Best Hands-on Big Data Practices with PySpark & Spark Tuning
Best Hands-on Big Data Practices with PySpark & Spark Tuning
Semi-Structured (JSON), Structured and Unstructured Data Analysis with Spark and Python & Spark Performance Tuning
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Created by Amin Karami | 13 hours on-demand video course
PySpark Course Overview
Best Hands-on Big Data Practices with PySpark & Spark Tuning
In this course, students will be provided with hands-on PySpark practices using real case studies from academia and industry to be able to work interactively with massive data. In addition, students will consider distributed processing challenges, such as data skewness and spill within big data processing. We designed this course for anyone seeking to master Spark and PySpark and Spread the knowledge of Big Data Analytics using real and challenging use cases.
We will work with Spark RDD, DF, and SQL to process huge sized of data in the format of semi-structured, structured, and unstructured data. The learning outcomes and the teaching approach in this course will accelerate the learning by Identifying the most critical required skills in the industry and understanding the demands of Big Data analytics content.
We will not only cover the details of the Spark engine for large-scale data processing, but also we will drill down big data problems that allow users to instantly shift from an overview of large-scale data to a more detailed and granular view using RDD, DF and SQL in real-life examples. We will walk through the Big Data case studies step by step to achieve the aim of this course.
By the end of the course, you will be able to build Big Data applications for different types of data (volume, variety, veracity) and you will get acquainted with best-in-class examples of Big Data problems using PySpark.
What you’ll learn
- Understand Apache Spark’s framework, execution and programming model for the development of Big Data Systems
- Learn step-by-step hands-on PySpark practices on structured, unstructured and semi-structured data using RDD, DataFrame and SQL
- Learn how to work with a free Cloud-based and a Desktop computer for Spark setup and configuration
- Build simple to advanced Big Data applications for different types of data (volume, variety, veracity) through real case studies
- Investigate and apply optimization and performance tuning methods to manage data Skewness and prevent Spill
- Investigate and apply Adaptive Query Execution (AQE) to optimize Spark SQL query execution at runtime
- Investigate and be able to explain the lazy evaluations (Narrow vs Wide transformation) and internal working of Spark
- Build and learn Spark SQL applications using JDBC (Java Database Connectivity)
Recommended PySpark Course
Azure Databricks & Spark For Data Engineers (PySpark / SQL)
Azure Databricks & Spark For Data Engineers (PySpark / SQL)
The Azure Databricks & Spark For Data Engineers (PySpark / SQL) course follows a logical progression of a real world project implementation with technical concepts being explained and the Databricks notebooks being built at the same time. Even though this course is not specifically designed to teach you the skills required for passing the Azure Data Engineer Associate Certification Exam DP203, it can greatly help you get most of the necessary skills required for the exam.
PYSPARK End to End Developer Course (Spark with Python)
PYSPARK End to End Developer Course (Spark with Python)
Welcome to PYSPARK End to End Developer Course (Spark with Python) Course. Learn PySpark end to end features and functionalities. Course also includes a Python course and HDFS Commands Course.
Who this course is for
- Beginner/Junior/Senior Data Developers who want to master Spark/PySpark and Spread the knowledge of Big Data Analytics
- If you are new to Python programming, Don’t worry at all, you can learn it freely through my YouTube channel. Subscribe to my YouTube channel and keep learning without any hassle
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Taught by Amin Karami
