Data Engineer

Mastering Data Engineering: From Fundamentals to Advanced Techniques

Course Description:

This comprehensive course is designed to provide students with in-depth knowledge and practical skills in data engineering. Participants will learn to design, build, and manage scalable data infrastructure, implement data pipelines, and work with various data storage solutions. The course covers essential tools and technologies, including SQL, Python, ETL processes, big data platforms, and cloud services. It is suitable for beginners and intermediate users aiming to enhance their proficiency in data engineering.

Course Duration:

12 Weeks (3 hours per week)

Week 1: Introduction to Data Engineering

• Overview of Data Engineering
• Role and responsibilities of a Data Engineer
• Introduction to data architecture and data flow
• Overview of key tools and technologies

Week 2: SQL for Data Engineering

• Basics of SQL
• Writing and executing SQL queries
• Data manipulation and transaction management
• Advanced SQL concepts (joins, subqueries, indexes)

Week 3: Programming with Python

• Introduction to Python for data engineering
• Data manipulation with Pandas
• Working with NumPy for numerical operations
• Writing and debugging Python scripts

Week 4: Data Warehousing

• Concepts of data warehousing
• Star schema and snowflake schema
• Setting up and managing a data warehouse
• Using tools like Amazon Redshift, Google BigQuery

Week 5: ETL Processes

• Understanding ETL (Extract, Transform, Load)
• Designing ETL workflows
• Using ETL tools (Apache Nifi, Talend)
• Implementing ETL pipelines with Python

Week 6: Big Data Technologies

• Introduction to big data and Hadoop ecosystem
• Working with HDFS (Hadoop Distributed File System)
• Using Apache Spark for big data processing
• Batch processing vs. stream processing

Week 7: Data Lakes

• Understanding data lakes and their architecture
• Setting up a data lake
• Differences between data lakes and data warehouses
• Tools for managing data lakes (Azure Data Lake, AWS Lake Formation)

Week 8: Cloud Data Engineering

• Overview of cloud platforms (AWS, Azure, GCP)
• Setting up cloud-based data infrastructure
• Using cloud-native tools (AWS Glue, Google Dataflow)
• Managing data storage in the cloud

Week 9: Data Integration and APIs

• Data integration techniques
• Using APIs for data exchange
• Working with RESTful APIs
• Integrating data from multiple sources

Week 10: Data Pipeline Orchestration

• Introduction to workflow orchestration
• Using Apache Airflow for task scheduling
• Designing and managing complex data pipelines
• Monitoring and troubleshooting pipelines

Week 11: Data Security and Compliance

• Importance of data security
• Implementing data encryption and access controls
• Understanding data privacy laws (GDPR, CCPA)
• Ensuring compliance in data handling

Week 12: Capstone Project and Review

• Practical application: Building a complete data pipeline
• Peer review and feedback
• Final Q&A and course recap