Apache Spark 3 for Data Engineering and Analytics with Python
Master Python and PySpark 3.0.1 for Data Engineering / Analytics (Databricks)
Updated on Sep, 2023
Language - English
Apache Spark 3 is an open-source distributed engine for querying and processing data. This course will provide you with a detailed understanding of PySpark and its stack. This course is carefully developed and designed to guide you through the process of data analytics using Python Spark. The author uses an interactive approach in explaining keys concepts of PySpark such as the Spark architecture, Spark execution, transformations and actions using the structured API, and much more. You will be able to leverage the power of Python, Java, and SQL and put it to use in the Spark ecosystem.
You will start by getting a firm understanding of the Apache Spark architecture and how to set up a Python environment for Spark. Followed by the techniques for collecting, cleaning, and visualizing data by creating dashboards in Databricks. You will learn how to use SQL to interact with DataFrames. The author provides an in-depth review of RDDs and contrasts them with DataFrames.
There are multiple problem challenges provided at intervals in the course so that you get a firm grasp of the concepts taught in the course.
The code bundle for this course is available here: https://github.com/PacktPublishing/Apache-Spark-3-for-Data-Engineering-and-Analytics-with-Python
This course is designed for Python developers who wish to learn how to use the language for data engineering and analytics with PySpark. Any aspiring data engineering and analytics professionals.
What will you learn in this course:
- Learn Spark architecture, transformations, and actions using the structured API.
- Learn to set up your own local Py-Spark environment.
- Learn to interpret DAG (Directed Acyclic Graph) for Spark execution.
- Learn to interpret the Spark web UI.
- Learn the RDD (Resilient Distributed Datasets) API.
- Learn to visualize (graphs and dashboards) data on Data bricks.
What are the prerequisites for this course?
- Data scientists/analysts who wish to learn an analytical processing strategy that can be deployed over a big data cluster. Data managers who want to gain a deeper understanding of managing data over a cluster.
Check out the detailed breakdown of what’s inside the course
Introduction to Spark and Installation
- Introduction 04:43 04:43
- The Spark Architecture 03:39 03:39
- The Spark Unified Stack 03:38 03:38
- Java Installation 06:29 06:29
- Hadoop Installation 05:26 05:26
- Python Installation 04:23 04:23
- PySpark Installation 07:56 07:56
- Install Microsoft Build Tools 02:35 02:35
- MacOS - Java Installation 03:45 03:45
- MacOS - Python Installation 04:17 04:17
- MacOS - PySpark Installation 07:16 07:16
- MacOS - Testing the Spark Installation 05:07 05:07
- Install Jupyter Notebooks 09:18 09:18
- The Spark Web UI 11:19 11:19
- Section Summary 02:23 02:23
Spark Execution Concepts
RDD Crash Course
Structured API - Spark DataFrame
Introduction to Spark SQL and Databricks
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