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comprehensive Data Engineering course.

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Objectives

This Data Engineering course aims to provide a comprehensive understanding of data engineering, covering data pipelines, ETL processes, data storage, and processing techniques.

Participants will learn data modeling, transformation, pipeline orchestration, integration, governance, and security. The course includes hands-on experience and a final project to apply learned skills in real-world scenarios.

Course Outcome

Ability to design, build, and maintain scalable data pipelines.
Proficiency in working with various data storage solutions and processing frameworks.
Competence in data modeling, transformation, and integration.
Understanding of data governance, security, and compliance.
Skill in visualizing and reporting insights from data.
Capability to work with real-world data sets and solve complex data engineering problems.
Readiness for roles such as Data Engineer, ETL Developer, Data Architect, etc.
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Course Preview

  • Overview of Data Engineering and its Role in Data Science
  • Data Pipelines, ETL (Extract, Transform, Load), Data Warehousing

  • Data Storage
  • Relational Databases (SQL) and Database Design
  • NoSQL Databases (MongoDB, Cassandra, etc.)
  • Data Lakes and Data Warehouses
  • Cloud Storage Solutions (Amazon S3, Google Cloud Storage)

  • Data Processing
  • Introduction to Big Data and Distributed Computing
  • Batch Processing with Hadoop MapReduce
  • Stream Processing with Apache Kafka and Apache Flink
  • Real-time Data Processing

  • Data Modeling Concepts (ER Diagrams, Schema Design)
  • Introduction to Data Transformation Tools (Apache Spark, Apache Beam)
  • Data Quality and Data Cleaning Techniques
  • Introduction to Workflow Management (Apache Airflow)

  • Workflow Orchestration and Dependency Management
  • Monitoring and Error Handling in Data Pipelines
  • Managing and Scaling Data Infrastructure

  • Integrating Data from Multiple Sources
  • Data Extraction Techniques (Web Scraping, API Integration)
  • Data Integration Platforms (Talend, Informatica)

  • Data Governance Frameworks and Best Practices
  • Data Security and Compliance Regulations (GDPR, HIPAA, etc.)
  • Data Privacy and Anonymization Techniques

  • Introduction to Data Visualization Tools (Tableau, Power BI)
  • Building Dashboards and Reports
  • Data Exploration and Insights Discovery

  • Machine Learning Engineering for Data Pipelines
  • Data Engineering for IoT and Sensor Data
  • Building Scalable and Fault-Tolerant Systems

  • Planning and Implementing a Data Engineering Project
  • Working with Real-world Data Sets
  • Presenting Insights and Findings