Data Engineering Course

Data Engineering Course

This course gives you an expert guide into data engineering, covering key concepts, tools, and techniques used to design and build scalable data infrastructure. Students will learn how to design data pipelines, manage data storage systems, and implement data processing workflows using popular technologies and frameworks. The course will include hands-on exercises and projects to reinforce learning and develop practical data engineering skills.

Learning Outcome

This course outline provides a structured approach to learning data engineering, covering key topics and skills necessary for understanding and applying data engineering techniques in various contexts. You will be job ready after taking this course

  • Database management systems
  • Master workflow management systems
  • Designing data warehouse architectures
  • Build job-ready portfolios

Course Outline

Here's a simple breakdown of what you'll learn if you enroll for the Data Science Course Today:.

  • Overview of data engineering and its importance
  • Role of data engineers in the data lifecycle
  • Key concepts and terminology in data engineering

  • Relational databases vs. NoSQL databases
  • Data modeling and schema design
  • Database management systems (MySQL, PostgreSQL, MongoDB)

  • Data ingestion techniques (batch vs. streaming)
  • Introduction to data serialization formats (JSON, Avro, Protocol Buffers)
  • Implementing data ingestion pipelines (Apache Kafka, Apache NiFi)

  • Introduction to distributed computing frameworks (Apache Hadoop, Apache Spark)
  • Batch processing vs. real-time processing
  • Implementing data processing workflows (Spark SQL, Spark Streaming)

  • Introduction to workflow management systems (Apache Airflow, Apache Oozie)
  • Designing and orchestrating data pipelines
  • Monitoring and managing data pipelines

  • Introduction to data warehousing concepts
  • Designing data warehouse architectures (star schema, snowflake schema)
  • Implementing data lakes (Apache Hadoop HDFS, Amazon S3)

  • Importance of data quality and data governance
  • Implementing data quality checks and validation
  • Ensuring data security and compliance

  • Designing scalable data infrastructure
  • Performance optimization techniques
  • Handling data growth and scalability challenges

  • Building and deploying a data pipeline project
  • Project planning, implementation, and deployment
  • Presentation of project findings and insights