Comprehensive LuxDevHQ Data Engineering Course Guide
This intensive 16-week (4-month) data engineering bootcamp provides comprehensive training in Python, SQL, cloud platforms (Azure and AWS), Apache Airflow, Kafka, Spark, and more.
Schedule:
- Monday - Thursday: Lectures and hands-on exercises.
- Friday: Industry mentorship or collaborative peer projects.
- Saturday: Dedicated lab sessions and project-based learning.
Module 1: Data Engineering Fundamentals (Weeks 1-4)
Week 1: Onboarding & Setup
- Monday: Welcome, course overview, career paths, tool introductions.
- Tuesday: Cloud computing overview (Azure & AWS).
- Wednesday: Data governance, security, and compliance.
- Thursday: SQL fundamentals and PostgreSQL setup.
- Friday: Peer project: Environment setup challenges.
- Saturday (Lab): Mini-project: Basic pipeline using PostgreSQL and Azure Blob Storage.
Week 2: Mastering SQL
- Monday: Core SQL (SELECT, WHERE, JOIN, GROUP BY).
- Tuesday: Advanced SQL (recursive queries, window functions, CTEs).
- Wednesday: Query optimization and execution plans.
- Thursday: Data modeling (normalization, denormalization, star schemas).
- Friday: Job shadowing: Observing SQL query optimization techniques.
- Saturday (Lab): Mini-project: Star schema design and SQL-based data analysis.
Week 3: Data Pipeline Introduction
- Monday: ETL/ELT workflow theory.
- Tuesday: Lab: Python-based ETL pipeline for CSV data.
- Wednesday: ETL best practices.
- Thursday: Lab: Python ETL pipeline for batch data processing.
- Friday: Peer project: Collaborative ETL workflow design.
- Saturday (Lab): Mini-project: ETL pipeline for sales data processing.
Week 4: Apache Airflow Fundamentals
- Monday: Introduction to Apache Airflow, DAGs, and scheduling.
- Tuesday: Lab: Setting up and creating a basic Airflow DAG.
- Wednesday: Airflow DAG best practices and scheduling.
- Thursday: Lab: Integrating Airflow with PostgreSQL and Azure Blob Storage.
- Friday: Job shadowing: Real-world Airflow pipeline observation.
- Saturday (Lab): Mini-project: Automating an ETL pipeline with Airflow.
Module 2: Intermediate Skills (Weeks 5-8)
Week 5: Data Warehousing & Lakes
- Monday: Data warehousing (OLAP vs. OLTP, partitioning, clustering).
- Tuesday: Lab: Working with Amazon Redshift and Snowflake.
- Wednesday: Data lakes and Lakehouse architecture.
- Thursday: Lab: Setting up Delta Lake.
- Friday: Peer project: Implementing a data warehouse and data lake model.
- Saturday (Lab): Mini-project: Designing and implementing a basic Lakehouse architecture.
Week 6: Data Governance & Security
- Monday: Data governance frameworks and security principles.
- Tuesday: Lab: Using AWS Lake Formation for access control.
- Wednesday: Managing sensitive data and compliance (GDPR, HIPAA).
- Thursday: Lab: Implementing security policies in S3 and Azure Blob Storage.
- Friday: Job shadowing: Observing the application of governance policies.
- Saturday (Lab): Mini-project: Securing cloud data using AWS and Azure.
Week 7: Real-Time Data with Kafka
- Monday: Introduction to Apache Kafka for real-time data streaming.
- Tuesday: Lab: Setting up a Kafka producer and consumer.
- Wednesday: Kafka topics, partitions, and message brokers.
- Thursday: Lab: Integrating Kafka with PostgreSQL for real-time updates.
- Friday: Peer project: Building a real-time Kafka pipeline.
- Saturday (Lab): Mini-project: Streaming e-commerce data with Kafka.
Week 8: Batch vs. Stream Processing
- Monday: Batch vs. stream processing comparison.
- Tuesday: Lab: Batch processing with PySpark.
- Wednesday: Combining batch and stream processing workflows.
- Thursday: Lab: Real-time processing with Apache Flink and Spark Streaming.
- Friday: Job shadowing: Observing a real-time processing pipeline.
- Saturday (Lab): Mini-project: Building a hybrid batch/real-time pipeline.
Module 3: Advanced Data Engineering (Weeks 9-12)
Week 9: ML Integration in Data Pipelines
- Monday: Overview of ML workflows in data engineering.
- Tuesday: Lab: Data preprocessing for ML using Pandas and PySpark.
- Wednesday: Feature engineering and automated feature extraction.
- Thursday: Lab: Automating feature extraction using Apache Airflow.
- Friday: Peer project: Building a pipeline integrating ML models.
- Saturday (Lab): Mini-project: Building an ML-powered recommendation system.
Week 10: Spark & PySpark for Big Data
- Monday: Introduction to Apache Spark.
- Tuesday: Lab: Setting up Spark and PySpark.
- Wednesday: Spark RDDs, DataFrames, and SQL.
- Thursday: Lab: Analyzing large datasets using Spark SQL.
- Friday: Peer project: Building a PySpark pipeline for large-scale data processing.
- Saturday (Lab): Mini-project: Analyzing big datasets with Spark and PySpark.
Week 11: Advanced Apache Airflow
- Monday: Advanced Airflow features (XCom, task dependencies).
- Tuesday: Lab: Implementing dynamic DAGs and task dependencies.
- Wednesday: Airflow scheduling, monitoring, and error handling.
- Thursday: Lab: Creating complex DAGs for multi-step ETL pipelines.
- Friday: Job shadowing: Observing advanced Airflow pipeline implementations.
- Saturday (Lab): Mini-project: Designing an advanced Airflow DAG.
Week 12: Data Lakes & Delta Lake
- Monday: Data lakes, Lakehouses, and Delta Lake architecture.
- Tuesday: Lab: Setting up Delta Lake on AWS.
- Wednesday: Managing schema evolution in Delta Lake.
- Thursday: Lab: Implementing batch and real-time data loading to Delta Lake.
- Friday: Peer project: Designing a Lakehouse architecture.
- Saturday (Lab): Mini-project: Implementing a scalable Delta Lake architecture.
Module 4: Capstone Projects (Weeks 13-16)
Weeks 13-16: Capstone Project Development & Presentation
These weeks focus on developing and presenting two major capstone projects: a batch data pipeline (e-commerce sales analytics) and a real-time data pipeline (IoT sensor monitoring), culminating in an integrated solution showcasing both. The final week involves project presentations to industry professionals and instructors.
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