Apache Spark Online Training

 >>  Apache Spark Online Training

Apache Spark Online Training


 (4.9) | 750 Ratings


Introduction


Apache Spark Online Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 35 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Module 1: Introduction to Spark

Topics: Problems with Traditional Large-Scale Systems, Introducing Spark

Module 2: Spark Basics

Topics: What is Apache Spark? Using the Spark Shell, Resilient Distributed Datasets (RDDs), Functional Programming with Spark

Module 3: Working with RDDs

Topics: RDD Operations, Key-Value Pair RDDs, MapReduce and Pair RDD Operations

Module 4: The Hadoop Distributed File System

Topics: Why HDFS? HDFS Architecture, Using HDFS

Module 5: Running Spark on a Cluster

Topics: A Spark Standalone Cluster, Web UI for the Spark Standalone

Module 6: Parallel Programming with Spark

Topics: RDD Partitions and HDFS Data Locality, Executing Parallel Operations, Working with Partitions

Module 7: Caching and Persistence

Topics: Distributed Persistence, RDD Lineage, Caching Overview,

Module 8: Writing Spark Applications

Topics: Spark Applications vs. Spark Shell, Configuring Spark Properties, Building and Running a Spark Application, Logging

Module 9: Spark, Hadoop, and the Enterprise Data Center

Topics: Spark and the Hadoop Ecosystem, Spark and MapReduce

Module 10: Spark Streaming

Topics: Example: Streaming Word Count, Other Streaming Operations, Sliding Window Operations, building Spark Applications

Module 11: Common Spark Algorithms

Topics: Iterative Algorithms, Graph Analysis, Machine Learning

Module 12: Improving Spark Performance

Topics:  Shared Variables: Broadcast Variables, Shared Variables: Accumulators, Common Performance Issues

 

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Course Curriculum

Module 1: Introduction to Spark

Topics: Problems with Traditional Large-Scale Systems, Introducing Spark

Module 2: Spark Basics

Topics: What is Apache Spark? Using the Spark Shell, Resilient Distributed Datasets (RDDs), Functional Programming with Spark

Module 3: Working with RDDs

Topics: RDD Operations, Key-Value Pair RDDs, MapReduce and Pair RDD Operations

Module 4: The Hadoop Distributed File System

Topics: Why HDFS? HDFS Architecture, Using HDFS

Module 5: Running Spark on a Cluster

Topics: A Spark Standalone Cluster, Web UI for the Spark Standalone

Module 6: Parallel Programming with Spark

Topics: RDD Partitions and HDFS Data Locality, Executing Parallel Operations, Working with Partitions

Module 7: Caching and Persistence

Topics: Distributed Persistence, RDD Lineage, Caching Overview,

Module 8: Writing Spark Applications

Topics: Spark Applications vs. Spark Shell, Configuring Spark Properties, Building and Running a Spark Application, Logging

Module 9: Spark, Hadoop, and the Enterprise Data Center

Topics: Spark and the Hadoop Ecosystem, Spark and MapReduce

Module 10: Spark Streaming

Topics: Example: Streaming Word Count, Other Streaming Operations, Sliding Window Operations, building Spark Applications

Module 11: Common Spark Algorithms

Topics: Iterative Algorithms, Graph Analysis, Machine Learning

Module 12: Improving Spark Performance

Topics:  Shared Variables: Broadcast Variables, Shared Variables: Accumulators, Common Performance Issues

 

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