Data Engineering

Data Engineering is a specialized discipline at the intersection of Computer Science and Data Science that focuses on designing, building, and maintaining the large-scale systems and infrastructure required for data collection, storage, and processing. Practitioners, known as data engineers, construct robust data pipelines, manage databases, and create data warehouses and data lakes to transform raw data into a clean, reliable, and accessible format. By providing this foundational architecture, data engineering enables data scientists and analysts to efficiently perform analyses and build machine learning models, thus serving as the critical backbone for all data-driven operations within an organization.

  1. Introduction to Data Engineering
    1. Defining Data Engineering
      1. Historical Context and Evolution
        1. Key Objectives and Outcomes
          1. Data Engineering in Modern Organizations
          2. The Role of a Data Engineer
            1. Core Job Functions
              1. Daily Responsibilities
                1. Collaboration with Data Scientists
                  1. Collaboration with Data Analysts
                    1. Collaboration with Software Engineers
                      1. Collaboration with Business Stakeholders
                        1. Industry Applications
                        2. Core Responsibilities and Skills
                          1. Data Pipeline Development
                            1. Data Modeling and Architecture
                              1. Data Integration and ETL
                                1. Performance Optimization
                                  1. System Monitoring and Maintenance
                                    1. Troubleshooting and Support
                                      1. Data Quality Assurance
                                      2. Data Engineering vs. Data Science vs. Data Analytics
                                        1. Distinct Roles and Responsibilities
                                          1. Required Technical Skills
                                            1. Required Business Skills
                                              1. Typical Workflows and Deliverables
                                                1. Career Progression Paths
                                                2. The Data Lifecycle
                                                  1. Data Generation and Collection
                                                    1. Data Ingestion
                                                      1. Data Storage
                                                        1. Data Processing and Transformation
                                                          1. Data Analysis and Consumption
                                                            1. Data Archiving and Deletion
                                                              1. Data Governance Throughout the Lifecycle