Apache Spark and Scala Certification Training

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

Hands on / Demo

• Edit Script
• QlikView Operations: Concatenate, If statement
• Create variables, Inline load, Functions and Joins

Learning Objectives: 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
• Getters and Setters
• Custom Getters and Setters
• Properties with only Getters
• Auxiliary Constructor and Primary Constructor
• Singletons
• Extending a Class
• Overriding Methods
• Traits as Interfaces and Layered Traits

Learning Objectives: 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
• Spark Deployment Modes
• Introduction to Spark Shell
• Writing your first Spark Job Using SBT
• Submitting Spark Job
• Spark Web UI
• Data Ingestion using Sqoop

Hands on / Demo

• Preceding load, Resident load & Incremental Load
• Rotating, Pivot, Cross & Mapping Tables
• Security with Section Access

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
• Data Loading and Saving Through RDDs
• 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

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?
• Spark SQL Architecture
• SQL Context in Spark SQL
• User Defined Functions
• Data Frames & Datasets
• Interoperating with RDDs
• JSON and Parquet File Formats
• Loading Data through Different Sources
• 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?
• Where Machine Learning is Used?
• Face Detection: USE CASE
• Different Types of Machine Learning Techniques
• 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
• Unsupervised Learning - K-Means Clustering & How It Works with MLlib
• Analysis on US Election Data using MLlib (K-Means)

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?
• 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?
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration
• Integrating Apache Flume and Apache Kafka

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?
• Spark Streaming Features
• Spark Streaming Workflow
• 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
• Apache Flume and Apache Kafka Data Sources
• Example: Using a Kafka Direct Data Source
• Perform Twitter Sentimental Analysis Using Spark Streaming

Spark Certification Training is designed by industry experts to make you a Certified Spark Developer. The Spark Scala Course offers:

• Overview of Big Data & Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator)
• Comprehensive knowledge of various tools that fall in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming
• The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
• The power of handling real time data feeds through a publish-subscribe messaging system like Kafka
• The exposure to many real-life industry-based projects which will be executed using Brighter Connect’s CloudLab
• Projects which are diverse in nature covering banking, telecommunication, social media, and govenment domains
• Rigorous involvement of a SME throughout the Spark Training to learn industry standards and best practices

Spark is one of the most growing and widely used tool for Big Data & Analytics. It has been adopted by multiple companies falling into various domains around the globe and therefore, offers promising career opportunities. In order to take part in these kind of opportunities, you need a structured training that is aligned as per Cloudera Hadoop and Spark Developer Certification (CCA175) and current industry requirements and best practices.

Besides strong theoretical understanding, it is quite essential to have a strong hands-on experience. Hence, during the Brighter Connect’s Spark and Scala course, you will be working on various industry-based use-cases and projects incorporating big data and spark tools as a part of solution strategy.

Additionally, all your doubts will be addressed by the industry professional, currently working on real life big data and analytics projects.

The Brighter Connect’s Spark Training is designed to help you become a successful Spark developer. During this course, our expert instructors will train you to- Write Scala Programs to build Spark Application Master the concepts of HDFS Understand Hadoop 2.x Architecture Understand Spark and its Ecosystem Implement Spark operations on Spark Shell Implement Spark applications on YARN (Hadoop) Write Spark Applications using Spark RDD concepts Learn data ingestion using Sqoop Perform SQL queries using Spark SQL Implement various machine learning algorithms in Spark MLlib API and Clustering Explain Kafka and its components Understand Flume and its components Integrate Kafka with real time streaming systems like Flume Use Kafka to produce and consume messages Build Spark Streaming Application Process Multiple Batches in Spark Streaming Implement different streaming data sources

Market for Big Data Analytics is growing tremendously across the world and such strong growth pattern followed by market demand is a great opportunity for all IT Professionals. Here are a few Professional IT groups, who are continuously enjoying the benefits and perks of moving into Big Data domain.

• Developers and Architects
• BI /ETL/DW Professionals
• Senior IT Professionals
• Testing Professionals
• Mainframe Professionals
• Freshers
• Big Data Enthusiasts
• Software Architects, Engineers and Developers
• Data Scientists and Analytics Professionals

The stats provided below will provide you a glimpse of growing popularity and adoption rate of Big Data tools like Spark in the current as well as upcoming years:

• 56% of Enterprises Will Increase Their Investment in Big Data over the Next Three Years – Forbes
• McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
• Average Salary of Spark Developers is $113k
• According to a McKinsey report, US alone will deal with shortage of nearly 190,000 data scientists and 1.5 million data analysts and Big Data managers by 2018

• There are no such prerequisites for our Spark and Scala Certification Training. However, prior knowledge of Java Programming and SQL will be helpful but is not at all mandatory.