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Apache Spark Training

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Overview

The Apache Spark Training program focuses on mastering the Spark framework for large-scale data analytics. It offers a unified API for developers, data scientists, and analysts to perform real-time data streaming and machine learning tasks efficiently. The course covers Spark Core, Streaming, SQL, MLLib, and integrations with NoSQL and cloud services. Learners will gain hands-on experience in developing, deploying, and tuning Spark applications.

Who Can Attend

Course Content

Introduction to Apache Spark

  • Overview of Spark and its use cases
  • Spark vs Hadoop comparison
  • Batch and real-time analytics concepts
  • Architecture and ecosystem overview
  • Spark job deployment and cloud integration

Scala Programming for Spark

  • Getting started with Scala and REPL
  • Variables, data types, and simple functions
  • Pattern matching and type inference
  • Functional programming concepts
  • Collections, maps, and flatMap operations

Object-Oriented and Functional Concepts

  • Classes, objects, and inheritance
  • Traits and multiple inheritance
  • Regular expressions and file handling
  • Difference between OOP and Functional Programming
  • Working with lists and collections

Spark Core

  • Introduction to Spark Core components
  • RDD programming: transformations and actions
  • Creating Spark Context and Spark Shell
  • Broadcast variables and persistence
  • Running Spark in local and cluster modes

Cluster Management and Deployment

  • Setting up a multi-node Spark cluster
  • Cluster management and configuration
  • Submitting and monitoring Spark jobs
  • Debugging and tuning Spark applications
  • Developing Spark apps in Eclipse IDE

Cassandra and NoSQL Integration

  • Introduction to Cassandra architecture
  • Creating and managing databases and tables
  • Data modeling and CRUD operations
  • Integrating Spark with Cassandra
  • Running Spark-Cassandra connectors on AWS

Spark Streaming

  • Architecture and overview of Spark Streaming
  • Processing distributed log files in real time
  • Discretized streams (DStreams) and transformations
  • Integration with Flume, Kafka, and Cassandra
  • Monitoring streaming jobs

Spark SQL

  • Introduction to Spark SQL and SQL Context
  • Working with DataFrames and datasets
  • Importing and saving data (Text, JSON, Parquet)
  • Using Hive with Spark SQL
  • Defining user-defined functions (UDFs)

Spark MLLib

  • Introduction to machine learning concepts
  • Regression and classification algorithms
  • Decision trees, SVM, and Naive Bayes
  • Clustering using K-Means
  • Building end-to-end ML solutions with Spark

Cloud and Production Deployment

  • Setting up Spark on Amazon EC2
  • Building a 4-node multi-cluster environment
  • Deploying Spark with Mesos and YARN
  • Running Spark jobs in production
  • Monitoring and scaling Spark clusters