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Coursera

Databricks Associate Developer: Apache Spark with Python

Packt via Coursera

Overview

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This course equips you with essential skills for working with Apache Spark using Python, preparing you for Databricks' certification exam. Apache Spark is a powerful open-source engine for processing large-scale data, and mastering it is a key asset in the data engineering and big data domain. Throughout the course, learners will gain hands-on experience with Spark's core components, including data processing, streaming, and machine learning. Practical examples and exercises will build confidence and ensure you're ready for real-world challenges. What sets this course apart is its strong focus on practical skills and real-world applications of Apache Spark. You'll not only learn the theory but also apply your knowledge in hands-on projects that reinforce the concepts. This course is ideal for aspiring data engineers, analysts, or scientists who want to achieve Databricks certification. A solid understanding of Python is required, and familiarity with Pyspark is beneficial, but not mandatory.

Syllabus

  • Overview of the Certification Guide and Exam
    • In this section, we introduce the Spark certification exam structure, review question types, and outline a step-by-step preparation strategy to enhance exam readiness.
  • Understanding Apache Spark and Its Applications
    • In this section, we explore Apache Spark's architecture, components, and applications, focusing on its role in big data processing, machine learning, and real-time analytics.
  • Spark Architecture and Transformations
    • In this section, we will learn Spark's architecture, execution hierarchy, and key operations for efficient big data processing.
  • Spark DataFrames and Their Operations
    • In this section, we explore PySpark DataFrame operations, focusing on data manipulation, viewing, and aggregation techniques for efficient big data processing.
  • Advanced Operations and Optimizations in Spark
    • In this section, we explore advanced Spark operations, including groupBy, join optimizations, and AQE, to enhance performance and scalability in data processing workflows.
  • SQL Queries in Spark
    • In this section, we explore Spark SQL for structured data processing, covering query implementation, data analysis, and integration with external systems.
  • Structured Streaming in Spark
    • In this section, we explore real-time data processing with Spark, focusing on Structured Streaming, streaming architectures, and joins for dynamic data handling.
  • Machine Learning with Spark ML
    • In this section, we will learn Spark ML workflows, scalable data analysis, and model evaluation techniques for real-world applications.

Taught by

Packt - Course Instructors

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