Cloud Data Engineer Program

Course description

A comprehensive program on designing, optimizing, and managing data pipelines, covering essential skills such as data engineering with Python and SQL, big data processing, data integration using tools like Apache Spark and Kafka, and cloud architecture on platforms such as AWS, Azure, or Google Cloud.

Course objectives

By the end of this course, participants will be able to:

• Master the fundamentals of data engineering: understand pipeline architecture, integration, transformation, and data storage.
• Use powerful tools for big data processing: master technologies such as Apache Spark and Apache Kafka for parallel processing and real-time data integration.
• Optimize performance and security of data pipelines: gain skills to optimize, secure, and monitor pipelines throughout their lifecycle.
• Manage workflows with orchestration tools: use tools like Airflow or Prefect to automate and orchestrate tasks and processes across pipelines.
• Design and deploy an end-to-end pipeline: build a full pipeline from ingestion to analysis, including performance optimization and error handling in production.

Who is this course for?

This program is designed for a broad audience, including:

• Developers and engineers who want to specialize in data management.
• Data analysts looking to deepen skills in managing and processing large data volumes.
• Junior data scientists who want to master data infrastructure to prepare their models.
• Database administrators expanding into complex data systems.
• Cloud professionals seeking to understand cloud data architectures.
• Recent graduates or career changers interested in Data Engineering.
• Technical leads or CTOs who want to better oversee data management projects.

Prerequisites

No specific prerequisites required.

Course curriculum

Days 1–2: Introduction to Data Engineering

• Goal: Understand the fundamentals of data pipelines—their architecture and how they work.

Introduction to data pipelines

• Pipeline principles: architecture, data flow, integration, transformation, and storage.
• Key concepts: ETL vs. ELT; managing structured and unstructured data.
• Introduction to Apache Kafka and Apache Spark for big data processing.

Core tools for data engineering

• Python with Pandas for data handling: manipulation, cleaning, and transformation.
• Intro to SQL: SELECT, JOINs, aggregations, and query optimization.
• Overview of NumPy and Matplotlib for computation and visualization.

Days 3–4: Introduction to Apache Spark and Kafka

• Goal: Learn to use Apache Spark for parallel processing and large-scale data.

Using Apache Spark

• Setting up Spark; RDD vs. DataFrame: differences and when to use each.
• Spark operations: map, filter, reduce, groupBy, and performance optimization.
• Caching and partitioning to speed up big data processing.

Kafka for real-time data streaming

• Kafka architecture: producers, consumers, brokers, topics, partitions.
• Using Kafka Streams to process data in real time.
• Integrating Kafka with Spark for streaming analytics.

Days 5–6: Pipeline optimization

• Goal: Optimize pipeline performance and secure data flows.

Performance optimization

• Resource management, data partitioning, and parallelism to improve performance.
• Best practices to secure pipelines: authentication, encryption, and error handling.

Securing and monitoring pipelines

• Data integrity monitoring and error management across pipelines.
• Using monitoring tools to ensure robust, efficient pipelines.

Day 7: Orchestration and pipeline management

• Goal: Manage workflows with orchestration tools.

Intro to pipeline orchestration

• Tools such as Apache Airflow, Luigi, or Prefect to orchestrate data pipelines.
• Automating workflows and managing task dependencies.

Error handling and data quality

• Ensuring data quality in pipelines: input validation and cleaning.
• Error handling: capturing and managing anomalies in automated pipelines.

Day 8: Final project — Build a complete pipeline

• Goal: Deploy an end-to-end pipeline using Kafka, Spark, and orchestration tools.

Design and development

• Design a pipeline integrating the studied tools: data collection, transformation, and analysis.

Production deployment and operations

• Pipeline optimization: performance, error handling, and scalability in production.
• Real-time streaming with Kafka and large-scale processing with Spark.

Course highlights

• Modular pedagogy: alternating theory and practice for better learning.
• Cloud integration: strong focus on cloud and distributed solutions.
• Qualified instructors: practitioners with real-world experience.
• Tools and learning resources: online materials, live demos, and real case studies.
• Accessibility: open to all, no advanced technical prerequisites.
• Hands-on: a full project at the end to consolidate learning.
• Industry readiness: focus on certifications and standard tools used by professionals.

Teaching methods and tools

• Live demonstrations with data engineering services.
• Hands-on workshops and real case studies across industries (manufacturing, retail, healthcare).
• Experience sharing: best practices and common pitfalls in companies.
• Simulations and tools: use of simulators for interactive labs.

Assessment

• End-of-course multiple-choice quiz to test understanding.
• Practical case studies or group discussions to apply knowledge.
• Ongoing assessment during hands-on sessions.
• Hands-on: a complete project at the end of the modules to consolidate learning.

Standards & references

• Well-Architected Cloud Framework.
• GDPR (General Data Protection Regulation).
• ISO 27001, SOC 2 (Service Organization Control).
• NIST Cybersecurity Framework.