Course Overview:
Hadoop Fundamentals is a one-stop course that introduces you to the domain of spark development as well as gives you technical knowhow of the same. At the end of this course you will be able to earn a credential of Spark professional and you will be capable of dealing with Terabyte scale of data and analyze it successfully using spark and its ecosystem. Scala is a condensed version of Java for large scale functional and object-oriented programming. Apache Spark Streaming is an extended component of the Spark API for processing big data sets as real-time streams. Together, Spark Streaming and Scala enable the streaming of big data.
Course Objectives:
- Create Spark applications with the Scala programming language.
- Use Spark Streaming to process continuous streams of data.
- Process streams of real-time data with Spark Streaming.
Pre-requisites:
- Programming and scripting experience
Target Audience:
- Software Engineers
Course Duration:
- 21 hours – 3 days
Course Content:
Introduction
Scala Programming in Depth Review
- Syntax and structure
- Flow control and function
Spark Internals
- Resilient Distributed Datasets (RDD)
- Spark script to graph to cluster
Overview of Spark Streaming
- Streaming architecture
- Intervals in streaming
- Fault tolerance
Preparing the Development Environment
- Installing and configuring Apache Spark
- Installing and configuring the Scala IDE
- Installing and configuring JDK
Spark Streaming Beginner to Advanced
- Working with key/value RDD’s
- Filtering RDD’s
- Improving Spark scripts with regular expressions
- Sharing data on a cluster
- Working with network data sets
- Implementing BFS algorithms
- Creating Spark driver scripts
- Tracking in real time with scripts
- Writing continuous applications
- Streaming linear regression
- Using Spark Machine Learning Library
Spark and Clusters
- Bundling dependencies and Spark scripts using the SBT tool
- Using EMR for illustrating clusters
- Optimizing by partitioning RDD’s
- Using Spark logs
Integration in Spark Streaming
- Integrating Apache Kafka and working with Kafka topics
- Integrating Apache Fume and working with pull-based/push-based Flume configurations
- Writing a custom receiver class
- Integrating Cassandra and exposing data as real-time services
In Production
- Packaging an application and running it with Spark-Submit
- Troubleshooting, tuning, and debugging Spark Jobs and clusters
