This comprehensive course spans 4 months (16 weeks) and equips learners with expertise in Python, SQL, Azure, AWS, Apache Airflow, Kafka, Spark, and more.

• Learning Days: Monday to Thursday (theory and practice).
• Friday: Job shadowing or peer projects.
• Saturday: Hands-on lab sessions and project-based learning.

• Monday:
  • Onboarding, course overview, career pathways, tools introduction.
• Tuesday:
  • Introduction to cloud computing (Azure and AWS).
• Wednesday:
  • Data governance, security, compliance, and access control.
• Thursday:
  • Introduction to SQL for data engineering and PostgreSQL setup.
• Friday:
  • Peer Project: Environment setup challenges.
• Saturday (Lab):
  • Mini Project: Build a basic pipeline with PostgreSQL and Azure Blob Storage.

• Monday:
  • Core SQL concepts (SELECT, WHERE, JOIN, GROUP BY).
• Tuesday:
  • Advanced SQL techniques: recursive queries, window functions, and CTEs.
• Wednesday:
  • Query optimization and execution plans.
• Thursday:
  • Data modeling: normalization, denormalization, and star schemas.
• Friday:
  • Job Shadowing: Observe senior engineers writing and optimizing SQL queries.
• Saturday (Lab):
  • Mini Project: Create a star schema and analyze data using SQL.

• Monday:
  • Theory: Introduction to ETL/ELT workflows.
• Tuesday:
  • Lab: Create a simple Python-based ETL pipeline for CSV data.
• Wednesday:
  • Theory: Extract, transform, load (ETL) concepts and best practices.
• Thursday:
  • Lab: Build a Python ETL pipeline for batch data processing.
• Friday:
  • Peer Project: Collaborate to design a basic ETL workflow.
• Saturday (Lab):
  • Mini Project: Develop a simple ETL pipeline to process sales data.

• Monday:
  • Theory: Introduction to Apache Airflow, DAGs, and scheduling.
• Tuesday:
  • Lab: Set up Apache Airflow and create a basic DAG.
• Wednesday:
  • Theory: DAG best practices and scheduling in Airflow.
• Thursday:
  • Lab: Integrate Airflow with PostgreSQL and Azure Blob Storage.
• Friday:
  • Job Shadowing: Observe real-world Airflow pipelines.
• Saturday (Lab):
  • Mini Project: Automate an ETL pipeline with Airflow for batch data processing.

• Monday:
  • Theory: Introduction to data warehousing (OLAP vs. OLTP, partitioning, clustering).
• Tuesday:
  • Lab: Work with Amazon Redshift and Snowflake for data warehousing.
• Wednesday:
  • Theory: Data lakes and Lakehouse architecture.
• Thursday:
  • Lab: Set up Delta Lake for raw and curated data.
• Friday:
  • Peer Project: Implement a data warehouse model and data lake for sales data.
• Saturday (Lab):
  • Mini Project: Design and implement a basic Lakehouse architecture.

• Monday:
  • Theory: Data governance frameworks and data security principles.
• Tuesday:
  • Lab: Use AWS Lake Formation for access control and security enforcement.
• Wednesday:
  • Theory: Managing sensitive data and compliance (GDPR, HIPAA).
• Thursday:
  • Lab: Implement security policies in S3 and Azure Blob Storage.
• Friday:
  • Job Shadowing: Observe senior engineers applying governance policies.
• Saturday (Lab):
  • Mini Project: Secure data in the cloud using AWS and Azure.

• Monday:
  • Theory: Introduction to Apache Kafka for real-time data streaming.
• Tuesday:
  • Lab: Set up a Kafka producer and consumer.
• Wednesday:
  • Theory: Kafka topics, partitions, and message brokers.
• Thursday:
  • Lab: Integrate Kafka with PostgreSQL for real-time updates.
• Friday:
  • Peer Project: Build a real-time Kafka pipeline for transactional data.
• Saturday (Lab):
  • Mini Project: Create a pipeline to stream e-commerce data with Kafka.

• Monday:
  • Theory: Introduction to batch vs. stream processing.
• Tuesday:
  • Lab: Batch processing with PySpark.
• Wednesday:
  • Theory: Combining batch and stream processing workflows.
• Thursday:
  • Lab: Real-time processing with Apache Flink and Spark Streaming.
• Friday:
  • Job Shadowing: Observe a real-time processing pipeline.
• Saturday (Lab):
  • Mini Project: Build a hybrid pipeline combining batch and real-time processing.

• Monday:
  • Theory: Overview of ML workflows in data engineering.
• Tuesday:
  • Lab: Preprocess data for machine learning using Pandas and PySpark.
• Wednesday:
  • Theory: Feature engineering and automated feature extraction.
• Thursday:
  • Lab: Automate feature extraction using Apache Airflow.
• Friday:
  • Peer Project: Build a simple pipeline that integrates ML models.
• Saturday (Lab):
  • Mini Project: Build an ML-powered recommendation system in a pipeline.

• Monday:
  • Theory: Introduction to Apache Spark for big data processing.
• Tuesday:
  • Lab: Set up Spark and PySpark for data analysis.
• Wednesday:
  • Theory: Spark RDDs, DataFrames, and SQL.
• Thursday:
  • Lab: Analyze large datasets using Spark SQL.
• Friday:
  • Peer Project: Build a PySpark pipeline for large-scale data processing.
• Saturday (Lab):
  • Mini Project: Analyze big data sets with Spark and PySpark.

• Monday:
  • Theory: Advanced Airflow features (XCom, task dependencies).
• Tuesday:
  • Lab: Implement dynamic DAGs and task dependencies in Airflow.
• Wednesday:
  • Theory: Airflow scheduling, monitoring, and error handling.
• Thursday:
  • Lab: Create complex DAGs for multi-step ETL pipelines.
• Friday:
  • Job Shadowing: Observe advanced Airflow pipeline implementations.
• Saturday (Lab):
  • Mini Project: Design an advanced Airflow DAG for complex data workflows.

• Monday:
  • Theory: Data lakes, Lakehouses, and Delta Lake architecture.
• Tuesday:
  • Lab: Set up Delta Lake on AWS for data storage and management.
• Wednesday:
  • Theory: Managing schema evolution in Delta Lake.
• Thursday:
  • Lab: Implement batch and real-time data loading to Delta Lake.
• Friday:
  • Peer Project: Design a Lakehouse architecture for an e-commerce platform.
• Saturday (Lab):
  • Mini Project: Implement a scalable Delta Lake architecture.

• Monday to Thursday:
  • Design and Implementation:
    • Build an end-to-end batch data pipeline for e-commerce sales analytics.
  • Tools: PySpark, SQL, PostgreSQL, Airflow, S3.
• Friday:
  • Peer Review: Present progress and receive feedback.
• Saturday (Lab):
  • Project Milestone: Finalize and present batch pipeline results.

• Monday to Thursday:
  • Design and Implementation:
    • Build an end-to-end real-time data pipeline for IoT sensor monitoring.
  • Tools: Kafka, Spark Streaming, Flink, S3.
• Friday:
  • Peer Review: Present progress and receive feedback.
• Saturday (Lab):
  • Project Milestone: Finalize and present real-time pipeline results.

• Monday to Thursday:
  • Design and Implementation:
    • Integrate both batch and real-time pipelines for a comprehensive end-to-end solution.
  • Tools: Kafka, PySpark, Airflow, Delta Lake, PostgreSQL, and S3.
• Friday:
  • Job Shadowing: Observe senior engineers integrating complex pipelines.
• Saturday (Lab):
  • Project Milestone: Showcase integrated solution for review.

• Monday to Thursday:
  • Final Presentation Preparation:
    • Polish, test, and document the final project.
• Friday:
  • Peer Review: Present final projects to peers and receive feedback.
• Saturday (Lab):
  • Capstone Presentation: Showcase completed capstone projects to industry professionals and instructors.