Big Data Hadoop & Spark Developer

Spark Core provides basic I/O functionalities, distributed task dispatching, and scheduling. Resilient Distributed Datasets (RDDs) are logical collections of data partitioned across machines. RDDs can be created by referencing datasets in external storage systems, or by applying transformations on existing RDDs. In this course, you will learn how to improve Spark's performance and work with Data Frames and Spark SQL. Spark Streaming leverages Spark's language-integrated API to perform streaming analytics. This design enables the same set of application code written for batch processing to join streams against historical data, or run ad-hoc queries on stream state. In this course, you will learn how to work with different input streams, perform transformations on streams, and tune up performance. MLlib is Spark's machine learning library. GraphX is Spark's API for graphs and graph-parallel computation. SparkR exposes the API and allows users to run jobs from the R shell on a cluster. In this course, you will learn how to work with each of these libraries.