* What is Spark RDDs
* How RDDs make Spark a feature rich framework
* Transformations, action and persistence
* Lazy operations and fault tolerance
* Load data and create RDD
* Persist RDD in memory or disk
* Pair operations and key-value
* Spark Hadoop Integration
* Hands on and core concepts of map() transformation.
* Hands on and core concepts of filter() transformation.
* Hands on and core concepts of flatMap() transformation.
* Compare map and flatMap transformation.
* Understanding RDD
* Loading data into RDD
* Scala RDD, Paired RDD, Double RDD & General RDD Functions
* Implementing HadoopRDD, Filtered RDD, Joined RDD
* Transformations, Actions and Shared Variables
* Spark Operations on YARN Sequence File Processing
* Partitioner and its role in Performance improvement
* Difference between Map Reduce Key-Value pair and RDD Key-Value pair
* RDD Lineage
* Garbage Collector and Memory Management
* Working with Key-Value Paired RDD RDD Partitions
* Partitioning of File-based RDDs
* HDFS and Data Locality
* All Methods if Transformations and Actions (Every RDD Method will get covered)