Big Data Hadoop, Spark, Storm, Scala Training

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Online Classroom training will be provided for Big Data Hadoop, Apache Spark and Scala. For Apache Storm Self paced videos will be provided for self study.

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About Course

This is a Combo Course that is created to give you an edge in the Big Data Hadoop milieu. You will be trained in the Hadoop Architecture, the constituent components MapReduce, HDFS, HBase and others. Gain proficiency in Apache Storm, Apache Spark, and Scala programming language. It is an all-in-one course designed to give a 360-degree overview of Hadoop Architecture using the real-time projects along with the real-time processing of unbound data streams using Apache Storm and creating applications in Spark with Scala programming. The major topics include Hadoop and its Ecosystem, core concepts of MapReduce and HDFS, Introduction to HBase Architecture, Hadoop Cluster Setup, Hadoop Administration and Maintenance. The course further trains you on the concepts of Big Data world, Batch Analysis, Types of Analytics and usage of Apache Storm for real-time Big Data Analytics, Comparison between Spark and Hadoop and Techniques to increase your application performance, enabling high-speed processing.

What you will learn in this Training Course?

Learn about Hadoop Architecture
Hadoop cluster setup and maintenance
Get to know Data Science Project Life Cycle
Write MapReduce programs
Learn YARN, Flume, Oozie, Impala, Zookeeper
Get to know Apache Storm Architecture
Study Storm Topology, Components and Logic Dynamics
Deploy Apache Spark on Hadoop cluster
Write Spark applications in Python, Java and Scala
In-depth Scala programming and implementation
Understand Trident Spouts and Filter in Storm
Work on real-time Hadoop project

Who should take this Training Course?

Software Developers , System Administrators and ETL Developers
Project Managers
Information Architects
Data Scientists

What are the prerequisites for taking this Training Course?

Anybody can take this Training Course.

Why should you take this Training Course?

This is a comprehensive course to help you make the big leap into the Big Data Hadoop ecosystem. This Training will provide you enough proficiency to work on real world projects on Big Data, build resilient Hadoop clusters, perform high-speed data processing using Apache Spark, write versatile application using Scala programming and so on. All in all this is a great combo course to help you land the best jobs in the Big Data domain.

Big Data Hadoop Course Content

Hadoop Installation & setup

The architecture of Hadoop 2.0 cluster, what is High Availability and Federation, how to setup a production cluster, the various shell commands in Hadoop, understanding configuration files in Hadoop 2.0, installing single node cluster with Cloudera Manager, understanding Spark, Scala, Sqoop, Pig and Flume.

Introduction to Big Data Hadoop. Understanding HDFS & Mapreduce

Introducing Big Data & Hadoop, what is Big Data and where does Hadoop fits in, two important Hadoop ecosystem componentsnamely Map Reduce and HDFS, in-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability, in-depth YARN – Resource Manager, Node Manager.

Hands-on Exercise – HDFS working mechanism, data replication process, how to determine the size of the block, understanding a DataNode and NameNode.

Deep Dive in Mapreduce

Learning the working mechanism of MapReduce, understanding the mapping and reducing stages in MR, the various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle and Sort

Hands-on Exercise – How to write a Word Count program in MapReduce, how to write a custom Partitioner, what is a MapReduce Combiner, how to run a job in a local job runner, deploying unit test, what is a map side join and reduce side join, what is a tool runner, how to use counters, dataset joining with map side and reduce side joins.

Introduction to Hive

Introducing Hadoop Hive, detailed architecture of Hive, comparing Hive with Pig and RDBMS, working with Hive Query Language, creation of database, table, Group by and other clauses, the various types of Hive tables, Hcatalog, storing the Hive Results, Hive partitioning and Buckets.

Hands-on Exercise – Database creation in Hive, dropping a database, Hive table creation, how to change the database, data loading, Hive table creation, dropping and altering table, pulling data by writing Hive queries with filter conditions, table partitioning in Hive, what is a group by clause

Advance Hive & Impala

The indexing in Hive, the Map side Join in Hive, working with complex data types, the Hive User-defined Functions, Introduction to Impala, comparing Hive with Impala, the detailed architecture of Impala

Hands-on Exercise – How to work with Hive queries, the process of joining table and writing indexes, external table and sequence table deployment, data storage in a different table.

Introduction to Pig

Apache Pig introduction, its various features, the various data types and schema in Hive, the available functions in Pig, Hive Bags, Tuples and Fields.

Hands-on Exercise – Working with Pig in MapReduce and local mode, loading of data, limiting data to 4 rows, storing the data into file, working with Group By,Filter By,Distinct,Cross,Split in Hive.

Flume, Sqoop & HBase

Apache Sqoop introduction, overview, importing and exporting data, performance improvement with Sqoop, Sqoop limitations, introduction to Flume and understanding the architecture of Flume, what is HBase and the CAP theorem.

Hands-on Exercise – Working with Flume to generating of Sequence Number and consuming it, using the Flume Agent to consume the Twitter data, using AVRO to create Hive Table, AVRO with Pig, creating Table in HBase, deploying Disable, Scan and Enable Table.

Writing Spark Applications using Scala

Using Scala for writing Apache Spark applications, detailed study of Scala, the need for Scala, the concept of object oriented programing, executing the Scala code, the various classes in Scala like Getters,Setters, Constructors, Abstract ,Extending Objects, Overriding Methods, the Java and Scala interoperability, the concept of functional programming and anonymous functions, Bobsrockets package, comparing the mutable and immutable collections.

Hands-on Exercise – Writing Spark application using Scala, understanding the robustness of Scala for Spark real-time analytics operation.

Spark framework

Detailed Apache Spark, its various features, comparing with Hadoop, the various Spark components, combining HDFS with Spark, Scalding, introduction to Scala, importance of Scala and RDD.

Hands-on Exercise – The Resilient Distributed Dataset in Spark and how it helps to speed up big data processing.

RDD in Spark

Understanding the Spark RDD operations, comparison of Spark with MapReduce, what is a Spark transformation, loading data in Spark, types of RDD operations viz. transformation and action, what is Key Value pair.

Hands-on Exercise – How to deploy RDD with HDFS, using the in-memory dataset, using file for RDD, how to define the base RDD from external file, deploying RDD via transformation, using the Map and Reduce functions, working on word count and count log severity.

Data Frames and Spark SQL

The detailed Spark SQL, the significance of SQL in Spark for working with structured data processing, Spark SQL JSON support, working with XML data, and parquet files, creating HiveContext, writing Data Frame to Hive, How to read a JDBC file, significance of a Spark Data Frame, how to create a Data Frame, what is schema manual inferring, how to work with CSV files, JDBC table reading, data conversion from Data Frame to JDBC, Spark SQL user-defined functions. shared variable and accumulators, how to query and transform data in Data Frames, how Data Frame provides the benefits of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.

Hands-on Exercise – Data querying and transformation using Data Frames, finding out the benefits of Data Frames over Spark SQL and Spark RDD.

Machine Learning using Spark (Mlib)

Introduction to Spark MLlib, understanding the various algorithms, what is Spark iterative algorithm, Spark graph processing analysis, introducing machine learning, K-Means clustering, Spark variables like shared and broadcast variables, what are accumulators.

Hands-on Exercise – Writing spark code using Mlib.

Spark Streaming

Introduction to Spark streaming, the architecture of Spark Streaming, working with the Spark streaming program, processing data using Spark streaming, requesting count and Dstream, multi-batch and sliding window operations and working with advanced data sources.

Hands-on Exercise – Deploying Spark streaming for data in motion and checking the output is as per the requirement.

Hadoop Administration – Multi Node Cluster Setup using Amazon EC2

Create a four node Hadoop cluster setup, running the MapReduce Jobs on the Hadoop cluster, successfully running the MapReduce code, working with the Cloudera Manager setup.

Hands-on Exercise – The method to build a multi-node Hadoop cluster using an Amazon EC2 instance, working with the Cloudera Manager.

Hadoop Administration – Cluster Configuration

The overview of Hadoop configuration, the importance of Hadoop configuration file, the various parameters and values of configuration, the HDFS parameters and MapReduce parameters, setting up the Hadoop environment, the Include’ and Exclude configuration files, the administration and maintenance of Name node, Data node directory structures and files, What is a File system image, understanding Edit log.

Hands-on Exercise – The process of performance tuning in MapReduce.

Hadoop Administration – Maintenance, Monitoring and Troubleshooting

Introduction to the Checkpoint Procedure, Name node failure and how to ensure the recovery procedure, Safe Mode, Metadata and Data backup, the various potential problems and solutions, what to look for, how to add and remove nodes.

Hands-on Exercise – How to go about ensuring the MapReduce File system Recovery for various different scenarios, JMX monitoring of the Hadoop cluster, how to use the logs and stack traces for monitoring and troubleshooting, using the Job Scheduler for scheduling jobs in the same cluster, getting the MapReduce job submission flow, FIFO schedule, getting to know the Fair Scheduler and its configuration.

ETL Connectivity with Hadoop Ecosystem

How ETL tools work in Big data Industry, Introduction to ETL and Data warehousing. Working with prominent use cases of Big data in ETL industry, End to End ETL PoC showing big data integration with ETL tool.

Hands-on Exercise – Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool

Project Solution Discussion and Cloudera Certification Tips & Tricks

Working towards the solution of the Hadoop project solution, its problem statements and the possible solution outcomes, preparing for the Cloudera Certifications, points to focus for scoring the highest marks, tips for cracking Hadoop interview questions.

Hands-on Exercise – The project of a real-world high value Big Data Hadoop application and getting the right solution based on the criteria set by the Intellipaat team.

Following topics will be available only in self-paced Mode.

Hadoop Application Testing

Why testing is important, Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end to end tests, Functional testing, Release certification testing, Security testing, Scalability Testing, Commissioning and Decommissioning of Data Nodes Testing, Reliability testing, Release testing

Roles and Responsibilities of Hadoop Testing Professional

Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges etc), Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Validating new feature and issues in Core Hadoop.

Framework called MR Unit for Testing of Map-Reduce Programs

Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Responsible for creating a testing Framework called MR Unit for testing of Map-Reduce programs.

Unit Testing

Automation testing using the OOZIE, Data validation using the query surge tool.

Test Execution

Test plan for HDFS upgrade, Test automation and result

Test Plan Strategy and writing Test Cases for testing Hadoop Application

How to test install and configure

Scala Course Content

Introduction of Scala

Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics.

Pattern Matching

The importance of Scala, the concept of REPL (Read Evaluate Print Loop), deep dive into Scala pattern matching, type interface, higher order function, currying, traits, application space and Scala for data analysis.

Executing the Scala code

Learning about the Scala Interpreter, static object timer in Scala, testing String equality in Scala, Implicit classes in Scala, the concept of currying in Scala, various classes in Scala.

Classes concept in Scala

Learning about the Classes concept, understanding the constructor overloading, the various abstract classes, the hierarchy types in Scala, the concept of object equality, the val and var methods in Scala.

Case classes and pattern matching

Understanding Sealed traits, wild, constructor, tuple, variable pattern, and constant pattern.

Concepts of traits with example

Understanding traits in Scala, the advantages of traits, linearization of traits, the Java equivalent and avoiding of boilerplate code.

Scala java Interoperability

Implementation of traits in Scala and Java, handling of multiple traits extending.

Scala collections

Introduction to Scala collections, classification of collections, the difference between Iterator, and Iterable in Scala, example of list sequence in Scala.

Mutable collections vs. Immutable collections

The two types of collections in Scala, Mutable and Immutable collections, understanding lists and arrays in Scala, the list buffer and array buffer, Queue in Scala, double-ended queue Deque, Stacks, Sets, Maps, Tuples in Scala.

Use Case bobsrockets package

Introduction to Scala packages and imports, the selective imports, the Scala test classes, introduction to JUnit test class, JUnit interface via JUnit 3 suite for Scala test, packaging of Scala applications in Directory Structure, example of Spark Split and Spark Scala.

Spark Course Content

Introduction to Spark

Introduction to Spark, how Spark overcomes the drawbacks of working MapReduce, understanding in-memory MapReduce,interactive operations on MapReduce, Spark stack, fine vs. coarse grained update, Spark stack,Spark Hadoop YARN, HDFS Revision, YARN Revision, the overview of Spark and how it is better Hadoop, deploying Spark without Hadoop,Spark history server, Cloudera distribution.

Spark Basics

Spark installation guide,Spark configuration, memory management, executor memory vs. driver memory, working with Spark Shell, the concept of Resilient Distributed Datasets (RDD), learning to do functional programming in Spark, the architecture of Spark.

Working with RDDs in Spark

Spark RDD, creating RDDs, RDD partitioning, operations & transformation in RDD,Deep dive into Spark RDDs, the RDD general operations, a read-only partitioned collection of records, using the concept of RDD for faster and efficient data processing,RDD action for Collect, Count, Collectsmap, Saveastextfiles, pair RDD functions.

Aggregating Data with Pair RDDs

Understanding the concept of Key-Value pair in RDDs, learning how Spark makes MapReduce operations faster, various operations of RDD,MapReduce interactive operations, fine & coarse grained update, Spark stack.

Writing and Deploying Spark Applications

Comparing the Spark applications with Spark Shell, creating a Spark application using Scala or Java, deploying a Spark application,Scala built application,creation of mutable list, set & set operations, list, tuple, concatenating list, creating application using SBT,deploying application using Maven,the web user interface of Spark application, a real world example of Spark and configuring of Spark.

Parallel Processing

Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions, file-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations,comparing repartition & coalesce, RDD actions.

Spark RDD Persistence

The execution flow in Spark, Understanding the RDD persistence overview,Spark execution flow & Spark terminology, distribution shared memory vs. RDD, RDD limitations, Spark shell arguments,distributed persistence, RDD lineage,Key/Value pair for sorting implicit conversion like CountByKey, ReduceByKey, SortByKey, AggregataeByKey

Spark Streaming & Mlib

Spark Streaming Architecture, Writing streaming programcoding, processing of spark stream,processing Spark Discretized Stream (DStream), the context of Spark Streaming, streaming transformation, Flume Spark streaming, request count and Dstream, multi batch operation, sliding window operations and advanced data sources. Different Algorithms, the concept of iterative algorithm in Spark, analyzing with Spark graph processing, introduction to K-Means and machine learning, various variables in Spark like shared variables, broadcast variables, learning about accumulators.

Improving Spark Performance

Introduction to various variables in Spark like shared variables, broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems.

Spark SQL and Data Frames

Learning about Spark SQL, the context of SQL in Spark for providing structured data processing, JSON support in Spark SQL, working with XML data, parquet files, creating HiveContext, writing Data Frame to Hive, reading JDBC files, understanding the Data Frames in Spark, creating Data Frames, manual inferring of schema, working with CSV files, reading JDBC tables, Data Frame to JDBC, user defined functions in Spark SQL, shared variable and accumulators, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.

Scheduling/ Partitioning

Learning about the scheduling and partitioning in Spark,hash partition, range partition, scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling,Map partition with index, the Zip, GroupByKey, Spark master high availability, standby Masters with Zookeeper, Single Node Recovery With Local File System, High Order Functions.

Apache Storm Course Content

Understanding Architecture of Storm

Big Data characteristics, understanding Hadoop distributed computing, the Bayesian Law, deploying Storm for real time analytics, the Apache Storm features, comparing Storm with Hadoop, Storm execution, learning about Tuple, Spout, Bolt.

Installation of Apache storm

Installing the Apache Storm, various types of run modes of Storm.

Introduction to Apache Storm

Understanding Apache Storm and the data model.

Apache Kafka Installation

Installation of Apache Kakfa and its configuration.

Apache Storm Advanced

Understanding of advanced Storm topics like Spouts, Bolts, Stream Groupings, Topology and its Life cycle, learning about Guaranteed Message Processing.

Storm Topology

Various Grouping types in Storm, reliable and unreliable messages, Bolt structure and life cycle, understanding Trident topology for failure handling, process, Call Log Analysis Topology for analyzing call logs for calls made from one number to another.

Overview of Trident

Understanding of Trident Spouts and its different types, the various Trident Spout interface and components, familiarizing with Trident Filter, Aggregator and Functions, a practical and hands-on use case on solving call log problem using Storm Trident.

Storm Components & classes

Various components, classes and interfaces in storm like – Base Rich Bolt Class, i RichBolt Interface, i RichSpout Interface, Base Rich Spout class and the various methodology of working with them.

Cassandra Introduction

Understanding Cassandra, its core concepts, its strengths and deployment.

Boot Stripping

Twitter Boot Stripping, detailed understanding of Boot Stripping, concepts of Storm, Storm Development Environment.

What Hadoop Projects You will be working on?

Project 1 : Working with MapReduce, Hive, Sqoop

Industry : General

Problem Statement : How to successfully import data using Sqoop into HDFS for data analysis.

Topics : As part of this project you will work on the various Hadoop components like MapReduce, Apache Hive and Apache Sqoop. Work with Sqoop to import data from relational database management system like MySQL data into HDFS. Deploy Hive for summarizing data, querying and analysis. Convert SQL queries using HiveQL for deploying MapReduce on the transferred data. You will gain considerable proficiency in Hive, and Sqoop after completion of this project.

Highlights :

Sqoop data transfer from RDBMS to Hadoop
Coding in Hive Query Language
Data querying and analysis.

Project 2 : Work on MovieLens data for finding top movies

Industry : Media and Entertainment

Problem Statement : How to create the top ten movies list using the MovieLens data.

Topics : In this project you will work exclusively on data collected through MovieLens available rating data sets. The project involves writing MapReduce program to analyze the MovieLens data and create list of top ten movies. You will also work with Apache Pig and Apache Hive for working with distributed datasets and analyzing it.

Highlights :

MapReduce program for working on the data file
Apache Pig for analyzing data
Apache Hive data warehousing and querying

Project 3 : Hadoop YARN Project – End to End PoC

Industry : Banking

Problem Statement : How to bring the daily data ( incremental data) into the Hadoop Distributed File System.

Topics : In this project we have transaction data which is daily recorded/store in the RDBMS. Now this data is transferred everyday into HDFS for further Big Data Analytics. You will work on live Hadoop YARN cluster. YARN is part of the Hadoop 2.0 ecosystem that lets Hadoop to decouple from MapReduce and deploy more competitive processing and wider array of applications. You will work on the YARN central Resource Manager.

Highlights :

Using Sqoop commands to bring the data into HDFS
End to End flow of transaction data
Working with the data from HDFS

Project 4 : Table Partitioning in Hive

Industry : Banking

Problem Statement : How to improve the query speed using Hive data partitioning.

Topics : This project involves working with Hive table data partitioning. Ensuring the right partitioning helps to read the data, deploy it on the HDFS, and run the MapReduce jobs at a much faster rate. Hive lets you partition data in multiple ways. This will give you hands-on experience in partitioning of Hive tables manually, deploying single SQL execution in dynamic partitioning, bucketing of data so as to break it into manageable chunks.

Highlights :

Manual Partitioning
Dynamic Partitioning
Bucketing

Project 5 : Connecting Pentaho with Hadoop Ecosystem

Industry : Social Network

Problem Statement : How to deploy ETL for data analysis activities.

Topics : This project lets you connect Pentaho with the Hadoop ecosystem. Pentaho works well with HDFS, HBase, Oozie and Zookeeper. You will connect the Hadoop cluster with Pentaho data integration, analytics, Pentaho server and report designer. This project will give you complete working knowledge on the Pentaho ETL tool.

Highlights :

Working knowledge of ETL and Business Intelligence
Configuring Pentaho to work with Hadoop Distribution
Loading, Transforming and Extracting data into Hadoop cluster

Project 6 : Multi-node cluster setup

Industry : General

Problem Statement : How to setup a Hadoop real-time cluster on Amazon EC2.

Topics : This is a project that gives you opportunity to work on real world Hadoop multi-node cluster setup in a distributed environment. You will get a complete demonstration of working with various Hadoop cluster master and slave nodes, installing Java as a prerequisite for running Hadoop, installation of Hadoop and mapping the nodes in the Hadoop cluster.

Highlights :

Hadoop installation and configuration
Running a Hadoop multi-node using a 4 node cluster on Amazon EC2
Deploying of MapReduce job on the Hadoop cluster.

Project 7 : Hadoop Testing using MRUnit

Industry : General

Problem Statement : How to test MapReduce applications

Topics : In this project you will gain proficiency in Hadoop MapReduce code testing using MRUnit. You will learn about real world scenarios of deploying MRUnit, Mockito, and PowerMock. This will give you hands-on experience in the various testing tools for Hadoop MapReduce. After completion of this project you will be well-versed in test driven development and will be able to write light-weight test units that work specifically on the Hadoop architecture.

Highlights :

Writing JUnit tests using MRUnit for MapReduce applications
Doing mock static methods using PowerMock & Mockito
MapReduce Driver for testing the map and reduce pair

Project 8 : Hadoop Weblog Analytics

Industry : Internet services

Problem Statement : How to derive insights from web log data

Topics : This project is involved with making sense of all the web log data in order to derive valuable insights from it. You will work with loading the server data onto a Hadoop cluster using various techniques. The web log data can include various URLs visited, cookie data, user demographics, location, date and time of web service access, etc. In this project you will transport the data using Apache Flume or Kafka, workflow and data cleansing using MapReduce, Pig or Spark. The insight thus derived can be used for analyzing customer behavior and predict buying patterns.

Highlights :

Aggregation of log data
Apache Flume for data transportation
Processing of data and generating analytics

Project 9 : Hadoop Maintenance

Industry : General

Problem Statement : How to administer a Hadoop cluster

Topics : This project is involved with working on the Hadoop cluster for maintaining and managing it. You will work on a number of important tasks that include recovering of data, recovering from failure, adding and removing of machines from the Hadoop cluster and onboarding of users on Hadoop.

Highlights :

Working with name node directory structure
Audit logging, data node block scanner, balancer.
Failover, fencing, DISTCP, Hadoop file formats.

Project 10 : Twitter Sentiment Analysis

Industry – Social Media

Problem Statement : Find out what is the reaction of the people to the demonetization move by India by analyzing their tweets.

Description : This Project involves analyzing the tweets of people by going through what they are saying about the demonetization decision taken by the Indian government. Then you look for key phrases, words and analyze them using the dictionary and the value attributed to them based on the sentiment that it is conveying.

Highlights :

Download the Tweets & Load into Pig Storage
Divide tweets into words to calculate sentiment
Rating the words from +5 to -5 on AFFIN dictionary
Filtering the Tweets and analyzing sentiment.

Project 11 : Analyzing IPL T20 Cricket

Industry – Sports & Entertainment

Problem Statement : Analyze the entire cricket match and get answers to any question regarding the details of the match.

Description : This project involves working with the IPL dataset that has information regarding batting, bowling, runs scored, wickets taken, and more. This dataset is taken as input and then it is processed so that the entire match can be analyzed based on the user queries or needs.

Highlights :

Load the data into HDFS
Analyze the data using Apache Pig or Hive
Based on user queries give the right output

Apache Spark Projects

Project 1 – Movie Recommendation

Industry : Entertainment

Problem Statement : How to recommend the most appropriate movie to a user based on his taste

Topics :This is a hands-on Apache Spark project deployed for the real-world application of movie recommendations. This project helps you gain essential knowledge in Spark MLlib which is a machine learning library, you will know how to create collaborative filtering, regression, clustering and dimensionality reduction using Spark MLlib. Upon finishing the project you will have first-hand experience in the Apache Spark streaming data analysis, sampling, testing, and statistics among other vital skills.

Highlights :

Apache Spark MLlib component
Statistical analysis
Regression & clustering

Project 2 –Twitter API Integration for tweet Analysis

Industry : Social Media

Problem Statement : Analyzing the user sentiment based on the tweet

Topics :This is a hands-on Twitter analysis project using the Twitter API for analyzing of tweets. You will integrate the Twitter API and do programing using Python or PHP for developing the essential server side codes. Finally you will be able to read the results for various operations by filtering, parsing, and aggregating it depending on the tweet analysis requirement.

Highlights :

Making requests to Twitter API
Building the server side codes
Filtering, parsing & aggregating data

Project 3 –Data Exploration Using Spark SQL – Wikipedia data set

Industry : Internet

Problem Statement : Making sense of Wikipedia data using Spark SQL.

Topics :In this project you will be using the Spark SQL tool for analyzing the Wikipedia data. You will gain hands-on experience in integrating Spark SQL for various applications like batch analysis, machine learning, visualizing and processing of data, ETL processes along with real-time analysis of data.

Highlights :

Machine learning using Spark
Deploying data visualization
Spark SQL integration

Apache Spark – Scala Project

Project 1: Movie Recommendation

Topics – This is a project wherein you will gain hands-on experience in deploying Apache Spark for movie recommendation. You will be introduced to the Spark Machine Learning Library, a guide to MLlib algorithms and coding which is a machine learning library. Understand how to deploy collaborative filtering, clustering, regression, and dimensionality reduction in MLlib. Upon completion of the project you will gain experience in working with streaming data, sampling, testing and statistics.

Project 2: Twitter API Integration for tweet Analysis

Topics – With this project you will learn to integrate Twitter API for analyzing tweets. You will write codes on the server side using any of the scripting languages like PHP, Ruby or Python, for requesting the Twitter API and get the results in JSON format. You will then read the results and perform various operations like aggregation, filtering and parsing as per the need to come up with tweet analysis.

Project 3: Data Exploration Using Spark SQL – Wikipedia data set

Topics – This project lets you work with Spark SQL. You will gain experience in working with Spark SQL for combining it with ETL applications, real time analysis of data, performing batch analysis, deploying machine learning, creating visualizations and processing of graphs.

Apache Storm Project

Project 1. Call Log Analysis using Trident

Topics : In this project you will be working on call logs to decipher the data and gather valuable insights using Apache Storm Trident. You will extensively work with data about calls made from one number to another. The aim of this project is to resolve the call log issues with Trident stream processing and low latency distributed querying. You will gain hands-on experience in working with Spouts and Bolts along with various Trident functions, filters, aggregation, joins and grouping.

Project 2. Twitter Data Analysis using Trident

Topics : This is a project that involves working with Twitter data and processing it to extract patterns out of it. The Apache Storm Trident is the perfect framework for real-time analysis of tweets. Working with Trident you will be able to simplify the task of live Twitter feed analysis. In this project you will gain real world experience of working with Spouts, Bolts, and Trident filters, joins, aggregation, functions and grouping.

Project 3. US Presidential Election Result analysis using Trident DRPC Query

Topics : This is a project that lets you work on the US presidential election results and predict who is leading and trailing on a real-time basis. For this you exclusively work with Trident distributed Remote Procedure Call server. After completion of the project you will learn how to access data residing in a remote computer or network and deploy it for real-time processing, analysis and prediction.

Hadoop Tutorial

What is Apache Spark

Introduction to Spark

View previous class recording

Hadoop Testing

Hive Introduction

About Big Data Course

Existing Learner Feedback

Introduction Apache Storm

Why should I learn Hadoop, Spark, Scala, Storm Combo from Intellipaat?

Intellipaat is the pioneer of Hadoop training. This is an all-in-one Hadoop, Spark, Scala, Storm training designed to assist you grow rapidly in your career.

This Intellipaat all-in-one Combo course exclusively trains you in the most sought-after domains in the Hadoop and Big Data computational domains. You will gain hands-on experience in mastering the Hadoop ecosystem, Apache Spark and Storm processing tools, and Scala programming language for Spark application.

The entire training course content is fully aligned towards clearing the following certification exams: Cloudera Spark and Hadoop Developer Certification (CCA175), and Cloudera CCA Administrator Exam (CCA131).

This is a completely career-oriented training designed by industry experts. Your training program includes real time projects, step-by-step assignments to evaluate your progress and specially designed quizzes for clearing the requisite certification exams.

Intellipaat also offers lifetime access to videos, course materials, 24/7 Support, and course material upgrades to latest version at no extra fees. For Hadoop and Spark training you get the Intellipaat Proprietary Virtual Machine for Lifetime and free cloud access for 6 months for performing training exercises. Hence it is clearly a one-time investment.

What are the different modes of training that Intellipaat provides?

At Intellipaat you can enroll either for the instructor-led online training or self-paced training. Apart from this Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience and they have been actively working as consultants in the same domain making them subject matter experts. Go through the sample videos to check the quality of the trainers.

Can I request for a support session if I need to better understand the topics?

Intellipaat is offering the 24/7 query resolution and you can raise a ticket with the dedicated support team anytime. You can avail the email support for all your queries. In the event of your query not getting resolved through email we can also arrange one-to-one sessions with the trainers. You would be glad to know that you can contact Intellipaat support even after completion of the training. We also do not put a limit on the number of tickets you can raise when it comes to query resolution and doubt clearance.

Can you explain the benefits of the Intellipaat self-paced training?

Intellipaat offers the self-paced training to those who want to learn at their own pace. This training also affords you the benefit of query resolution through email, one-on-one sessions with trainers, round the clock support and access to the learning modules or LMS for lifetime. Also you get the latest version of the course material at no added cost. The Intellipaat self-paced training is 75% lesser priced compared to the online instructor-led training. If you face any problems while learning we can always arrange a virtual live class with the trainers as well.

What kind of projects are included as part of the training?

Intellipaat is offering you the most updated, relevant and high value real-world projects as part of the training program. This way you can implement the learning that you have acquired in a real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning and practical knowledge thus making you completely industry-ready. You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. Upon successful completion of the projects your skills will be considered equal to six months of rigorous industry experience.

Does Intellipaat offer job assistance?

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this we are exclusively tied-up with over 80 top MNCs from around the world. This way you can be placed in outstanding organizations like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation part as well.

Is it possible to switch from self-paced training to instructor-led training?

You can definitely make the switch from self-paced to online instructor-led training by simply paying the extra amount and joining the next batch of the training which shall be notified to you specifically.

How are Intellipaat verified certificates awarded?

Once you complete the Intellipaat training program along with all the real-world projects, quizzes and assignments and upon scoring at least 60% marks in the qualifying exam; you will be awarded the Intellipaat verified certification. This certificate is very well recognized in Intellipaat affiliate organizations which include over 80 top MNCs from around the world which are also part of the Fortune 500 list of companies.

This course is designed for clearing the following certification exams:

Cloudera Spark and Hadoop Developer Certification (CCA175)

Cloudera CCA Administrator Exam (CCA131)

The entire training course content is in line with respective certification program and helps you clear the requisite certification exam with ease and get the best jobs in the top MNCs.

As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.

At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.

Intellipaat Storm Certification and Course Completion certificate will be awarded on the completion of Project work (upon expert review) and on scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

Big Data Hadoop Training Review

Ramya

Monica Ramteke

Vikram Pratap

Santosh Kumar

Nitesh Kumar Dash

Vishal Navlani

Pradeep

Divyesh Garg

Deepak Punde

Somashekhar Hatti

Abhishek Chatterjee

Dileep Vuppaladhadiam

Sanjay Singh

Ashok Guntupalli

Vipin Panicker

Deep Desai

Ajay Khushani

Javed Shiraj

Sagar Sudhir Nayak

Deepanjan Acharya

Anveshan Reddy

Ranjan Kumar

Tareg Alnaeem

Great Course

One of the most intresting, valuable and enjoyable course i ever have. Excellent material and good tutoring. Highly recommended.
Monika Kadel

Good Stuff

You have been extremely helpful for making me understand all demanding big data technologies at one place.
Rashi G

Good Starter Kit

This course has been a good starter kit for understanding the Hadoop fundamentals with Other Technologies.
Ashwin Singhania

Wonderful Work

All videos are in-depth yet concise. I had no problem understanding the tough concepts. Wonderful job Intellipaat!
Purvi Narang

Superb Training

The course material is really helpful to understand the core concepts behind Hadoop, Spark and others...overall training is superb. Good work.

Course Advisor

Suresh Paritala

A senior software architect at NextGen Healthcare who has previously worked with IBM Corporation. Suresh has worked on Big Data, Data Science, advanced analytics, Internet of Things, Azure along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high impact projects in major corporations around the world.

David Callaghan

An experienced blockchain professional who has been bringing integrated blockchain particularly Hyperledger and Ethereum and big data solutions to the cloud. David has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.

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Online Classroom

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Key Features

Instructor Led Training : 102 Hrs

All the instructor led training sessions are recorded and made available in LMS within 24 hours. This includes video, course material, exercise files and data sets used during the session.

Self-paced Videos : 114 Hrs

You will get lifetime access to high quality self -learning video tutorials.

Exercises & Project Work : 166 Hrs

As part of the training multiple hands on exercises, assignments project work and Quiz are provided. Our technical support team is available always there to help you complete this successfully.

Get Certified & Job Assistance

This course is designed for clearing Cloudera Spark and Hadoop Developer Certification (CCA175), Cloudera CCA Administrator Exam (CCA131) exam, Apache Storm training organized by any reputed agency like Horton Works and Apache Spark Certification examination of any reputed company. At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate. Intellipaat enjoys strong relationship with 80+ MNCs across the globe. We have a dedicated team who will help you with your resume building once you complete the course and your resume will be forwarded to partner MNCs. Intellipaat don’t charge any extra fees for passing the resume to our partners and clients

Flexible Schedule

For Online Classroom training we provide flexible schedule. If you miss any session or you are not able to join the classes for the enrolled batch then you can reschedule your enrollment and join another batch or attend only the missed classes in another batch.

Lifetime free upgrade

Intellipaat courses come with lifetime free upgrade to latest version. It’s a lifetime investment in the skills you want to enhance

24 x 7 Lifetime Support & Access

Once you enroll to our course, you get lifetime access to the course as well as 24x7 lifetime technical support. All you need is to raise a ticket. In the case of instructor led training, the instructor will help you with your queries directly. If required, our tech support can provide you live support by accessing your machine remotely. Our top notch tech support ensures that all your doubts and problems faced during labs and project work are clarified round the clock.

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Big Data Hadoop, Spark, Storm, Scala Training

Online Classroom training will be provided for Big Data Hadoop, Apache Spark and Scala. For Apache Storm Self paced videos will be provided for self study.

About Course

What you will learn in this Training Course?

Who should take this Training Course?

What are the prerequisites for taking this Training Course?

Why should you take this Training Course?

Big Data Hadoop Course Content

Hadoop Installation & setup

Introduction to Big Data Hadoop. Understanding HDFS & Mapreduce

Deep Dive in Mapreduce

Introduction to Hive

Advance Hive & Impala

Introduction to Pig

Flume, Sqoop & HBase

Writing Spark Applications using Scala

Spark framework

RDD in Spark

Data Frames and Spark SQL

Machine Learning using Spark (Mlib)

Spark Streaming

Hadoop Administration – Multi Node Cluster Setup using Amazon EC2

Hadoop Administration – Cluster Configuration

Hadoop Administration – Maintenance, Monitoring and Troubleshooting

ETL Connectivity with Hadoop Ecosystem

Project Solution Discussion and Cloudera Certification Tips & Tricks

Following topics will be available only in self-paced Mode.

Hadoop Application Testing

Roles and Responsibilities of Hadoop Testing Professional

Framework called MR Unit for Testing of Map-Reduce Programs

Unit Testing

Test Execution

Test Plan Strategy and writing Test Cases for testing Hadoop Application

Scala Course Content

Introduction of Scala

Pattern Matching

Executing the Scala code

Classes concept in Scala

Case classes and pattern matching

Concepts of traits with example

Scala java Interoperability

Scala collections

Mutable collections vs. Immutable collections

Use Case bobsrockets package

Spark Course Content

Introduction to Spark

Spark Basics

Working with RDDs in Spark

Aggregating Data with Pair RDDs

Writing and Deploying Spark Applications

Parallel Processing

Spark RDD Persistence

Spark Streaming & Mlib

Improving Spark Performance

Spark SQL and Data Frames

Scheduling/ Partitioning

Apache Storm Course Content

Understanding Architecture of Storm

Installation of Apache storm

Introduction to Apache Storm

Apache Kafka Installation

Apache Storm Advanced

Storm Topology

Overview of Trident

Storm Components & classes

Cassandra Introduction

Boot Stripping

What Hadoop Projects You will be working on?

Apache Spark Projects

Apache Spark – Scala Project

Apache Storm Project

Big Data Hadoop Training Review

Course Advisor

$350

$650

Sales Offer

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