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BigData Hadoop Testing Specialization Program

  • Overview
  • Course Highlights
  • Pre-requisites and Eligibility
  • Syllabus
  • Audience for this course
  • Batches
  • Mode of Training
  • Big Data Certification
  • Key Features

Overview

The Big Data Hadoop Testing Course covers the various tools and framework in Hadoop Cluster and how to use the components. Hadoop Testing course deals with various tools, techniques, and frameworks to process Hadoop ecosystem technologies. Hadoop testing is done by Writing and running Hive queries on Hadoop.You will also learn the technique of functional and performance testing in order to detect, analyze and rectify errors in Hadoop and various test case scenarios. With this Hadoop testing training you will also be fully equipped with experience in various test case scenarios, proof of concepts implementation and real-world scenarios. With more and more Hadoop developers and Hadoop architects deployed on Hadoop projects, there is an equal and urgent necessity of Hadoop testers. This Big Data and Hadoop testing training will ensure that you gain the right skills which will open up opportunities in the Big Data testing domain as a Hadoop Tester.
The Big Data Testing Training helps to understand Hadoop ecosystem in-depth, demonstrate how to determine the right data permutations, and sample sizes, know about the source-to-target mapping documents, how to find the proper mechanisms to test different businesses rule, apply and understand automation testing for Big Data Testing. Big Data Hadoop Testing training will make the candidates proficient in HDFS architecture, MapReduce concepts, shuffle and ordering, mapper and reducer functions, data replication, data node, name node, the flow of data, Hadoop and Big Data Testing ecosystem, the workflow process of Big Data Testing, formulate, design and execute Big Data Testing test scripts, test cases, and test scenarios, deploy Hive for Big Data Testing analysis, and Hive for relational data analysis, unit testing of Big Data Testing mapper on MapReduce application and more through hands-on approaches, real-life case studies, industry-based scenarios.

Course Highlights

  • Introduction to Big Data & Hadoop Fundamentals
  • Overview of Big Data & HDFS Concepts along with Linux Commands
  • Understanding of Hadoop ecosystem architecture and components
  • Introduction to MapReduce, Yarn, Hive, impala, Hbase, Sqoop, Flume, Kafka
  • Deep dive in hive
  • Apache Sqoop - Moving Data into Hadoop (Vice-Versa)
  • Apache Hive Basics and Advance Components
  • Hue, Ambari, Cloudera Manager
  • Intro to NoSQL – Hbase , Phoenix
  • Oozie (Workflow Orchestration)
  • Learning Python
  • Apache Spark - General Purpose Cluster Computing Framework
  • Learning Apache Kafka , NIFI
  • Bigdata on cloud
  • Testing Big data Systems
  • Testing Methods, Tools, and Reporting
  • Testing Methods, Tools and Reporting on Analytics
  • Performance and Failover Testing
  • Infrastructure Setup, Design, and Implementation
  • Real-Time Project on Bigdata
  • Resume Building session and sample resumes
  • Interview Questions
  • Mock Interview and Mock Interview Answers

Pre-requisites and Eligibility

There are no pre-requisites for Big Data Hadoop and spark course. However, a basic understanding of computer science technicalities and Basic knowledge of Linux commands and SQL will be helpful but is not mandatory. Don't worry, We will cover Linux commands/scripting and SQL in detail in our course.

Syllabus

  • Topic 1: Overview of Big Data & HDFS Concepts along with Linux Commands
    • Bigdata Intro
    • HDFS Architecture
    • Linux and HDFS commands
    • Quiz
    • Assessment
  • Topic 2: Linux Scripting in detail
    • History
    • Architecture
    • Development Commands
    • Env Variables
    • File Management
    • Directories Management
    • Admin Commands
    • Advance Commands
    • Shell Scripting
    • Groups and User managements
    • Permissions
    • Important directory structure
    • Disk utilities
    • Compression Techniques
    • Misc Commands
    • Kernel, Shell
    • Terminal, SSH, GUI
    • Automation & Scripting in Linux
    • Quiz
    • Assessment
  • Topic 3: Deep Dive in Hadoop
    • What is Hadoop?
    • Evolution of Hadoop
    • Features of Hadoop
    • Characteristics of Hadoop
    • Hadoop compared with Traditional Dist. Systems
    • When to use Hadoop
    • Limitations of Hadoop
    • Components of Hadoop (HDFS, MapReduce, YARN)
    • Hadoop Architecture
    • Daemons in Hadoop Version 1 & 2
    • How Data is stored in Hadoop Cluster, Datacenter, Spilt, Block,
    • Rack Awareness, Replication, Heartbeat)
    • Hadoop 1.0 Limitation
    • NameNode High Availability
    • Quiz
    • Assessment
  • Topic 4: MapReduce - Distributed Computing Framework
    • MapReduce Intro
    • MapReducte - Theory / Depth
    • MapReduce Programming concept
    • Different types of files supported
    • MapReduce Job submission in YARN Cluster in details
    • Hadoop Testing MR UNIT and shell Hadoop Automation testing
    • Quiz
    • Assessment
  • Topic 5: Apache Sqoop - Moving Data into Hadoop (Vice-Versa)
    • Sqoop Fundamentals
    • Sqoop Import and Export
    • Sqoop Incremental
    • Sqoop Job
    • Sqoop Merge
    • Best practices & performance tuning
    • Sqoop Test Use cases
    • Sqoop Testing
    • Quiz
    • Assessment
  • Topic 6: Apache Hive Basics & Advance components
    • Introduction to Apache Hive
    • Understanding Apache Hive
    • Hive Practical
    • Hive to know more
    • Hive Definition Level Optimizations : Theory
    • Hive Definition Level Optimizations : Practical
    • Hive Query Level Optimizations : Theory
    • Hive Query Level Optimizations : Practical
    • Hive Windowing Functions
    • Hive Ranking
    • Hive Sorting
    • Hive File Format
    • Hive File Format - Practicals
    • Hive Compression Techniques
    • Hive Vectorization & Changing the Hive Engine
    • Hive SCD
    • Hive Sqoop, HBase Integration
    • Hive Schema evolution (AVSC) use cases using AVRO dataset
    • Quiz
    • Assessment
  • Topic 7: Hue, Ambari, Cloudera Manager
    • Introduction
    • Cluster formation guide and implementation
    • Deployment in Cloud
    • Full Visibility into Cluster Health
    • Provisioning, Managing and Monitoring Hadoop Clusters
    • Hue Introduction
    • Access Hive
    • Query executor
    • Data browser
    • Access Hive, HCatalog, Oozie, File Browser
    • Hortonworks/Cloudera
    • Cluster Design
    • Different nodes (Gateway, Ingestion, Edge)
    • System consideration
    • Commands (fsck, job, dfsadmin, distcp, balancer)
    • Schedulers in RM (Capacity, Fair, FIFO)
    • View all services in Ambari & Cloudera Manager
  • Topic 8: Intro to NOSQL - HBase
    • HBase Basics
    • CAP Theorem
    • Hbase Architecture
    • Hbase Practicals
    • Storage Hierarchy – Characteristics
    • Table Design
    • HMaster & Regions
    • Region Server & Zookeeper
    • Inside Region Server (Memstore, Blockcache, HFile, WAL)
    • Minor/Major Compactions
    • Role of Zookeeper
    • HBase Shell
    • Introduction to Filters
    • Row Key Design
    • Performance Tuning
    • Cassandra Overview
    • Integration with Hive
    • Quiz
    • Assessment
  • Topic 9: Oozie (Workflow Orchestration)
    • Introduction
    • History - Why Oozie
    • Components
    • Architecture
    • Workflow Engine
    • Nodes
    • Workflow
    • Coordinator
    • Action (MapReduce, Hive, Spark, Shell & Sqoop)
    • Introduction to Bundle
    • Email Notification
    • Error Handling
    • Scheduling of data pipeline
    • Invoking shell script, Sqoop, Hive & Spark
  • Topic 10: Python
    • Python Introduction
    • Evolution
    • Application
    • Features
    • Installation & Configuration
    • Objectives
    • Flow Control
    • Variables
    • Data types
    • Functions
    • Modules
    • OOPS
    • Python for Spark
    • Structures
    • Collection types
    • Looping Constructs
    • Dictionary & Tuples
    • File I/O
  • Topic 11: YARN
    • Introduction to YARN
    • YARN Architecture
    • YARN Components
    • YARN Longlived & Shortlived Daemons
    • YARN Schedulers
    • Job Submission under YARN
    • Multi tenancy support of YARN
    • YARN High Avalability
    • YARN Fault tolerance handling
    • MapReduce job submission using YARN
    • YARN UI
    • History Server
    • YARN Dynamic allocation
    • Containerization of YARN
    • Quiz
    • Assessment
  • Topic 12: Apache Spark - General Purpose Cluster Computing Framework
    • Scala Interview Prep Series
    • Spark Fundamental Theory
    • Spark Fundamental Practical
    • Quiz
    • Assessment
  • Topic 13: Apache Kafka
    • Kafka Introduction
    • Applications, Cluster Setup
    • Broker fault tolerance
    • Architecture
    • Components
    • Partitions & Replication
    • Distribution of messages
    • Producer & Consumer workload distribution
    • Topics management
    • Cluster deployment in cloud
  • Topic 14: Overview of Big Data Testing
    • Big Data and Bad Data
    • Characteristics of Big Data (3Vs)
    • Big Data Testing Vs. Traditional Database Testing
    • Tools used in Big Data Scenarios
    • Challenges in Big Data Testing
  • Topic 15: Testing Big data Systems
    • Big data Testing Strategy
    • Testing Steps in verifying Big Data Applications
    • Data Staging Validation
    • “MapReduce” Validation
    • Output validation Phase
  • Topic 16 : Testing Methods, Tools, and Reporting
    • For validation of Pre-Hadoop Processing
    • For Hadoop MapReduce Processes
    • For Data Extract and EDW Loading
  • Topic 17 : Testing Methods, Tools and Reporting on Analytics
    • Four Big Data reporting strategies
    • Methodology for report testing
    • Apache Falcon
  • Topic 18 : Performance and Failover Testing
    • Performance testing and Approach
    • Failover testing
    • Methods and tools
    • Jepsen
  • Topic 19 : Infrastructure Setup, Design, and Implementation
    • Hardware selection for master nodes
    • Hardware selection for slave nodes
    • Infrastructure setup key points
  • Topic 20: Real Time Project classes
    Real Time Projects on Bigdata which are diverse in nature covering various data sets from multiple domains such as banking, Healthcare, telecommunication, social media, insurance, and e-commerce.
  • Topic 21: Real Time Project on Bigdata
    • Project Testing Statement
    • Architectural Diagram and Solution
    • Project Demo Session
    • Various test case scenarios.
  • Topic 22:Interview Questions
  • Topic 23:Mock Interview and Mock Interview Answers
  • Topic 24:Resume Preperation session and sample resumes

Audience for this course

The course is ideal for Software engineers and programmers, Bigdata Hadoop developers and Hadoop administrators, Software testing Professionals including manual, automation and selenium testers, systems administrators, Java Developers, Quality Assurance, Tester, Tech Support and System Administrators, Data analysts and database administrators, Database Administrator, System architects, IT managers, IT administrators and operators, IT systems engineers, data engineers and database administrators, data analytics administrators, cloud, web engineers, Project Managers, Software Architects, ETL and Data Warehousing Professionals, Data Engineers, Data Analysts & Business Intelligence Professionals, DBAs and DB professionals.

Mode of Training

  • Classroom Training
  • Online Instructor-Led Live Training
  • Online Video Recorded Sessions Training

Week days batch

  • Class Room Training @ Anna nagar & OMR
  • Online Instructor LED Training for Other Locations
  • Online Vedio Recorderd Training sessions for Other Locations

Week end batch

  • Class Room Training @ Anna nagar & OMR
  • Online Instructor LED Training for Other Locations
  • Online Vedio Recorderd Training sessionsOther Locations

Fast track Batch

  • Class Room Training @ Anna nagar & OMR
  • Online Instructor LED Training for Other Locations
  • Online Vedio Recorderd Training sessions

Big Data Certification

Hiring companies are looking for certified Big Data Hadoop professionals. Our BigData Hadoop Certification oriented Training helps you to grab this opportunity and accelerate your career. we offer Hadoop online professional certification Guidance.

Key features

  • Real-Time Projects on Bigdata
  • 200+ Hours Course Duration
  • Job Oriented Training
  • 100% Placement guarantee
  • Fast track placement mode
  • Managed and mastered by highly skilled Industry Experts
  • both Classroom Training and Online Training
  • Online Professional Certification Guidance
  • Complete Career Guidance
  • Hands-on with 30+ Case Studies
  • Placement in Top MNC Company
  • Certification: Cloudera / Hortonworks / Databricks
  • Support 24/7 * 365
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