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Big Data Hadoop, Spark, Storm and Scala Training
The Big Data Hadoop certification combo course provided by the pioneering e-learning institute Intellipaat will help you master various aspects of Big Data Hadoop, Apache Storm, Apache Spark and Scala programming language. An online classroom training will be provided for Big Data Hadoop, Spark and Scala, and for Apache Storm self-paced videos will be provided for self-study.
About Course
What will you learn in this training course?
- Hadoop architecture
- Hadoop cluster setup and maintenance
- Data Science project life cycle
- Writing MapReduce programs
- YARN, Flume, Oozie, Impala and ZooKeeper
- Apache Storm architecture
- Storm topology, components and logic dynamics
- Deploying Apache Spark on Hadoop cluster
- Writing Spark applications in Python, Java and Scala
- In-depth Scala programming and implementation
- Trident spouts and filter in Storm
- Working on real-time Hadoop projects
Who should take up this training course?
- Software Developers, System Administrators and ETL Developers
- Project Managers
- Information Architects
- Data Scientists
What are the prerequisites for taking up this training course?
Why should you take up this training course?
Big Data Hadoop Course Content
Hadoop Installation and Setup
The architecture of Hadoop 2.0 cluster, what is High Availability and Federation, how to setup a production cluster, various shell commands in Hadoop, understanding configuration files in Hadoop 2.0, installing single node cluster with Cloudera Manager and understanding Spark, Scala, Sqoop, Pig and Flume
Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
Introducing Big Data and Hadoop, what is Big Data and where does Hadoop fit in, two important Hadoop ecosystem components, namely, Map Reduce and HDFS, in-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and 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, 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, 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 and what is a Group by clause
Advance Hive and Impala
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 and data storage in a different table
Introduction to Pig
Apache Pig introduction, its various features, 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 files and working with Group By,Filter By,Distinct,Cross,Split in Hive
Flume, Sqoop and HBase
Apache Sqoop introduction, overview, importing and exporting data, performance improvement with Sqoop, Sqoop limitations, introduction to Flume and understanding the architecture of Flume and 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 and deploying Disable, Scan and Enable Table
Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2
Create a 4-node Hadoop cluster setup, running the MapReduce Jobs on the Hadoop cluster, successfully running the MapReduce code and working with the Cloudera Manager setup
Hands-on Exercise – The method to build a multi-node Hadoop cluster using an Amazon EC2 instance and 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 NameNode, DataNode directory structures and files, what is a File system image and understanding Edit log.
Hands-on Exercise –The process of performance tuning in MapReduce
Hadoop Administration – Maintenance, Monitoring and Troubleshooting
Introduction to the checkpoint procedure, NameNode failure and how to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes
Hands-on Exercise –How to go about ensuring the MapReduce File System Recovery for 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 and 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 and 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 and creating MapReduce job in ETL tool
Project Solution Discussion and Cloudera Certification Tips and 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 and 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 and 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 and 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.), reporting defects to the development team or manager and driving them to closure, consolidating 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 MapReduce programs
Unit Testing
Automation testing using the OOZIE and 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 to Scala
Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics, Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), First Spark Application Using SBT/Eclipse, Spark Web UI, Spark in Hadoop Ecosystem.
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 and testing string equality in Scala, implicit classes in Scala, the concept of currying in Scala and various classes in Scala
Classes Concept in Scala
Learning about the Classes concept, understanding the constructor overloading, various abstract classes, the hierarchy types in Scala, the concept of object equality and 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 and handling of multiple traits extending
Scala Collections
Introduction to Scala collections, classification of collections, the difference between Iterator and Iterable in Scala and 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 and double-ended queue Deque, Stacks, Sets, Maps and 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 and examples 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 and 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 and the architecture of Spark
Working with RDDs in Spark
Spark RDD, creating RDDs, RDD partitioning, operations, and 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, collects map, save-as-text-files and 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 and coarse-grained update and 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 and 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 and coalesce and RDD actions
Spark RDD Persistence
The execution flow in Spark, understanding the RDD persistence overview, Spark execution flow, and Spark terminology, distribution shared memory vs. RDD, RDD limitations, Spark shell arguments, distributed persistence, RDD lineage, Key-Value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey and AggregateByKey
Spark MLlib
Introduction to Machine Learning, types of Machine Learning, introduction to MLlib, various ML algorithms supported by MLlib, Linear Regression, Logistic Regression, Decision Tree, Random Forest, K-means clustering techniques, building a Recommendation Engine
Hands-on Exercise: Building a Recommendation Engine
Integrating Apache Flume and Apache Kafka
Why Kafka, what is Kafka, Kafka architecture, Kafka workflow, configuring Kafka cluster, basic operations, Kafka monitoring tools, integrating Apache Flume and Apache Kafka
Hands-on Exercise: Configuring Single Node Single Broker Cluster, Configuring Single Node Multi Broker Cluster, Producing and consuming messages, Integrating Apache Flume and Apache Kafka.
Spark Streaming
Introduction to Spark Streaming, features of Spark Streaming, Spark Streaming workflow, initializing StreamingContext, Discretized Stream (DStreams), Input DStreams and Receivers, transformations on DStreams, Output Operations on DStreams, Windowed Operators and why it is useful, important Windowed Operators, Stateful Operators.
Hands-on Exercise: Twitter Sentiment Analysis, streaming using netcat server, Kafka-Spark Streaming and Spark-Flume Streaming
Improving Spark Performance
Introduction to various variables in Spark like shared variables and 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 Hive context, 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 variables and accumulators, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL and 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 and 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, Apache Storm features, comparing Storm with Hadoop, Storm execution and learning about Tuple, Spout and Bolt
Installation of Apache Storm
Installing Apache Storm and various types of run modes of Storm
Introduction to Apache Storm
Understanding Apache Storm and the data model
Apache Kafka Installation
Installation of Apache Kafka and its configuration
Apache Storm Advanced
Understanding of advanced Storm topics like Spouts, Bolts, Stream Groupings, Topology and its Life cycle and 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 and Call Log Analysis Topology for an analyzing call logs for calls made from one number to another
Overview of Trident
Understanding of Trident Spouts and its different types, various Trident Spout interface and components, familiarizing with Trident Filter, Aggregator and Functions and a practical and hands-on use case on solving call log problem using Storm Trident
Storm Components and 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 and its strengths and deployment.
Boot Stripping
Twitter Boot Stripping, detailed understanding of Boot Stripping, concepts of Storm and Storm Development Environment.
What Hadoop projects you will be working on?
Highlights :
- Sqoop data transfer from RDBMS to Hadoop
- Coding in Hive Query Language
- Data querying and analysis.
Highlights :
- MapReduce program for working on the data file
- Apache Pig for analyzing data
- Apache Hive data warehousing and querying
Highlights :
- Using Sqoop commands to bring the data into HDFS
- End to End flow of transaction data
- Working with the data from HDFS
Highlights :
- Manual Partitioning
- Dynamic Partitioning
- Bucketing
Highlights :
- Working knowledge of ETL and Business Intelligence
- Configuring Pentaho to work with Hadoop distribution
- Loading, transforming and extracting data into 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.
Highlights :
- Writing JUnit tests using MRUnit for MapReduce applications
- Doing mock static methods using PowerMock and Mockito
- MapReduce Driver for testing the map and reduce pair
Highlights :
- Aggregation of log data
- Apache Flume for data transportation
- Processing of data and generating analytics
Highlights :
- Working with Name Node directory structure
- Audit logging, data node block scanner and balancer.
- Failover, fencing, DISTCP and Hadoop file formats.
Highlights :
- Download the tweets and 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
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
Highlights :
- Apache Spark MLlib component
- Statistical analysis
- Regression and clustering
Highlights :
- Making requests to Twitter API
- Building the server-side codes
- Filtering, parsing and aggregating 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. You will understand how to deploy collaborative filtering, clustering, regression, and dimensionality reduction in MLlib. Upon the 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. While 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, Trident filters, joins, aggregation, functions and grouping.
Project 3 : The 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 the 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.
Sample Big Data Hadoop, Spark, Storm and Scala Video Tutorial
Hadoop Tutorial
What is Apache Spark?
Hive Introduction
Existing Learner Feedback
Intellipaat is the pioneer in Hadoop training. This is an all-in-one Hadoop, Spark, Storm and Scala training designed to assist you to 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 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 and step-by-step assignments to evaluate your progress and specifically 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 the latest version at no extra fee. For Hadoop and Spark training, you get Intellipaat Proprietary Virtual Machine for lifetime and free cloud access for six months for performing training exercises. Hence, it is clearly a one-time investment.
Big Data Hadoop, Spark, Storm and Scala Certification
This course is designed for clearing the following certification exams:
- Cloudera Spark and Hadoop Developer Certification (CCA175)
- Cloudera CCA Administrator Exam (CCA131)
The entire course content is in line with respective certification programs and helps you clear the requisite certification exams with ease and get the best jobs in 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 scenarios, 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 help you score better.
Intellipaat Storm Certification and Course Completion Certificate will be awarded upon the completion of the project work (after expert review) and upon scoring 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 Reviews
Course Advisor
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.
An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan 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.
$350
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Sales Offer
- To avail this offer, enroll before 17th August 2019.
- This offer cannot be combined with any other offer.
- This offer is valid on selected courses only.
- Cashback will be accumulated in Intellipaat e-wallet
- Please use coupon codes mentioned below to avail the offer
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Great course
One of the most interesting, valuable and enjoyable course I ever had. Excellent material and good tutoring. Highly recommended.
Good stuff
You have been extremely helpful for making me understand all demanding Big Data technologies at one place.
Good starter kit
This course has been a good starter kit for understanding the Hadoop fundamentals, along with other technologies.
Wonderful work
All videos are in-depth yet concise. I had no problem understanding the tough concepts. Wonderful job Intellipaat!
Superb training
The course material is really helpful to understand the core concepts behind Hadoop, Spark and others...Overall training is superb. Good work.