Apache Spark Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

The Apache Spark - Essentials training course provides a solid technical introduction to the Spark architecture and how Spark works. You will learn the basic building blocks of Spark, including RDDs and the distributed compute engine, as well as higher-level constructs that provide a simpler and more capable interface, including Spark SQL and DataFrames. This course also covers more advanced capabilities such as the use of Spark Streaming to process streaming data, and provides an overview of Spark Graph Processing (GraphX and GraphFrames) and Spark Machine Learning (SparkML Pipelines). Finally, the class explores possible performance issues, troubleshooting, cluster deployment techniques, and strategies for optimization.

The Apache Spark - Advanced training course teaches the advanced Spark skills. You will discover how to integrate Spark with Cassandra, cluster data workflows, measure performance, and more.

By attending Apache Spark - Essentials workshop, delegates will learn to:

Understand the need for Spark in data processing
Understand the Spark architecture and how it distributes computations to cluster nodes
Be familiar with basic installation / setup / layout of Spark
Use the Spark for interactive and ad-hoc operations
Use Dataset/DataFrame/Spark SQL to efficiently process structured data
Understand basics of RDDs (Resilient Distributed Datasets), and data partitioning, pipelining, and computations
Understand Spark's data caching and its usage
Understand performance implications and optimizations when using Spark
Be familiar with Spark Graph Processing and SparkML machine learning

By attending Apache Spark - Advanced workshop, delegates will learn to:

Build on Spark fundamentals to gain a deeper understanding of Spark internals
Learn the operational tweaks to generate the maximum performance from Spark
Discover how to use GraphX and MLib for machine learning

For Apache Spark - Essentials

Knowledge of Python or Scala.

For Apache Spark - Advanced

Developers who have knowledge of Spark Programming.

Scala Ramp Up

Scala Introduction, Variables, Data Types, Control Flow
The Scala Interpreter
Collections and their Standard Methods (e.g. map())
Functions, Methods, Function Literals
Class, Object, Trait

Introduction to Spark

Overview, Motivations, Spark Systems
Spark Ecosystem
Spark vs. Hadoop
Typical Spark Deployment and Usage Environments

RDDs and Spark Architecture

RDD Concepts, Partitions, Lifecycle, Lazy Evaluation
Working with RDDs - Creating and Transforming (map, filter, etc.)
Caching - Concepts, Storage Type, Guidelines

DataSets/DataFrames and Spark SQL

Introduction and Usage
Creating and Using a DataSet
Working with JSON
Using the DataSet DSL
Using SQL with Spark
Data Formats
Optimizations: Catalyst and Tungsten
DataSets vs. DataFrames vs. RDDs

Creating Spark Applications

Overview, Basic Driver Code, SparkConf
Creating and Using a SparkContext/SparkSession
Building and Running Applications
Application Lifecycle
Cluster Managers
Logging and Debugging

Spark Streaming

Overview and Streaming Basics
Structured Streaming
DStreams (Discretized Steams),
Architecture, Stateless, Stateful, and Windowed Transformations
Spark Streaming API
Programming and Transformations

Performance Characteristics and Tuning

The Spark UI
Narrow vs. Wide Dependencies
Minimizing Data Processing and Shuffling
Caching - Concepts, Storage Type, Guidelines
Using Caching
Using Broadcast Variables and Accumulators

Spark GraphX Overview

Introduction
Constructing Simple Graphs
GraphX API
Shortest Path Example

MLLib Overview

Introduction
Feature Vectors
Clustering / Grouping, K-Means
Recommendations
Classifications

Spark integration with Cassandra (other compatible NoSQL implementations can be substituted if supported)

Advanced Spark SQL and Spark Streaming

Implementing Spark on DataStax and Hortonworks

Cluster resource requirements

Debugging/troubleshooting Spark apps

Developing data workflows

Performance metrics

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Apache Spark

COURSE AGENDA