Learn Data Engineering

Data Engineering is the process of designing, building, and maintaining systems for data collection, storage, and processing, enabling efficient analysis and data-driven decision-making.

Introduction

• Definition of Data Engineering
• Importance in the Data Ecosystem
• Difference Between Data Engineering and Data Science

Fundamentals of Data Engineering

• Basic data concepts
  • Data types
  • Data structures
• Data architecture
  • Relational and non-relational databases
  • Data Warehousing vs. Data Lake
• ETL (Extract, Transform, Load)
  • Concepts and processes
  • Common tools (e.g., Apache Nifi, Talend)

Tools and Technologies

• Databases
  • SQL (MySQL, PostgreSQL, SQL Server)
  • NoSQL (MongoDB, Cassandra, Redis)
• Processing systems
  • Batch vs. Streaming
  • Apache Hadoop, Apache Spark
• Orchestration tools
  • Apache Airflow, Luigi

Data Pipeline Development

• Pipeline design
  • Pipeline architecture
  • Best practices
• Implementation
  • Programming in Python, Java, Scala
  • Using specific tools (e.g., dbt, Airflow)

Data Storage and Management

• Data Warehousing
  • Data modeling (Star, Snowflake)
  • Tools (e.g., Amazon Redshift, Google BigQuery)
• Data Lakes
  • Architecture and use cases
  • Tools (e.g., AWS S3, Azure Data Lake)

Data Quality and Governance

• Data Quality
  • Data validation and cleaning
• Data Governance
  • Policies and procedures
  • Data security and privacy

Scalability and Performance

• System scalability
  • Horizontal vs. vertical
• Performance optimization
  • Techniques and tools

Case Studies and Applications

• Case studies
  • Real-world examples of Data Engineering implementation
• Practical applications
  • Integration with BI tools
  • Data analysis and visualization

Trends and the Future of Data Engineering

• New technologies
• Emerging trends
• Impact of AI and Machine Learning

Questions