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Big Data Training.

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60 HRS. Duration.

The term Big knowledge refers associate to any or all the information that’s being generated across the world at an new rate. This knowledge might be either structured or unstructured. Today’s business enterprises owe a large a part of their success to Associate in Nursing economy that’s firmly knowledge-oriented.

Data drives the trendy organizations of the globe and therefore creating sense of this knowledge and unraveling the assorted patterns and revealing unseen connections inside the huge ocean of information becomes essential and a massively bounties endeavor so. there’s a desire to convert huge knowledge into Business Intelligence that enterprises will promptly deploy. higher knowledge ends up in higher higher cognitive process Associate in Nursing an improved thanks to strategist for organizations notwithstanding their size, geography, market share, client segmentation and such different categorizations. Hadoop is that the platform of selection for operating with very massive volumes of information.


The most flourishing enterprises of tomorrow are those which will add up of all that knowledge at very high volumes and speeds so as to capture newer markets and client base.

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The Big Data industry has gained Highly growth in recent years and recent research has estimated that the Big Data market is more than a $ 50 billion industry. Garner’s survey confirmed that 64% of companies invested in Big Data in 2013 and that number continues to increase every year. With the challenges of manipulating and collecting Bigdata Big Data, the opportunities are unlimited for anyone wishing to enter the Big Data Hadoop ecosystem. , JAVA professionals, analytics, ETL professionals, data storage professionals, test professionals and project managers can receive Hadoop training in Chennai and make a career change. Our excellent data training in Chennai will provide practical experience to meet the demands of the industry.
huge knowledge could be a term related to complicated and enormous datasets. A {relational knowledgebase|electronic database|on-line database|computer database|electronic information service} cannot handle huge data, and that’s why special tools and methods are used to perform operations on a vast collection of data. Big knowledge permits firms to grasp their business higher and helps them derive purposeful info from the unstructured and data collected on an everyday basis. Big knowledge additionally permits the businesses to require higher business selections backed by knowledge.

The five V’s of Big data is as follows:

• Volume – Volume represents the volume i.e. amount of data that is growing at a high rate i.e. data volume in Petabytes

• Velocity – Velocity is the rate at which data grows.

Social media contributes a significant role within the speed of growing knowledge.

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Big knowledge has bound characteristics and therefore is outlined victimization 4Vs namely:

Volume: the number of information that companies will collect is actually huge and therefore the amount of the information becomes a important consider massive knowledge analytics.

Velocity: the speed at that new knowledge is being generated all because of our dependence on the net, sensors, machine-to-machine knowledge is additionally vital to analyze massive knowledge in a very timely manner.

Variety: the information that's generated is totally heterogeneous within the sense that it might be in numerous formats like video, text, database, numeric, detector knowledge so on and therefore understanding the sort of massive knowledge could be a key issue to unlocking its worth.

Veracity: knowing whether or not the information that's offered is returning from a reputable supply is of utmost importance before deciphering and implementing massive knowledge for business desires.

Viscosity: refers to the inertia when navigating through a data collection. For example due to the variety of data sources, the velocity of data flows and the complexity of the required processing. Virility – measures the speed at which data can spread through a network.


Virility: measures the speed at which data can spread through a network. Veracity – relates to the quality and origin of the data to determine whether it is trustworthy, conflicting or impure. Value – refers to the value that could be extracted from certain data and how Big Data techniques could increase this value.

Value: When we talk about value, we’re referring to the worth of the data being extracted.  Having endless amounts of data is one thing, but unless it can be turned into value it is useless.  While there is a clear link between data and insights, this does not always mean there is value in Big Data.  The most important part of embarking on a big data initiative is to understand the costs and benefits of collecting and analyzing the data to ensure that ultimately the data that is reaped can be monetized. 

Visibility – the state of being able to see or be seen – is implied. Data from disparate sources need to be stitched together where they are visible to the technology stack making up Big Data.

o    Lesson 1 Introduction to the course
o    1.1 Introduction to the course
o    1.2 Access to the practice laboratory
o    Lesson 2 Introduction to Big Data and Hadoop
o    1.1 Introduction to Big Data and Hadoop
o    1.2 Introduction to Big Data
o    1.3 Big Data Analysis
o     1.4 What is Big Data?
o     1.5 Four Big Data Vs
o    1.6 Case study of the Royal Bank of Scotland
o    1.7 Challenges of the traditional system.
o     1.8 Distributed systems
o    1.9 Introduction to Hadoop
o    1.10 Components of the Hadoop ecosystem, part one
o    1.11 Components of the Hadoop ecosystem, part two
o    1.12 Components of the Hadoop ecosystem, part three
o    1.13 Commercial distributions of Hadoop
o    1.14 Demo: Simpleness Cloud lab Tutorial
o    1.15 Themes
o     Verification of knowledge
o    Lesson 3 Hadoop architecture, spread storage (HDFS) and YARN
o    2.1 Distributed storage of Hadoop Architecture (HDFS) and YARN
o     2.2 What is HDFS?
o    2.3 Need for HDFS
o     2.4 Regular vs. HDFS file system
o     2.5 HDFS features
o    2.6 HDFS architecture and components
o    2.7 High availability cluster implementations
o     2.8 Namespace of the HDFS component file system
o    2.9 Data block division
o    2.10 Data Replication Topology
o    2.11 HDFS command line
o    2.12 Demo: Common HDFS Commands
o    HDFS command line
o    2.13 Introduction of FIO
o    2.14 HILO use case
o    2.15 HILO and its architecture
o    2.16 Resource Manager
o    2.17 How Resource Manager works
o    2.18 Application Master
o    2.19 How YARN runs an application
o    2.20 Tools for YARN developers
o    2.21 Demo: tutorial of the first part of the cluster
o    2.22 Demo: tutorial part two of the cluster
o    2.23 Main topics
o    Verification of knowledge
o    Hadoop, distributed storage (HDFS) and YARN Lesson
4 Data ingestion in Big Data and ETL systems
o     3.1 Data ingestion in Big Data and ETL systems
o    3.2 Summary of data collection, part one
o    3.3 General description of data entry, part two
o    3.4 Apache Sqoop
o    3.5 Sqoop and its uses
o    3.6 Sqoop Processing
o    3.7 Sqoop import process
o    3.8 Square connectors
o    3.9 Demo: import and export data from MySQL to HDFS
o    Apache Sqoop
o    3.9 Apache Channel
o    3.10 Channel Template
o    3.11 Channel scalability
o    3.12 Components in the channel architecture
o    3.13 Channel component configuration
o    3.15 Demo: Twitter data intake
o    3.14 Apache Kafka
o    3.15 Adding user activity using Kafka
o    3.16 Kafka data model
o    3.17 Partitions
o    3.18 Apache Kafka Architecture
o    3.21 Demo: Kafka Cluster configuration
o    3.19 Example of producer side API
o    3.20 Consumer side API
o    3.21 Example of consumer side API
o    3.22 Kafka Connect
o    3.26 Demonstration: creation of a Kafka data pipeline sample with producer and consumer
o    3.23 Main topics
o    Verification of knowledge
o    Data ingestion in Big Data and ETL systems
Lesson 5 Distributed Processing - MapReduce Framework and Pig
o    4.1 MapReduce and Pig distributed processing framework
o    4.2 Distributed processing in MapReduce
o    4.3 Word count example
o    4.4 Map execution phases
o    4.5 Distributed map execution environment of two nodes
o    4.6 MapReduce Jobs
o    4.7 Interaction of work at work Hadoop MapReduce
o    4.8 Setting up the environment for MapReduce development
o     4.9 Class set
o    4.10 Create a new project
o    4.11 Advanced MapReduce
o    4.12 Types of data in Hadoop
o    4.13 OutputFormats in MapReduce
o    4.14 Use of distributed cache
o    4.15 MapReduce Unions
o    4.16 Replicated Union
o    4.17 Introduction to pork
o    4.18 Pork components
o    4.19 Pig data model
o    4.20 Interactive pig modes
o    4.21 Swine operations
o    4.22 Various relationships with developers
o    4.23 Demo: analysis of web log data using MapReduce
o    4.24 Demonstration: analysis of sales data and KPI resolution using PIG
o    Apache pig
o    4.25 Demo: Wordcount
o    4.23 Main topics
o    Verification of knowledge
o    Distributed processing: MapReduce Framework and Pig
Apache Hive Lesson 6
o    5.1 Apache Hive
o    5.2 Hive SQL on Hadoop MapReduce
o    5.3 Hive architecture
o    5.4 Interfaces to execute Hive queries
o    5.5 Running Beeline from the command line
o    5.6 Hive Metastore
o    5.7 Hive DDL and DML
o    5.8 Create new table
o    5.9 Data types
o    5.10 Data validation
o    5.11 File format types
o    5.12 Data serialization
o     5.13 Avro table and section diagram
o    5.14 Sampling hive and buckle optimization partitioning
o     5.15 Unpartitioned table
o    5.16 Data entry
o    5.17 Dynamic partitioning in Hive
o    5.18 Bracketing
o    5.19 What do the cubes do?
o    5.20 Hive Analytics UDF and UDAF
o    5.21 Other functions of the hive
o    5.22 Demonstration: real-time analysis and data filtering
o    5.23 Demonstration: real world problem
o     5.24 Demonstration: data representation and import with Hive
o    5.25 Topics
o     Verification of knowledge
o    Apache hive
o    Lesson 7: NoSQL databases - HBase
o    6.1 NoSQL HBase databases
o    6.2 Introduction to NoSQL
o     Demonstration: cable adjustment
o    6.3 HBase Overview
o    6.4 HBase Architecture
o    6.5 Data model
o    6.6 Connection to HBase
o    HBase Shell
o    6.7 Main topics
o     Verification of knowledge
NoSQL databases - HBaseLesson

8 Basics of Functional Programming and Scala
  7.1 Basics of Functional Programming and Scala
  7.2 Introduction to Scala
  7.3 Demo: Scala Installation
  7.3 Functional Programming
  7.4 Programming with Scala
  Demo: Basic Literals and Arithmetic Operators
  Demo: Logical Operators
  7.5 Type Inference Classes Objects and Functions in Scala
  Demo: Type Inference Functions Anonymous Function and Class
  7.6 Collections
  7.7 Types of Collections
  Demo: Five Types of Collections
  Demo: Operations on List
  7.8 Scala REPL
  Demo: Features of Scala REPL
  7.9 Key Takeaways
  Knowledge Check
  Basics of Functional Programming and Scala
Lesson 9 Apache Spark Next Generation Big Data Framework
  8.1 Apache Spark Next Generation Big Data Framework
  8.2 History of Spark
  8.3 Limitations of MapReduce in Hadoop
  8.4 Introduction to Apache Spark
  8.5 Components of Spark
  8.6 Application of In-Memory Processing
  8.7 Hadoop Ecosystem vs Spark
  8.8 Advantages of Spark
  8.9 Spark Architecture
  8.10 Spark Cluster in Real World
  8.11 Demo: Running a Scala Programs in Spark Shell
  8.12 Demo: Setting Up Execution Environment in IDE
  8.13 Demo: Spark Web UI
  8.11 Key Takeaways
  Knowledge Check
  Apache Spark Next Generation Big Data Framework
Lesson 10 Spark Core Processing RDD
  9.1 Processing RDD
  9.1 Introduction to Spark RDD
  9.2 RDD in Spark
  9.3 Creating Spark RDD
  9.4 Pair RDD
  9.5 RDD Operations
  9.6 Demo: Spark Transformation Detailed Exploration Using Scala Examples
  9.7 Demo: Spark Action Detailed Exploration Using Scala
  9.8 Caching and Persistence
  9.9 Storage Levels
  9.10 Lineage and DAG
  9.11 Need for DAG
  9.12 Debugging in Spark
  9.13 Partitioning in Spark
  9.14 Scheduling in Spark
  9.15 Shuffling in Spark
  9.16 Sort Shuffle
  9.17 Aggregating Data with Pair RDD
  9.18 Demo: Spark Application with Data Written Back to HDFS and Spark UI
  9.19 Demo: Changing Spark Application Parameters
  9.20 Demo: Handling Different File Formats
  9.21 Demo: Spark RDD with Real-World Application
  9.22 Demo: Optimizing Spark Jobs
  9.23 Key Takeaways
  Knowledge Check
  Spark Core Processing RDD
Lesson 11 Spark SQL - Processing DataFrames
  10.1 Spark SQL Processing DataFrames
  10.2 Spark SQL Introduction
  10.3 Spark SQL Architecture
  10.4 DataFrames
  10.5 Demo: Handling Various Data Formats
  10.6 Demo: Implement Various DataFrame Operations
  10.7 Demo: UDF and UDAF
  10.8 Interoperating with RDDs
  10.9 Demo: Process DataFrame Using SQL Query
  10.10 RDD vs DataFrame vs Dataset
  Processing DataFrames
  10.11 Key Takeaways
  Knowledge Check
  Spark SQL - Processing DataFrames
Lesson 12 Spark MLLib - Modelling BigData with Spark
  11.1 Spark MLlib Modeling Big Data with Spark
  11.2 Role of Data Scientist and Data Analyst in Big Data
  11.3 Analytics in Spark
  11.4 Machine Learning
  11.5 Supervised Learning
  11.6 Demo: Classification of Linear SVM
  11.7 Demo: Linear Regression with Real World Case Studies
  11.8 Unsupervised Learning
  11.9 Demo: Unsupervised Clustering K-Means
  11.10 Reinforcement Learning
  11.11 Semi-Supervised Learning
  11.12 Overview of MLlib
  11.13 MLlib Pipelines
  11.14 Key Takeaways
  Knowledge Check
  Spark MLLib - Modeling BigData with Spark
Lesson 13 Stream Processing Frameworks and Spark Streaming
  12.1 Stream Processing Frameworks and Spark Streaming
  12.1 Streaming Overview
  12.2 Real-Time Processing of Big Data
  12.3 Data Processing Architectures
  12.4 Demo: Real-Time Data Processing
  12.5 Spark Streaming
  12.6 Demo: Writing Spark Streaming Application
  12.7 Introduction to DStreams
  12.8 Transformations on DStreams
  12.9 Design Patterns for Using ForeachRDD
  12.10 State Operations
  12.11 Windowing Operations
  12.12 Join Operations stream-dataset Join
  12.13 Demo: Windowing of Real-Time Data Processing
  12.14 Streaming Sources
  12.15 Demo: Processing Twitter Streaming Data
  12.16 Structured Spark Streaming
  12.17 Use Case Banking Transactions
  12.18 Structured Streaming Architecture Model and Its Components
  12.19 Output Sinks
  12.20 Structured Streaming APIs
  12.21 Constructing Columns in Structured Streaming
  12.22 Windowed Operations on Event-Time
  12.23 Use Cases
  12.24 Demo: Streaming Pipeline
  Spark Streaming
  12.25 Key Takeaways
  Knowledge Check
  Stream Processing Frameworks and Spark Streaming
Lesson 14 Spark GraphX
  13.1 Spark GraphX
  13.2 Introduction to Graph
  13.3 Graphx in Spark
  13.4 Graph Operators
  13.5 Join Operators
  13.6 Graph Parallel System
  13.7 Algorithms in Spark
  13.8 Pregel API
  13.9 Use Case of GraphX
  13.10 Demo: GraphX Vertex Predicate
  13.11 Demo: Page Rank Algorithm
  13.12 Key Takeaways
  Knowledge Check
  Spark GraphX
  13.14 Project Assistance
Practice Projects
Car Insurance Analysis
Transactional Data Analysis

Learn Big Data and Hadoop Spark with World class Experts

You will be going through detailed 2 to 3 months of Big Data Hands-on training

  • Detailed instructor led sessions to help you become a proficient Expert in Big data.
  • Build a dig data professional portfolio by working on hands on assignments and projects.
  • Personalised mentorship from professionals working in leading companies.
  • Lifetime access to downloadable big data course materials, interview questions and project resources.
Best AWS Training Program In Chennai  

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Enroll for the Best Big data Training with Certification and become a Cloud Expert

To know more details about the big data Course and its services, Real-time projects and placements, Ring us ✆ +91-7358655420

Big data Training in Chennai

We understand that once you have completed our industry standard certification programs, you will be eager to begin your career in this high growth potential industry. We go beyond training to provide useful and active lifelong assistance in various areas that will allow you to approach your career with confidence. As you work to obtain certification in our courses and join as a professional big data analyst, our team offers some important benefits. We call this our form of accelerated learning opportunities and advantages to ensure you get the initial advantage in this industry. Obviously, to take advantage of our positioning assistance, it is mandatory to delete the certification process.

  • Live work updates: our team worked to create an autonomous structure that goes beyond simple training in terms of helping you find suitable jobs. We provide some easy-to-use features designed to provide you with a solid foundation to develop your career. According to industry reports, in India, Big Data analysis and related IT services will create between 15,000 and 20,000 skilled jobs by 2015.
  • Our Student Corner contains real-time job updates compiled in multiple industries and locations to help you gather information in one place to help you make informed decisions. The tool provides updates on relevant work at all levels and makes it accessible to you. The good thing is that you can now concentrate on your training while working behind the scenes to get all the information you need to explore suitable jobs to help you get started with your work objective. We also present companies that can try to hire us directly, which is an additional advantage for our students.

Don't stop learning and check this other courses.


Big data Certified Solutions Architect – Associate Exam Details

First of all, Big data Solution Architect Associate level certification is one of the most popular certification for an individual who perform solution architect role. Also, this certification also has more number of job opportunities and good payscale as well, so that we have shared the certification details Such as Exam Duration, Exam level, Big data training and certification cost given below.

We are also providing assistance to clear all the levels of Big data certification exams on successfully completing the Big data course in our institute. To know more about the training and certification please feel free to reach us via  +91-7358655420

Placement Assistance after completing Big data course in Chennai

We are providing the complete placement assistance for each and every candidate according to their need. Our placement approach will be unique and professional which is handled down by a separate team of experienced professionals. The team will guide you in,

  • Resume Building
  • Mock Interview by Professionals
  • Updating the frequently asked interview questions & answers
  • Helping you to Complete Big data Certifications.
  • Regular Assignments & Assessment to examine the candidate’s knowledge
  • Frequent update about the latest job opportunities available in and around Chennai

Big data Training with Placement in Chennai

Creation of resumes: In today’s highly competitive labor market, we realize that first impressions count, and a well-written and industry-centered curriculum is one of the best ways to make a lasting first impression. You will receive expert guidance and professional contributions to help you create a relevant, professional and impressive curriculum. This would be the first step to get the coveted job and we are here to help you.

Simulated interview: While a well-written resume and a social media profile help you accept the early stages, breaking the interview with senior corporate executives can be a challenge. This is where we take action, with our experience and ownership in the industry, to help you feel safe in this situation. Our mock interview sessions are designed to make you feel comfortable simulating a live interview scenario. The goal is to help you understand your weakest areas and your strengths, to help you work on the aspects that may pose a challenge in the actual interview. We are sure that by attending these sessions you can face the real interview with more confidence and be well prepared too.

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Highlights of Big data Training by Placement Point Solutions:

  1. The use of big data reduces your costs

A recent Tech Cocktail article discusses how Twiddly & Company Realtors reduces their costs by 15%. The company compared the maintenance charges of the contractor with the average of its other suppliers. Through this process, the company identified and eliminated invoice processing errors and automated service schedules.

  1. The use of big data increases its efficiency

The use of digital technology tools increases the efficiency of your business. Using tools such as Google Maps, Google Earth and social networks, you can perform many tasks directly on your desktop, without travel expenses. These tools also save a lot of time.

  1. Using Big Data improves your prices

Use a business intelligence tool to evaluate your finances, which can give you a clearer idea of   where your business is.

  1. You can compete with large companies

Using the same tools that large companies do allows you to be in the same field of play. Your business becomes more sophisticated by taking advantage of the tools available for use.

  1. Allows you to focus on local preferences.

Small businesses must focus on the local environment they serve. Big Data allows you to further expand the likes and dislikes of your local customer. When your company knows the preferences of its customers combined with a personal touch, it has an advantage over its competition.

  1. The use of Big Data helps increase sales and loyalty

The fingerprints we leave behind reveal a great deal of information about our preferences, beliefs, etc. This information allows companies to adapt their products and services exactly to what the customer wants. A fingerprint is left when your customers browse online and publish on social media channels.

  1. The use of Big Data guarantees the hiring of the right employees.

Recruitment firms can check candidate resumes and LinkedIn profiles for keywords that match the job description. The hiring process is no longer based on the candidate’s appearance on paper and how they are perceived personally.

How companies can analyze big data

To analyze big data, you must first identify the problems that need solutions or answers. Then try to identify the answer to your question and ask yourself: ‘How can I get the data to solve it?’ Or ‘what can big data do for my business?’

Your Big Data solutions should be easy to use, match what you thought and flexible enough to serve your business now and in the future.

Research the most reliable tool for the problem you need to solve. For example, if you want to launch more effective promotions and marketing campaigns, you can use Canopy Labs, which predicts customer behavior and sales trends.

There are many tools out there that are cheap

Ranked as Best Big data Training in Chennai

  • Placement Point Solutions is Hadoop’s main training center with world-class infrastructure and laboratory facilities. The most attractive thing is that candidates can choose from several IT training courses. Placement Point Solutions prepares thousands of candidates for Hadoop training with an affordable rate structure sufficient for the best Hadoop training and participates in Hadoop classes.
  • Hadoop is an open source technology. Hadoop stores and processes most of the data in any format. The volumes of data increase day by day with the evolution of social networks, taking into account that this technology is really very important.
  • At Placement Point Solutions, we guarantee the best Hadoop training with various concepts such as data analysis, big data, HDFS, Hadoop installation modes, Hadoop development tasks: MapReduce programming, Hadoop ecosystems – PIG, HIVE, SQOOP, HBASE. and another.
  • Placement Point Solutions offers a cutting-edge certification course in the revolutionary field of BigData to begin a glorious career as a qualified data scientist. It is a project-based training. If you are looking for the best Hadoop course, SkyInfotech is the right place for you. We believe in the fact that a student can develop a lot of knowledge in a stress-free environment and that is why we offer an excellent planned learning program.
  • Placement Point Solutions offers a cutting-edge certification course in the revolutionary field of BigData to begin a glorious career as a qualified data scientist. It is a project-based training. If you are looking for the best Hadoop course, Placement Point Solutions is the right place for you. We believe in the fact that a student can develop a lot of knowledge in a stress-free environment and that is why we offer an excellent planned learning program.
  • The Placement Point Solutions educational team believes in creating a beginner from scratch and creating a specialist. Here the various forms of education are carried out; Tests, simulated tasks and practical problem-solving lessons are performed. Realism-based training modules are designed primarily by Sky Infotech to highlight an expert.
  • Hadoop training is administered during classes Monday through Friday from 9 a.m. at 6 p.m., weekend classes at the same time. We also agree if a candidate wants to learn the best Hadoop training in less time.
  • Hadoop architecture
  •  Learning objectives: in this module you will understand what Big Data is, what are the limitations of existing solutions for the Big Data problem, how Hadoop solves the Big Data problem, what are the common components of the Hadoop ecosystem, Architecture of Hadoop, Hadoop architecture, HDFS and map reduction structure and anatomy of writing and reading files.
  •  Topics: What is Big Data, Hadoop Architecture, Hadoop ecosystem components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Hadoop Server Roles: NameNode, Secondary NameNode and DataNode, Writing and Reading Anatomy files.
  •  Hadoop cluster configuration and data loading.
  •  Learning objectives: in this module you will learn the architecture and configuration of Hadoop Cluster, important configuration files in a Hadoop Cluster, data loading techniques.
  •  Topics: Hadoop cluster architecture, Hadoop cluster configuration files, Hadoop cluster modes, multi-node Hadoop cluster, typical production Hadoop cluster, MapReduce task execution, common Hadoop shell commands, Data loading techniques: FLUME, SQOOP, Hadoop copy commands, Hadoop project: Data loading.
  •  Hadoop MapReduce structure
  •  Learning objectives: in this module, you will understand the structure of Hadoop MapReduce and how MapReduce works on data stored in HDFS. In addition, you will learn what are the different types of input and output formats in the framework of MapReduce and their use.
  •  Topics: Hadoop data types, Hadoop MapReduce paradigm, assignment and reduction tasks, MapReduce execution framework, partitioners and combiners, input formats (divisions and input registers, text input, binary input, multiple entries) , output formats (TextOutput, BinaryOutPut, Multiple Output), Hadoop Project: MapReduce Programming.
  •  Advanced MapReduce
  •  Learning objectives: in this module, you will learn advanced MapReduce concepts such as counters, planners, custom writers, compression, serialization, adjustment, error handling and management of complex MapReduce programs.
  •  Topics: counters, customizable records, unit tests: JUnit and MRUnit test framework, error handling, adjustment, MapReduce advance, Hadoop design: advanced MapReduce programming and error handling.
  •  Pork and Latin pig
  •  Learning objectives: in this module, you will learn what Pig is, in what kind of use case we can use Pig, since Pig is strongly associated with the MapReduce and Pig Latin scripts.
  •  Topics: installation and execution of Pig, Grunt, Pig data model, Latin Pig, development and test of Latin Pig script, writing evaluation, filtering, loading and storage functions, Hadoop Project: Scripting Pig.
  •  Hive and HiveQL
  •  Learning objectives: this module will help you understand the installation of Apache Hive, load and query data in Hive, etc.
  •  Topics: Hive architecture and installation, comparison with the traditional database, HiveQL: data types, operators and functions, Hive tables (managed tables and external tables, partitions and cubes, storage formats, data import, change tables, drop-down tables), data query (Classification and aggregation, map reduces scripts, combinations and subqueries, views, map and reduces lateral combinations to optimize the query).
  •  Advanced databases of Hive, NoSQL and HBase
  •  Learning objectives: in this module you will understand the concepts of Advanced Hive, such as UDF. You will also get a deep understanding of what HBase is, how to load data in HBase and query HBase data using the client.
  •  Topics: Hive: Hive data manipulation, user-defined functions, append data to the existing table, Hive custom map / reduction, Hadoop project: Hive script, HBase: HBase introduction, client API and its characteristics , available client, HBase Architecture, MapReduce Integration.
  •  Advanced HBase and Handler
  •  Learning objectives: this module will cover the concepts of Advance HBase. You will also learn what Zookeeper is, how it helps monitor a cluster, why HBase uses Zookeeper and how to create applications with Zookeeper.
  •  Topics: HBase: advanced use, schema design, advanced indexing, coprocessors, Hadoop design: HBase tables The ZooKeeper service: data model, operations, implementation, consistency, sessions and states.
  •  Hadoop 2.0, MRv2 and HILO
  •  Learning objectives: in this module you will understand the new functions added in Hadoop 2.0, namely YARN, MRv2, high availability of NameNode, HDFS federation, Windows support, etc.
  • Topics: programmers: fair and capacity, new features of Hadoop 2.0: high availability of NameNode, federation HDFS, MRv2, YARN, execution of MRv1 in YARN, update your existing MRv1 code to MRv2 programmatically in the YARN framework.
  • Hadoop and Apache Oozie project environment
  •  Learning objectives: in this module you will understand how several components of the Hadoop ecosystem work together in a Hadoop implementation to solve big data problems. We will discuss various data sets and project specifications. This module will also cover the Apache Oozie workflow programmer for Hadoop jobs.
  •  The experts in the field of Placement Point Solutions have more than 10 years of experience in their respective technologies and are active for their companies, the highest percentage of IT companies.
  •  Experts are exposed to the real-time implementation of various projects and guide students through their experience.
  •  They have a very fluid social circle in the IT industry to nominate candidates for various positions.
  •  Placement Point Solutions connections with leading multinational companies such as DXC, CTS, Delloite, Accenture, Infosys, etc. And that is the only reason why our students are currently in many global multinationals around the world.
  • Regular exam sessions and interviews are part of the course curriculum.
  • Upon completing the 70% training course, students are prepared for face-to-face interaction in the form of an interview to assess their skills.
  • References for unlimited interviews are provided to applicants until final placement.
  •  Orientation for curriculum development.

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huge knowledge and Hadoop square measure virtually synonyms terms.

With the rise of big data, Hadoop, a framework that specializes in big data operations also became popular.

The framework are often employed by professionals to research huge knowledge and facilitate businesses to create selections.

Note: This question is usually asked in a very huge knowledge interview.

You can go further to answer this question and try to explain the main components of Hadoop.

huge knowledge analysis has become important for the companies.

It helps businesses to differentiate themselves from others and increase the revenue. Through predictive analytics, big data analytics provides businesses customized recommendations and suggestions.

Also, huge knowledge analytics permits businesses to launch new product looking on client desires and preferences.

These factors create businesses earn a lot of revenue, and thus companies are using big data analytics.

Companies could encounter {a significant|a huge|a major} increase of 5-20% in revenue by implementing big knowledge analytics.

Some in style firms those square measure victimization huge knowledge analytics to extend their revenue is – Walmart, LinkedIn, Facebook, Twitter, Bank of America etc.

Followings square measure the 3 steps that square measure followed to deploy an enormous knowledge answer –

i. Data Ingestion

The first step for deploying an enormous knowledge answer is that the knowledge activity i.e.

extraction of data from various sources.

The data supply could also be a CRM like Salesforce, Enterprise Resource Planning System like SAP, RDBMS like MySQL or any other log files, documents, social media feeds etc.

The data are often eaten either through batch jobs or period of time streaming.

The extracted data is then stored in HDFS.

Steps of Deploying Big Data Solution

ii. Data Storage

After data ingestion, the next step is to store the extracted data.

The data either be hold on in HDFS or NoSQL info (i.e. HBase).

The HDFS storage works well for sequential access whereas HBase for random read/write access.

iii. Data Processing

The final step in deploying an enormous knowledge answer is that the processing.

The data is processed through one of the processing frameworks like Spark, MapReduce, Pig, etc.

The two main components of HDFS are-

• NameNode – This is the master node for processing metadata information for data blocks within the HDFS

• DataNode/Slave node – This is the node which acts as slave node to store the data, for processing and use by the NameNode

In addition to serving the client requests, the NameNode executes either of two following roles –

• CheckpointNode – It runs on a different host from the NameNode

• BackupNode- It is a read-only NameNode which contains file system metadata information excluding the block locations

The two main components of YARN are–

• ResourceManager– This component receives processing requests and accordingly allocates to respective NodeManagers depending on processing needs.

• NodeManager– It executes tasks on each single Data Node

Since data analysis has become one of the key parameters of business, hence, enterprises are dealing with massive amount of structured, unstructured and semi-structured data.

Analyzing unstructured knowledge is sort of troublesome wherever Hadoop takes major give up its capabilities of

• Storage

• Processing

• Data collection

Moreover, Hadoop is open source and runs on commodity hardware. Hence it is a cost-benefit solution for businesses.

fsc stands for File System Check. It is a command used by HDFS. This command is used to check inconsistencies and if there is any problem in the file. For example, if there are any missing blocks for a file, HDFS gets notified through this command.

the most variations between NAS (Network-attached storage) and HDFS –

• HDFS runs on a cluster of machines while NAS runs on an individual machine.

Hence, knowledge redundancy may be a common issue in HDFS.

On the contrary, the replication protocol is totally different just in case of NAS.

Thus the chances of data redundancy are much less.

• Data is stored as data blocks in local drives in case of HDFS.

In case of NAS, it’s hold on in dedicated hardware.

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