Best Hands-on Big Data Practices with PySpark & Spark Tuning

What you'll learn:

Tuning Apache Spark: Powerful Big Data Processing Recipes Course

• How to attain a solid foundation in the most powerful and versatile technologies involved in data streaming: Apache Spark and Apache Kafka
• Form a robust and clean architecture for a data streaming pipeline
• Ways to implement the correct tools to bring your data streaming architecture to life
• How to create robust processing pipelines by testing Apache Spark jobs
• Learn How to create highly concurrent Spark programs by leveraging immutability
• How to solve repeated problems by leveraging the GraphX API
• How to solve long-running computation problems by leveraging lazy evaluation in Spark
• Tips to avoid memory leaks by understanding the internal memory management of Apache Spark
• Troubleshoot real-time pipelines written in Spark Streaming

Requirements:

• To pick up this course, you don’t need to be an expert with Spark. Customers should be familiar with Java or Scala.

Description:

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.

Who this course is for:

• Beginner/Junior/Senior Data Developers who want to master Spark/PySpark and Spread the knowledge of Big Data Analytics
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Course Details:

• 13 hours on-demand video
• Assignments
• 3 articles
• 38 downloadable resources
• Access on mobile and TV
• Certificate of completion

Best Hands-on Big Data Practices with PySpark & Spark Tuning udemy free download

Semi-Structured (JSON), Structured and Unstructured Data Analysis with Spark and Python & Spark Performance Tuning