Master the key concepts of Hadoop and Big data. Join and learn from a Big Data/Hadoop expert.
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Corporate Served- Big Data Hadoop is a legit open-source framework that is specifically utilized to accumulate and process large datasets ranging in various sizes from gigabytes to petabytes of data. Instead of using one large computer to accumulate and proceed with the data, Hadoop offers you clustering numerous computers to examine massive datasets in parallel more quickly. Well, presently Big Data Hadoop Training in Noida is one sort of a course that you might need to know if you are anytime planning to construct your career in the development field.
- By deep-delving into its topic, you will come across how it helps in storing a huge mass of data. And to get started with its professional course, you must get in touch with Croma Campus as it offers the best Big Data Hadoop Training in Noida. And this training perhaps will help you in building your career in this field.
Big Data Hadoop Training in Noida
- Big Data Hadoop is one of the most demanding courses belonging to the IT domain. For beginners, Big Data Hadoop Training in Noida might seem a bit difficult one's, but with adequate guidance, you will surely end up understanding every part of this course. By getting in touch with Big Data Hadoop Training Institute in Noida, you will get the chance to know its exact topics and sub-topics in an explained manner.
At the initial level of the course, our trainers will help you know its basic fundamentals.
Further, you will also receive sessions concerning Hadoop Architecture Distributed Storage (HDFS) and YARN, What Is HDFS, Need for HDFS, Need for HDFS, etc.
Our trainers will also help you know the whole Data Replication Topology, HDFS Command Line, command line, Data Ingestion into Big Data Systems and ETL, distributed processing, Hive SQL over Hadoop MapReduce, basics of Functional Programming and Scala, etc.
Here, you will also get a chance to imbibe new skills concerning Big Data Hadoop Training in Noida.
In a nutshell, we can say, you will end up imbibing every detail of this course in a detailed manner.
- Whereas salary package is concerned, then it's eventually one of the well-paid fields. By obtaining a legitimate accreditation of Big Data Hadoop Training in Noida in hand, you will end up obtaining a decent salary package.
At the initial level, you will make around Rs. 3 Lakh to Rs. 3.5 Lakh, which is quite good for freshers.
Likewise, an experienced Big Data Hadoop Developer earns Rs. 11.5 Lakh annually.
Moreover, by obtaining more work experience along with the latest skills, and trends your salary structure will expand.
By working as a freelancer, you will make some good additional income also.
- Well, Hadoop is one sort of technology that offers numerous opportunities to build and grow your career. In fact, Hadoop is genuinely one of the most valuable skills to imbibe today that can help you obtain a rewarding job. If your interest lies in this direction, opting Big Data Hadoop Training in Noida will be suitable for your career in numerous ways.
By obtaining for its legit training from a reputed educational foundation, you will turn into a knowledgeable Big Data Hadoop Developer.
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Getting to know each side of Big Data Hadoop will also push you forward to come up with innovative applications.
Knowing about Big Data Hadoop Training in Noida will eventually enhance your resume.
You will always have numerous jobs offers in hand.
- A skilled Big Data Hadoop Developer is responsible for executing a lot of tasks, and to smoothly execute those tasks, you will have to imbibe those skills adequately as well. By getting in touch with a decent Big Data Hadoop Training Institute in Noida, you will get the chance to understand each role in quite an explained manner.
- Some of the major roles are mentioned below.
Firstly, you will have to build, imply and maintain Big Data solutions.
In fact, you will also have to execute database modeling and development, data mining, and warehousing.
You will also have to design and deploy data platforms across numerous domains assuring operability.
Our trainers will also help you to experience the Hadoop ecosystem (HDFS, MapReduce, Oozie, Hive, Impala, Spark, Kerberos, KAFKA, etc.).
At the same time, you will also have to transform data for meaningful analyses and enhance data efficiency.
Often you will have to execute the testing scripts and analyze the results.
- If you will deep delve into its topic, then you will find various reasons to get started with Big Data Hadoop Training in Delhi. One of the significant one's its ability to handle huge data.
By getting started with this specific course, you will end up strengthening your base knowledge.
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You will also know about building a new sort of application and implying some latest features.
You will end up obtaining some untold, and hidden facts from the Big Data Hadoop Training in Noida respectively.
- At the moment, you will find Big Data Hadoop Developers extensively in demand, and yet the grant is low. If you are also planning to construct your career in this field, getting started with Big Data Hadoop Training in Noida will be a suitable move for your career. So, getting associated with a decent Big Data Hadoop Training Institute in Noida will be beneficial for you to secure a higher position.
By joining Croma Campus, you will get the opportunity to get placed in your choice of companies post enrolling with the Big Data Hadoop course.
Our trainers will also help you in building an impressive resume.
They will also suggest you some effective tips to pass the interviews.
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Course Duration
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CURRICULUM & PROJECTS
Big Data Hadoop Training
- Introduction to Big Data & Hadoop
- HDFS
- YARN
- Managing and Scheduling Jobs
- Apache Sqoop
- Apache Flume
- Getting Data into HDFS
- Apache Kafka
- Hadoop Clients
- Cluster Maintenance
- Cloudera Manager
- Cluster Monitoring and Troubleshooting
- Planning Your Hadoop Cluster
- Advanced Cluster Configuration
- MapReduce Framework
- Apache PIG
- Apache HIVE
- No SQL Databases HBase
- Functional Programming using Scala
- Apache Spark
- Hadoop Datawarehouse
- Writing MapReduce Program
- Introduction to Combiner
- Problem-solving with MapReduce
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- Overview of Course
- What is Big Data
- Big Data Analytics
- Challenges of Traditional System
- Distributed Systems
- Components of Hadoop Ecosystem
- Commercial Hadoop Distributions
- Why Hadoop
- Fundamental Concepts in Hadoop
- Why Hadoop Security Is Important
- Hadoop’s Security System Concepts
- What Kerberos Is and How it Works
- Securing a Hadoop Cluster with Kerberos
- Deployment Types
- Installing Hadoop
- Specifying the Hadoop Configuration
- Performing Initial HDFS Configuration
- Performing Initial YARN and MapReduce Configuration
- Hadoop Logging
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- What is HDFS
- Need for HDFS
- Regular File System vs HDFS
- Characteristics of HDFS
- HDFS Architecture and Components
- High Availability Cluster Implementations
- HDFS Component File System Namespace
- Data Block Split
- Data Replication Topology
- HDFS Command Line
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- Yarn Introduction
- Yarn Use Case
- Yarn and its Architecture
- Resource Manager
- How Resource Manager Operates
- Application Master
- How Yarn Runs an Application
- Tools for Yarn Developers
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- Managing Running Jobs
- Scheduling Hadoop Jobs
- Configuring the Fair Scheduler
- Impala Query Scheduling
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- Apache Sqoop
- Sqoop and Its Uses
- Sqoop Processing
- Sqoop Import Process
- Sqoop Connectors
- Importing and Exporting Data from MySQL to HDFS
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- Apache Flume
- Flume Model
- Scalability in Flume
- Components in Flume’s Architecture
- Configuring Flume Components
- Ingest Twitter Data
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- Data Ingestion Overview
- Ingesting Data from External Sources with Flume
- Ingesting Data from Relational Databases with Sqoop
- REST Interfaces
- Best Practices for Importing Data
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- Apache Kafka
- Aggregating User Activity Using Kafka
- Kafka Data Model
- Partitions
- Apache Kafka Architecture
- Setup Kafka Cluster
- Producer Side API Example
- Consumer Side API
- Consumer Side API Example
- Kafka Connect
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- What is a Hadoop Client
- Installing and Configuring Hadoop Clients
- Installing and Configuring Hue
- Hue Authentication and Authorization
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- Checking HDFS Status
- Copying Data between Clusters
- Adding and Removing Cluster Nodes
- Rebalancing the Cluster
- Cluster Upgrading
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- The Motivation for Cloudera Manager
- Cloudera Manager Features
- Express and Enterprise Versions
- Cloudera Manager Topology
- Installing Cloudera Manager
- Installing Hadoop Using Cloudera Manager
- Performing Basic Administration Tasks using Cloudera Manager
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- General System Monitoring
- Monitoring Hadoop Clusters
- Common Troubleshooting Hadoop Clusters
- Common Misconfigurations
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- General Planning Considerations
- Choosing the Right Hardware
- Network Considerations
- Configuring Nodes
- Planning for Cluster Management
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- Advanced Configuration Parameters
- Configuring Hadoop Ports
- Explicitly Including and Excluding Hosts
- Configuring HDFS for Rack Awareness
- Configuring HDFS High Availability
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- What is MapReduce
- Basic MapReduce Concepts
- Distributed Processing in MapReduce
- Word Count Example
- Map Execution Phases
- Map Execution Distributed Two Node Environment
- MapReduce Jobs
- Hadoop MapReduce Job Work Interaction
- Setting Up the Environment for MapReduce Development
- Set of Classes
- Creating a New Project
- Advanced MapReduce
- Data Types in Hadoop
- Output formats in MapReduce
- Using Distributed Cache
- Joins in MapReduce
- Replicated Join
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- Introduction to Pig
- Components of Pig
- Pig Data Model
- Pig Interactive Modes
- Pig Operations
- Various Relations Performed by Developers
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- Introduction to Apache Hive
- Hive SQL over Hadoop MapReduce
- Hive Architecture
- Interfaces to Run Hive Queries
- Running Beeline from Command Line
- Hive Meta Store
- Hive DDL and DML
- Creating New Table
- Data Types
- Validation of Data
- File Format Types
- Data Serialization
- Hive Table and Avro Schema
- Hive Optimization Partitioning Bucketing and Sampling
- Non-Partitioned Table
- Data Insertion
- Dynamic Partitioning in Hive
- Bucketing
- What Do Buckets Do
- Hive Analytics UDF and UDAF
- Other Functions of Hive
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- NoSQL Databases HBase
- NoSQL Introduction
- HBase Overview
- HBase Architecture
- Data Model
- Connecting to HBase
- HBase Shell
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- Basics of Functional Programming and Scala
- Introduction to Scala
- Scala Installation
- Functional Programming
- Programming with Scala
- Basic Literals and Arithmetic Programming
- Logical Operators
- Type Inference Classes Objects and Functions in Scala
- Type Inference Functions Anonymous Function and Class
- Collections
- Types of Collections
- Operations on List
- Scala REPL
- Features of Scala REPL
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- Apache Spark Next-Generation Big Data Framework
- History of Spark
- Limitations of MapReduce in Hadoop
- Introduction to Apache Spark
- Components of Spark
- Application of In-memory Processing
- Hadoop Ecosystem vs Spark
- Advantages of Spark
- Spark Architecture
- Spark Cluster in Real World
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- Hadoop and the Data Warehouse
- Hadoop Differentiators
- Data Warehouse Differentiators
- When and Where to Use Which
- Introduction
- RDBMS Strengths
- RDBMS Weaknesses
- Typical RDBMS Scenario
- OLAP Database Limitations
- Using Hadoop to Augment Existing Databases
- Benefits of Hadoop
- Hadoop Trade-offs
- Advance Programming in Hadoop
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- A Sample MapReduce Program: Introduction
- Map Reduce: List Processing
- MapReduce Data Flow
- The MapReduce Flow: Introduction
- Basic MapReduce API Concepts
- Putting Mapper & Reducer together in MapReduce
- Our MapReduce Program: Word Count
- Getting Data to the Mapper
- Keys and Values are Objects
- What is Writable Comparable
- Writing MapReduce application in Java
- The Driver
- The Driver: Complete Code
- The Driver: Import Statements
- The Driver: Main Code
- The Driver Class: Main Method
- Sanity Checking the Job’s Invocation
- Configuring the Job with Job Conf
- Creating a New Job Conf Object
- Naming the Job
- Specifying Input and Output Directories
- Specifying the Input Format
- Determining Which Files to Read
- Specifying Final Output with Output Format
- Specify the Classes for Mapper and Reducer
- Specify the Intermediate Data Types
- Specify the Final Output Data Types
- Running the Job
- Reprise: Driver Code
- The Mapper
- The Mapper: Complete Code
- The Mapper: import Statements
- The Mapper: Main Code
- The Map Method
- The map Method: Processing the Line
- Reprise: The Map Method
- The Reducer
- The Reducer: Complete Code
- The Reducer: Import Statements
- The Reducer: Main Code
- The reduce Method
- Processing the Values
- Writing the Final Output
- Reprise: The Reduce Method
- Speeding up Hadoop development by using Eclipse
- Integrated Development Environments
- Using Eclipse
- Writing a MapReduce program
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- The Combiner
- MapReduce Example: Word Count
- Word Count with Combiner
- Specifying a Combiner
- Demonstration: Writing and Implementing a Combiner
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- Introduction
- Sorting
- Sorting as a Speed Test of Hadoop
- Shuffle and Sort in MapReduce
- Searching
- Secondary Sort: Motivation
- Implementing the Secondary Sort
- Secondary Sort: Example
- Indexing
- Inverted Index Algorithm
- Inverted Index: Data Flow
- Aside: Word Count
- Term Frequency Inverse Document Frequency (TF-IDF)
- TF-IDF: Motivation
- TF-IDF: Data Mining Example
- TF-IDF Formally Defined
- Computing TF-IDF
- Word Co-Occurrence: Motivation
- Word Co-Occurrence: Algorithm
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Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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FAQ's
Our training programs will enable professionals to secure placements in MNCs. Croma Campus is one of the most recommended Big Data Hadoop Training Institute in Noida that offers hands on practical knowledge / practical implementation.
The need of Its professionals is increasing so Hadoop is one of the better choices for career growth and enough income Apache Hadoop provides you the better package.
Join an institute who provides the best trainer and training as well. Like Croma Campus, training programs are based on current industry standards.
Industry standard projects are included in our training programs and Live Project based training with trainers having 5 to 15 years of Industry Experience.
There are two ways of payment, one is digital payment like NEFT, GOOGLE PAY or PATYM and the second one CASH.
Any Body who Graduated, Post Graduated, Fresher or Professionals who want to quick start their career in BIG DATA HADOOP Field.
You just need to select the best institute wisely So that you can learn the latest Module in a particular course.
The duration is depending on your selection of training mode like classroom training, online training or fast track training. For fee structure you can contact us at: 9711526942.
The best way to obtain its information is to get in touch with an educational foundation.
Yes, it's hugely in demand, and getting acquainted with its information will help you grow in this field.
A skilled Big Data Hadoop Developer earns around ₹ 13.5 Lakhs to ₹ 15.5 Lakhs based on their experience.
For details information & FREE demo class call us on +91-9711526942 or write to us info@cromacampus.com
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WHAT OUR ALUMNI SAYS ABOUT US
Sachin Tyagi
Big Data Hadoop
Thanks for making this wonderful platform available. I would love to encourage more people to join Croma Learning Campus to fill the gap for their career needs. I took Big Data Hadoop Training from Croma and I must say that course content is just the great and well-structured as per the certificatio Read more...
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