What is this course about:
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In particularly, you will learn:
- An overview of the architecture of Apache Spark.
- Develop Apache Spark 2.0 applications using RDD transformations and actions and Spark SQL.
- Work with Apache Spark’s primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.
- Deep dive into advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.
- Scale up Spark applications on a Hadoop YARN cluster through Amazon’s Elastic MapReduce service.
- Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Spark SQL.
- Share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators.
- Best practices of working with Apache Spark in the field.
- Big data ecosystem overview.
Why choosing this course?
This course is very hands-on, Tao has put lots effort to provide you with not only the theory but also real-life examples of developing Spark applications that you can try out on your own laptop.
Tao has uploaded all the source code to Github and you will be able to follow along with either Windows, MAC OS or Linux.
In the end of this course, Tao is confident that you will gain in-depth knowledge about Spark and general big data analysis and data manipulation skills. You’ll be able to develop Spark application that analyzes Gigabytes scale of data both on your laptop, and in the cloud using Amazon’s Elastic MapReduce service!
Who is the target audience?
- Anyone who want to fully understand how Apache Spark technology works and learn how Apache Spark is being used in the field.
- Software engineers who want to develop Apache Spark 2.0 applications using Spark Core and Spark SQL.
- Data scientists or data engineers who want to advance their career by improving their big data processing skills.