Apache Spark and Scala Certification Training

Learning Objectives: Learn the basics of Scala that are required for programming Spark applications. You will also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.

Topics:
• What is Scala?
  
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• Why Scala for Spark?
• Scala in other Frameworks
• Introduction to Scala REPL
• Basic Scala Operations
• Variable Types in Scala
• Control Structures in Scala
  
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• Foreach loop, Functions and Procedures
• Collections in Scala- Array
• ArrayBuffer, Map, Tuples, Lists, and more

Hands-on:
• Scala REPL Detailed Demo

Learning Objectives: In this module, you will learn about object-oriented programming and functional programming techniques in Scala.

Topics:
• Functional Programming
• Higher Order Functions
• Anonymous Functions
• Class in Scala
  
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• Getters and Setters
• Custom Getters and Setters
• Properties with only Getters
• Auxiliary Constructor and Primary Constructor
• Singletons
• Extending a Class
  
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• Overriding Methods
• Traits as Interfaces and Layered Traits

Hands-on:
• OOPs Concepts
• Functional Programming

Learning Objectives: Understand Apache Spark and learn how to develop Spark applications. At the end, you will learn how to perform data ingestion using Sqoop.

Topics:
• Spark’s Place in Hadoop Ecosystem
• Spark Components & its Architecture
  
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• Spark Deployment Modes
• Introduction to Spark Shell
• Writing your first Spark Job Using SBT
• Submitting Spark Job
• Spark Web UI
• Data Ingestion using Sqoop
  
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Hands-on:
• Building and Running Spark Application
• Spark Application Web UI
• Configuring Spark Properties
• Data ingestion using Sqoop

Learning Objectives: Get an insight of Spark – RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions, and Functions performed on RDD).

Topics:
• Challenges in Existing Computing Methods
• Probable Solution & How RDD Solves the Problem
• What is RDD, It’s Operations, Transformations & Actions
  
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• Data Loading and Saving Through RDDs
  
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• Key-Value Pair RDDs
• Other Pair RDDs, Two Pair RDDs
• RDD Lineage
• RDD Persistence
• WordCount Program Using RDD Concepts
• RDD Partitioning & How It Helps Achieve Parallelization
• Passing Functions to Spark

Hands-on:
• Loading data in RDDs
• Saving data through RDDs
• RDD Transformations
• RDD Actions and Functions
• RDD Partitions
• WordCount through RDDs

Learning Objectives: In this module, you will learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about Spark and Hive integration.

Topics:
• Need for Spark SQL
• What is Spark SQL?
  
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• Spark SQL Architecture
• SQL Context in Spark SQL
• User Defined Functions
• Data Frames & Datasets
  
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• Interoperating with RDDs
• JSON and Parquet File Formats
• Loading Data through Different Sources
• Spark – Hive Integration

Hands-on:
• Spark SQL – Creating Data Frames
• Loading and Transforming Data through Different Sources
• Stock Market Analysis
• Spark-Hive Integration

Learning Objectives: Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.

Topics:
• Why Machine Learning?
• What is Machine Learning?
  
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• Where Machine Learning is Used?
• Face Detection: USE CASE
• Different Types of Machine Learning Techniques
  
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• Introduction to MLlib
• Features of MLlib and MLlib Tools
• Various ML algorithms supported by MLlib

Learning Objectives: Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.

Topics:
• Supervised Learning – Linear Regression, Logistic Regression, Decision Tree, Random Forest
  
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• Unsupervised Learning – K-Means Clustering & How It Works with MLlib
  
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• Analysis on US Election Data using MLlib (K-Means)

Hands-on:
• Machine Learning MLlib
• K- Means Clustering
• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest

Learning Objectives: Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Cluster. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. In the end, learn how to ingest streaming data using flume.

Topics:
• Need for Kafka
• What is Kafka?
  
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• Core Concepts of Kafka
• Kafka Architecture
• Where is Kafka Used?
• Understanding the Components of Kafka Cluster
• Configuring Kafka Cluster
• Kafka Producer and Consumer Java API
• Need of Apache Flume
• What is Apache Flume?
  
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• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration
  
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• Integrating Apache Flume and Apache Kafka

Hands-on:
• Configuring Single Node Single Broker Cluster
• Configuring Single Node Multi Broker Cluster
• Producing and consuming messages
• Flume Commands
• Setting up Flume Agent
• Streaming Twitter Data into HDFS

Learning Objectives: Work on Spark streaming which is used to build scalable fault-tolerant streaming applications. Also, learn about DStreams and various Transformations performed on the streaming data. You will get to know about commonly used streaming operators such as Sliding Window Operators and Stateful Operators.

Topics:
• Drawbacks in Existing Computing Methods
• Why Streaming is Necessary?
• What is Spark Streaming?
  
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• Spark Streaming Features
• Spark Streaming Workflow
  
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• How Uber Uses Streaming Data
• Streaming Context & DStreams
• Transformations on DStreams
• Describe Windowed Operators and Why it is Useful
• Important Windowed Operators
• Slice, Window and ReduceByWindow Operators
• Stateful Operators

Learning Objectives: In this module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.

Topics:
• Apache Spark Streaming: Data Sources
• Streaming Data Source Overview
  
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• Apache Flume and Apache Kafka Data Sources
• Example: Using a Kafka Direct Data Source
• Perform Twitter Sentimental Analysis Using Spark Streaming
  
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Hands-on:
• Different Streaming Data Sources