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Data Engineering: Syllabus
Mastering Data Engineering: Concepts, Techniques & Best Practices
Data Engineering is the backbone of modern data-driven organizations. It focuses on designing, building, and maintaining scalable data pipelines that enable efficient data processing, storage, and retrieval. This book provides a hands-on approach to mastering data engineering principles, covering data pipelines, ETL workflows, data governance, and real-world best practices.
Module 1: Introduction to Data Engineering
- What is Data Engineering? Why is it important?
- Role of a Data Engineer vs. Data Scientist vs. Data Analyst
- Overview of modern data architectures (Data Warehouses, Data Lakes, Lakehouses)
- Understanding batch vs. real-time data processing
Module 2: Data Storage & Databases
- Understanding relational databases (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Cassandra)
- Data Warehouses vs. Data Lakes (Redshift, Snowflake, Delta Lake, BigQuery)
- Columnar vs. Row-based storage formats (Parquet, Avro, ORC)
- Best practices for data partitioning and indexing
Module 3: Data Ingestion & ETL (Extract, Transform, Load)
- Principles of ETL and ELT workflows
- Data ingestion techniques: Batch, Stream, CDC (Change Data Capture)
- Extracting data from APIs, Databases, and Files (CSV, JSON, XML)
- Transforming data using SQL, Python (Pandas), and Apache Spark
- Loading data into Data Warehouses and Data Lakes
Module 4: Workflow Orchestration & Automation
- Introduction to workflow orchestration tools (Apache Airflow, Prefect, Dagster)
- Scheduling and automating ETL pipelines
- Monitoring data pipelines with logging and alerting
- Implementing retries and failure handling in workflows
Module 5: Real-Time Data Processing & Streaming
- Introduction to real-time data architectures
- Streaming vs. Batch processing: Key differences
- Apache Kafka for real-time data ingestion and processing
- Processing streaming data with Apache Spark Streaming and Flink
Module 6: Data Modeling & Schema Design
- Normalization vs. Denormalization
- Star and Snowflake Schema for Data Warehouses
- Designing efficient schemas for Data Lakes and NoSQL databases
- Handling schema evolution in production systems
Module 7: Data Governance & Quality
- Ensuring data reliability and consistency
- Data validation techniques (Great Expectations, dbt tests)
- Implementing data lineage and metadata management (Apache Atlas)
- Security best practices: Encryption, RBAC, GDPR compliance
Module 8: Performance Optimization & Scalability
- Query optimization techniques for large datasets
- Indexing and partitioning strategies for better performance
- Optimizing Apache Spark jobs for efficiency
- Scaling data pipelines with distributed computing
Hands-On Examples & Best Practices
Example 1: Building an ETL Pipeline with Apache Airflow
- Extract data from an API and store it in a PostgreSQL database
- Transform data using Pandas and Apache Spark
- Automate the workflow using Apache Airflow DAGs
Example 2: Real-Time Streaming Pipeline with Kafka & Spark
- Stream data from a Kafka topic into a Data Lake
- Process real-time events with Apache Spark Streaming
- Store transformed data in Delta Lake for analysis
Example 3: Data Warehouse Optimization with Partitioning & Indexing
- Optimize PostgreSQL queries using indexes and partitions
- Tune a Snowflake data warehouse for efficient querying
- Use columnar storage formats for performance improvements
Example 4: Data Governance with Great Expectations & Apache Atlas
- Implement data quality checks in an ETL pipeline
- Track data lineage and metadata for compliance
- Automate alerts for data anomalies
Example 5: Deploying a Scalable Data Pipeline on Kubernetes
- Containerize an ETL pipeline using Docker
- Deploy data processing jobs on Kubernetes
- Implement CI/CD for data pipeline automation