https://www.udemy.com/course/apache-spark-core-30-in-depth

In-Depth, Hands-On driven exposure to the features and concpets of Spark Core with tips on tuning its performance

What you'll learn: 

Apache spark in detail with focus on hands-on and the underlying concepts. Learners will get to know the ways to get the best performance from Spark in production and avoid the common pitfalls.

Requirements:

We'll be using Python API in Spark Programming. However, we'll explain all the programs in details but fundamental knowledge of Python will be beneficial.

Description:

Apache Spark has turned out to be the most sought-after skill for any big data engineer. An evolution of MapReduce programming paradigm, Spark provides unified data processing from writing SQL to performing graph processing to implementing Machine Learning algorithms. It effectively uses cluster nodes and better memory management to spread the load across cluster of nodes to get faster results. Apache Spark drives the mission of data-driven-decision-making in thousands of organizations.

In order to fairly appreciate the benefits of the libraries of Apache Spark, it is essential to know the foundations right. This course aims exactly at that part. It starts from the beginner level and gradually explains all the complex concepts in an easy to reflect manner. It gives a profound description of the features and working of the framework through 5 different use cases with detailed hands on implementations. In fact, some hands-on sessions and solutions to the use-cases are explained in a full classroom mode with videos extending over 40 mins. After taking this course, you will gain the expertise on Spark Core and usage of further libraries like Spark SQL, Structured Streaming, Spark ML and GraphX will be much easier to visualize, implement and optimize.Who this course is for:Data engineers and developers who wish to leverage the fast analytics using Apache Spark in production environments.

Who this course is for:

Data engineers and developers who wish to leverage the fast analytics using Apache Spark in production environments.