Databricks Associate Developer: Apache Spark with Python

Databricks Associate Developer: Apache Spark with Python

Instructor: Packt - Course Instructors

What you'll learn

Create and manipulate SQL queries in Apache Spark
Build complex Spark functions using user-defined functions (UDFs)
Develop real-time applications using Spark Streaming

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

8 assignments

Taught in English

There are 8 modules in this course

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.

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.

What's included

2 videos1 reading1 assignment

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.

What's included

1 video7 readings1 assignment

In this section, we will learn Spark's architecture, execution hierarchy, and key operations for efficient big data processing.

What's included

1 video4 readings1 assignment

In this section, we explore PySpark DataFrame operations, focusing on data manipulation, viewing, and aggregation techniques for efficient big data processing.

What's included

1 video5 readings1 assignment

1 videoTotal 1 minute

Spark DataFrames and Their Operations - Overview Video1 minute

5 readingsTotal 150 minutes

Introduction30 minutes
Viewing Columns of Data30 minutes
Converting a PySpark DataFrame to a Pandas DataFrame30 minutes
Changing the Case of a Column30 minutes
Dropping Null Values from a DataFrame30 minutes

1 assignmentTotal 10 minutes

Spark DataFrame Fundamentals10 minutes

In this section, we explore advanced Spark operations, including groupBy, join optimizations, and AQE, to enhance performance and scalability in data processing workflows.

What's included

1 video10 readings1 assignment

1 videoTotal 1 minute

Advanced Operations and Optimizations in Spark - Overview Video1 minute

10 readingsTotal 240 minutes

Introduction20 minutes
Joining DataFrames in Spark30 minutes
Use case30 minutes
Reading and Writing Data30 minutes
Reading and Writing Delta Files20 minutes
Catalyst Optimizer20 minutes
Data-based Optimizations in Apache Spark20 minutes
Managing Data Spills in Apache Spark20 minutes
Shuffle Joins20 minutes
Optimizing Wide Transformations30 minutes

1 assignmentTotal 10 minutes

Mastering Spark Execution and Data Management10 minutes

In this section, we explore Spark SQL for structured data processing, covering query implementation, data analysis, and integration with external systems.

What's included

1 video4 readings1 assignment

In this section, we explore real-time data processing with Spark, focusing on Structured Streaming, streaming architectures, and joins for dynamic data handling.