Introduction to Databricks Certified Associate Developer Course for Apache Spark

Introduction

• Welcome to the introductory video for the Databricks Certified Associate Developer course.
• Focuses on Spark development for the Spark Developer Associate Certificate.
• Follow the course in sequence for a detailed understanding of Spark concepts.
• Course available on YouTube channel for free.

Why Take This Course?

• Spark is widely used in big data, data processing, data engineering, data science, and machine learning.
• Hadoop clusters are built on concepts of MapReduce and Spark.
• Important to understand Spark for roles in data spaces.
• Spark is a standard framework for big data cluster processing.
• Databricks uses Spark for its processing engine.

What You Will Learn

• Detailed understanding of the Spark architecture and core APIs.
• Topics will cover various perspectives of Apache Spark.

Course Recommendations

• Use headphones for best experience.
• Adjust volume according to your device.
• Pause and take notes as needed.
• Follow the sequence of the course as designed in the playlist.
• Practice along with the course by running commands and notebooks in Databricks.
• Comment and reach out for practice notebooks or queries.

Target Audience

• Data Engineers and Developers (amateurs to professionals).
• Those preparing for the Databricks Certified Associate Developer for Apache Spark certification.
• Data enthusiasts looking to learn more about data technologies.

Prerequisites

• Basic understanding of SQL.
• Basic knowledge of Scala or any programming language (helpful but not mandatory).
• Basic knowledge about data and database concepts.
• No prior Spark knowledge required.

Course Content Overview

• Apache Spark Architecture: Distributed processing and execution, distributed data storage.
• Data Transformations: Techniques and practical executions of various data transformations in Spark.
• Certification Exam Details and Tips: Exam details, types of questions, duration, etc.

Detailed Topics Covered

• Spark architecture theory and practical aspects.
• Spark execution and cluster nodes.
• Hierarchy of Spark execution.
• DataFrame operations.
  • Schema and data types.
  • DataFrame API and SQL functions.
  • Rules to filter data.
  • Sorting data.
  • Handling nulls.
  • DataFrame creation from files.
  • Selecting and manipulating DataFrame columns.
  • Saving results to external sources (e.g., Amazon S3, Azure Data Lake).
  • User-defined functions.
  • Spark SQL functions and DataFrames.
  • Grouping data in DataFrames.
  • Using Databricks Community Edition.

Closing

• Full set of detailed videos will be available in the playlist.
• Contact through email for questions or notes.
• Social media links available for connection.