Module 1: Introduction to Data Engineering

• Understanding the role of data engineering in the data lifecycle 

• Differentiating between data engineering and data science 

• Overview of data engineering tools and technologies 

Module 2: Data Modeling and Database Management

• Introduction to data modeling concepts (relational, NoSQL) 

• Designing and creating relational databases (e.g., MySQL, PostgreSQL) 

• Introduction to NoSQL databases (e.g., MongoDB, Cassandra) 

Module 3: Data Integration and ETL Processes

• Extract, Transform, Load (ETL) process fundamentals 

• Exploring ETL tools (e.g., Apache NiFi, Apache Airflow) 

• Hands-on: Building a simple ETL pipeline 

Module 4: Big Data and Distributed Computing

• Introduction to big data concepts and challenges 

• Overview of distributed computing frameworks (e.g., Hadoop, Spark) 

• Hands-on: Processing data using Apache Spark 

Module 5: Data Warehousing and Data Lakes

• Understanding data warehousing architecture 

• Introduction to cloud-based data warehousing (e.g., Amazon Redshift, Google BigQuery) 

• Creating and querying data warehouses 

Module 6: Streaming Data and Real-time Processing

• Exploring streaming data concepts 

• Introduction to stream processing frameworks (e.g., Apache Kafka, Apache Flink) 

• Building a simple real-time data pipeline 

Module 7: Data Quality and Governance

• Importance of data quality and data governance 

• Implementing data validation and cleaning processes 

• Ensuring data security and compliance 

Module 8: Scalable Infrastructure and Cloud Services

• Overview of cloud computing and its benefits 

• Introduction to cloud services for data engineering (e.g., AWS, Azure, Google Cloud) 

• Deploying data engineering solutions on the cloud 

Module 9: Workflow Orchestration and Automation

• Managing complex workflows using orchestration tools (e.g., Apache Airflow, Luigi) 

• Creating automated data pipelines.
• Hands-on: Designing and scheduling data workflows