialized focus on Terraform. By the end of this program, you will possess the highly sought-after skills to automatically provision, configure, and manage cloud resources for your data workloads, ensuring seamless and cost-effective operations, paving the way for advanced analytics and machine learning initiatives. This is the definitive skillset for modern data platform excellence.

Target Audience

• Cloud Engineers and Architects
• Data Engineers and Scientists
• DevOps Practitioners
• Software Developers with a focus on data systems
• Data Analysts and BI Professionals
• IT Professionals transitioning to cloud-native roles
• System Administrators
• Technical project managers overseeing data projects

Course Objectives

• Understand the core principles of Infrastructure as Code and its importance in data engineering.
• Master the fundamentals of Terraform for provisioning cloud infrastructure.
• Design and implement scalable data architectures on major cloud platforms.
• Automate the deployment of data ingestion and processing pipelines.
• Secure cloud infrastructure and data resources using best practices.
• Manage and version infrastructure configurations effectively.
• Integrate Terraform with CI/CD pipelines for automated deployments.
• Monitor and log infrastructure for performance and reliability.
• Optimize cloud resource usage for cost-effectiveness.
• Implement fault-tolerant and highly available data systems.
• Build and manage a complete end-to-end cloud-native data platform.

Course Content

Module 1: Foundations of Infrastructure as Code

• What is Infrastructure as Code (IaC) and why is it essential for data engineering?
• Comparing IaC tools: Terraform, Ansible, and CloudFormation
• The role of declarative vs. imperative approaches
• Best practices for structuring IaC projects
• Introduction to cloud-native principles

Module 2: Getting Started with Terraform

• Installing and configuring Terraform
• Understanding Terraform's syntax and core concepts
• Working with providers, resources, and modules
• Managing Terraform state and remote backends
• Using variables, outputs, and data sources

Module 3: Cloud-Native Data Storage and Services

• Provisioning object storage (e.g., S3, Google Cloud Storage, Azure Blob Storage)
• Deploying and configuring relational databases
• Setting up managed data warehouses like BigQuery and Snowflake
• Creating and managing message queues and streaming services
• Configuring security groups and network settings

Module 4: Data Ingestion and Processing Pipelines

• Automating the deployment of data pipeline tools
• Creating serverless functions for data ingestion
• Provisioning managed services for real-time streaming data
• Deploying distributed computing clusters
• Configuring and managing data transformation jobs

Module 5: Data Governance and Security

• Implementing IAM roles and policies with Terraform
• Managing network security and firewall rules
• Encrypting data at rest and in transit
• Using secrets management services
• Automating compliance and policy enforcement

Module 6: Advanced Terraform Techniques

• Creating reusable modules for common infrastructure patterns
• Using loops, conditionals, and functions in HCL
• Managing multiple environments with workspaces
• Working with Terraform Registry and community modules
• Understanding and using advanced resource attributes

Module 7: CI/CD for IaC

• Integrating Terraform with popular CI/CD platforms
• Automating terraform plan and apply
• Implementing testing for infrastructure code
• Creating a robust deployment workflow
• Managing state locking and concurrency

Module 8: Monitoring and Logging

• Provisioning and configuring monitoring services
• Setting up logging for data pipelines and infrastructure
• Creating alerts and dashboards for key metrics
• Integrating with observability platforms
• Automating log collection and analysis

Module 9: Cost Management and Optimization

• Implementing resource tagging for cost allocation
• Using Terraform to enforce budget and cost controls
• Right-sizing resources based on usage
• Automating resource shutdown and cleanup
• Monitoring cost trends and anomalies

Module 10: Case Study: Building a Modern Data Lake

• Designing a data lake architecture on a cloud provider
• Provisioning all necessary storage and compute resources
• Setting up data ingestion and transformation jobs
• Implementing security and access controls
• Orchestrating the entire workflow with Terraform

Module 11: Final Project: End-to-End Platform Deployment

• Designing a complete data engineering platform
• Writing all Terraform configurations from scratch
• Deploying the platform to a cloud environment
• Documenting the infrastructure and deployment process
• Demonstrating the platform's functionality and scalability

Module 12: Integrating with Data Processing Frameworks

• Using Terraform to deploy and manage Apache Spark clusters
• Automating the setup of data warehousing solutions
• Configuring infrastructure for real-time analytics
• Deploying machine learning platforms
• Managing dependencies and versions

Module 13: Disaster Recovery and High Availability

• Designing fault-tolerant data architectures
• Implementing cross-region replication and failover
• Using Terraform to manage backups and snapshots
• Automating recovery procedures
• Ensuring business continuity and data durability

Module 14: Advanced Cloud Services for Data

• Provisioning specialized databases
• Using managed services for ETL and analytics
• Deploying containerized data applications
• Configuring autoscaling for compute resources
• Leveraging serverless computing for specific tasks

Module 15: Future Trends and Best Practices

• Exploring new features in Terraform and cloud providers
• The rise of MLOps and GitOps
• Community contributions and open-source projects
• Career paths and certifications in IaC and data engineering
• Continuous learning and skill development

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.