UDEMY.Data.Engineering.with.Python.SQL.Spark.Airflow.2026.BOOKWARE-MiMiR

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eBooks/Audiobooks

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MiMiR

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25,34 GB

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71

Date

2026-10-10

NFO

Mimir, Keeper of the Well of Wisdom
  Data Engineering with Python, SQL, Spark & Airflow
  https://www.udemy.com/course/data-engineering-with-python-sql-
  spark-airflow/
  Year         : 2026
  Language     : English
  Level        : All Levels
  Category     : IT & Software
  Subcategory  : Other IT & Software
  Duration     : 27h 45m
  Lectures     : 89
  Rating       : 4.7/5 (14 reviews)
  Students     : 196
  INSTRUCTOR(S)
  HEADLINE
    Build ETL pipelines with Python, SQL, PySpark, Airflow, Docker
    & Cloud through hands-on projects
  WHAT YOU'LL LEARN
  * Build end-to-end data pipelines using Python, SQL,
    PostgreSQL,
    ETL/ELT workflows, and practical data engineering
      techniques.
  * Process and transform real-world datasets with Python,
    Pandas,
    NumPy, and PySpark, including large datasets with 1M+
      records.
  * Design databases and data models, write advanced SQL
    queries,
    optimize performance, and create analytics-ready datasets.
  * Automate data pipelines using Docker and Airflow, work with
    cloud storage, and build a complete end-to-end data
    engineering project.
  REQUIREMENTS
  * No prior Data Engineering experience is required. Basic
    computer skills are sufficient. Basic programming or SQL
    knowledge is helpful but not required.
  * A computer capable of running Python, PostgreSQL, Docker,
    Airflow, and Apache Spark is recommended for completing the
    hands-on projects.
  WHO IS THIS COURSE FOR
  * Beginners aspiring to become Data Engineers.
  * Students learning Data Engineering from scratch.
  * Data Analysts transitioning into Data Engineering.
  * Software developers expanding into data engineering.
  * Professionals seeking practical skills in Python, SQL,
    Spark,
    Airflow, and ETL.
  DESCRIPTION
  Learn Data Engineering from the ground up by building
    practical
  data pipelines with Python, SQL, PySpark, Airflow, Docker,
  databases, and cloud storage. This hands-on Data Engineering
  course is designed for beginners who want to understand how
  modern data pipelines are designed, built, automated, and used
  in real-world environments. You will start with the
    fundamentals
  of Data Engineering, including the data lifecycle, structured
  and unstructured data, batch versus streaming processing, and
  the modern data stack. From there, you'll build a strong
  foundation in relational databases, data modeling, SQL, and
  Python for data processing. You will work with PostgreSQL,
  Python, Pandas, NumPy, SQL, Apache Spark, PySpark, Docker, and
  Apache Airflow, while learning how these technologies fit
  together in practical data engineering workflows. The course
  goes beyond theory. You will perform hands-on exercises
  involving database creation, schema design, SQL analytics,
    data
  cleaning, ETL pipelines, CSV-to-database loading, data quality
  validation, pipeline automation, Spark-based processing, cloud
  storage, and data warehousing concepts. You will also learn
    how
  to use Docker and Airflow to automate and orchestrate data
  workflows, and how to process large datasets using PySpark.
    The
  course culminates in an end-to-end Data Engineering capstone
  project, where you will ingest raw data, transform it with
  Python, load it into a database, perform SQL analytics,
    process
  data with Spark, generate reporting datasets, and automate the
  complete workflow. By completing the practical projects and
  exercises, you'll develop a portfolio-oriented understanding
    of
  how different Data Engineering tools work together to create
  reliable and repeatable data pipelines. What you'll work with:
  Python and Pandas SQL and PostgreSQL ETL and ELT pipelines
  Apache Spark and PySpark Docker Apache Airflow Cloud storage
  concepts Data warehouses and dimensional modeling Real-world
  datasets and practical projects Whether you're a student,
  aspiring Data Engineer, Data Analyst, software developer, or
  professional transitioning into Data Engineering, this course
  provides a structured path from fundamentals to building
  complete data pipelines.
  COURSE CONTENT
  Chapter 1: Introduction to Data Engineering
    1. 1.1 What Is Data Engineering?
    2. 1.2 Data Engineer vs Data Analyst vs Data Scientist
    3. 1.3 Understanding the Data Lifecycle
    4. 1.4 Understanding Structured, Semi-Structured &
       Unstructured Data
    5. 1.5 Batch vs Streaming Data Processing
    6. 1.6 Overview of the Modern Data Engineering Stack
    7. 1.7 Setting Up Python, Jupyter & VS Code
    8. 1.8 Setting Up a Git Repository for Data Projects
  Chapter 2: Database Fundamentals & Data Modeling
    9. 2.1 What Is a Database?
    10. 2.2 Relational Databases, Tables & Relationships
    11. 2.3 Understanding Primary & Foreign Keys
    12. 2.4 Database Normalization: 1NF, 2NF & 3NF
    13. 2.5 Database Indexing Fundamentals
    14. 2.6 OLTP vs OLAP: Understanding the Difference
    15. 2.7 Installing & Configuring PostgreSQL
    16. 2.8 Creating Databases & Tables in PostgreSQL
    17. 2.9 Inserting & Managing Data with SQL
    18. 2.10 Creating an Entity Relationship Diagram
    19. 2.11 Mini Project ? Student Management Database
  Chapter 3: SQL for Data Engineering
    20. 3.1 Introduction to SQL for Data Engineering
    21. 3.2 SELECT, WHERE & ORDER BY Clauses
    22. 3.3 SQL Aggregation Functions: COUNT, SUM & AVG
    23. 3.4 SQL Joins: INNER, LEFT & RIGHT JOIN
    24. 3.5 Working with SQL Subqueries
    25. 3.6 Introduction to SQL Window Functions
    26. 3.7 Understanding & Using SQL Indexes
    27. 3.8 SQL Query Optimization Fundamentals
    28. 3.9 Common Table Expressions (CTEs)
    29. 3.10 Creating & Using SQL Views
    30. 3.11 Stored Procedures & Functions in SQL
    31. 3.12 Data Cleaning Using SQL
    32. 3.13 Practical Lab ? Sales Data Analysis with SQL
    33. 3.14 Practical Lab ? Customer Segmentation with SQL
    34. 3.15 Practical Lab ? SQL Performance Tuning
    35. 3.16 Mini Project ? Building a SQL Analytics Dataset
  Chapter 4: Python for Data Processing
    36. 4.1 Python for Data Engineering
    37. 4.2 Python Variables, Data Types & Operators
    38. 4.3 Python Loops, Conditionals & Functions
    39. 4.4 Python File Handling & Virtual Environments
    40. 4.5 Introduction to Pandas for Data Engineering
    41. 4.6 Data Manipulation with Pandas
    42. 4.7 NumPy & Data Visualization Fundamentals
    43. 4.8 Data Cleaning with Python
    44. 4.9 Data Transformation with Python
    45. 4.10 Data Aggregation & Analysis with Python
    46. 4.11 Practical Lab ? Building a Data Cleaning Pipeline
    47. 4.12 Practical Lab ? Building a CSV-to-Database Loader
    48. 4.13 Practical Lab ? Implementing Data Quality Checks
    49. 4.14 Mini Project ? Building a Python ETL Pipeline Part
        1
    50. 4.15 Mini Project ? Building a Python ETL Pipeline Part
        2
  Chapter 5: Big Data Processing with Apache Spark
    51. 5.1 ETL vs ELT: Understanding Data Pipeline Workflows
    52. 5.2 Data Pipeline Architecture & Design
    53. 5.3 Logging & Monitoring Data Pipelines
    54. 5.4 Error Handling & Retry Logic
    55. 5.5 Scheduling & Automating Data Pipelines
    56. 5.6 Building an Automated Data Pipeline with Python
    57. 5.7 Scheduling Data Pipelines with Windows Task
        Scheduler
    58. 5.8 Docker Fundamentals for Data Engineers
    59. 5.9 Dockerizing a Data Pipeline
    60. 5.10 Introduction to Apache Airflow
    61. 5.11 Running Apache Airflow with Docker
    62. 5.12 Mini Project ? Building an Airflow Pipeline Part 1
    63. 5.13 Mini Project ? Building an Airflow Pipeline Part 2
  Chapter 6: Data Pipelines & Automation
    64. 6.1 Why Apache Spark for Data Engineering?
    65. 6.2 Understanding Apache Spark Architecture
    66. 6.3 Setting Up PySpark & Loading Data
    67. 6.4 RDD vs DataFrame in Apache Spark
    68. 6.5 PySpark Transformations & Actions
    69. 6.6 Lazy Evaluation & Catalyst Optimization
    70. 6.7 PySpark Aggregations & Window Functions
    71. 6.8 Performing Joins with PySpark
    72. 6.9 Reading, Writing & Storing Data with PySpark
    73. 6.10 Mini Project ? Building a PySpark ETL Pipeline
    74. 6.11 Mini Project ? Large-Scale Data Analytics with
        PySpark
  Chapter 7: Cloud & Data Warehousing Basics
    75. 7.1 Introduction to Cloud Data Engineering
    76. 7.2 Cloud Storage: Amazon S3, GCS & Azure Blob
    77. 7.3 Practical Lab ? Loading E-Commerce Data to Cloud
        Storage
    78. 7.4 Data Lakes vs Data Warehouses
    79. 7.5 Data Warehouses & Columnar Storage
    80. 7.6 Practical Lab ? Querying Data in the Cloud
    81. 7.7 Star Schema & Dimensional Data Modeling
  Chapter 8: End-to-End Data Engineering Capstone
    82. 8.1 Capstone Project Overview & Architecture
    83. 8.2 Project Configuration & Environment Setup
    84. 8.3 Building the Data Extraction Layer
    85. 8.4 Building the Data Transformation Layer
    86. 8.5 Building the Data Loading Layer
    87. 8.6 Building the Pipeline Orchestrator
    88. 8.7 Building SQL Analytics & Reporting
    89. 8.8 Building Spark Analytics for the Capstone
  DATES
  Published    : 2026-08-16
  Last Updated : 2026-09-24
If you fear the truth, dont come to my well.

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