Master Apache Spark using Spark SQL and PySpark 3

Build Data Engineering Pipelines with Spark & Python

Master Data Engineering with Apache Spark and Python

This comprehensive course equips you with the essential skills to build robust data engineering pipelines using Spark SQL and Spark Data Frame APIs with Python. Ideal for those looking to transition into a Data Engineer role, this course leverages the power of Apache Spark for scalable data processing.

What You'll Learn:

• Data Engineering Fundamentals: Understand the core concepts of data engineering, including batch pipelines, streaming pipelines, and the role of ETL development and data warehouse development.
• Apache Spark for Data Engineering: Learn how Apache Spark has become the leading technology for big data processing at scale.
• Python for Data Engineering: Gain proficiency in using Python with Spark to build efficient and scalable data pipelines.
• Hadoop Fundamentals: Master essential Hadoop commands (HDFS) for data ingestion and preparation.
  • Copying files to HDFS using put and copyFromLocal.
  • Verifying file copies and determining file sizes.
  • Understanding key HDFS concepts like block size and replication factor.
• Spark SQL: Leverage the power of Spark SQL for data manipulation and querying.
  • Basic Transformations: Learn fundamental SQL-style queries for data transformation.
  • Table Management: Master DDL and DML operations for managing Spark tables.
  • Spark SQL Functions: Utilize functions for manipulating strings, dates, and null values.
  • Windowing Functions: Apply windowing functions for advanced aggregations and ranking.
• Spark Data Frame APIs: Explore the alternative approach of using Spark Data Frame APIs for data engineering applications.
  • Data Projection & Selection: Select, rename, and drop columns from DataFrames.
  • Column Data Processing: Manipulate data within DataFrames (strings, dates, nulls).
  • Data Transformations: Perform filtering, aggregations, and sorting.
  • Data Joining: Join datasets using inner and outer joins.
  • Windowing Functions: Apply windowing functions for advanced analytics and ranking.
• Spark Metastore: Understand the role of the Spark Metastore in managing tables and integrating with Spark SQL and Data Frame APIs.
• Spark Application Development & Deployment: Learn the complete lifecycle of Spark application development.
  • Python Virtual Environments: Set up a proper development environment.
  • Project Setup: Create a structured project for Spark applications.
  • Application Deployment: Package and deploy Spark applications.
  • Application Monitoring: Understand the Spark application execution lifecycle.

Course Highlights:

• Hands-on Learning: Extensive practical exercises and tasks to reinforce your skills.
• Resource Materials: Access to reference material on GitHub for continued practice.
• Support: Dedicated support via Udemy Q&A and live sessions.
• Lab Access: Complementary lab access available (extendable with positive feedback).
• Suitable for: Individuals seeking to transition into Data Engineering, with a basic understanding of Python. (Consider our "Data Engineering Essentials" course for Python fundamentals).

Software Requirements:

• Docker
• Visual Studio Code
• (Udemy Business users: Check with your employer before downloading software)

Note: This course has been updated to reflect current best practices and covers industry-relevant topics beyond the scope of the previous certification exam.