Master the key concepts of Hadoop and Big data. Join and learn from a Big Data/Hadoop expert.
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Corporate Served- Hadoop is an important Big Data operating system. In fact, it is evaluated for parallel processing utilizing structured and unstructured data, using low hardware costs. Whereas Hadoop’s work is concerned, it's done in batch, not in real-time, duplicating the data through the network respectively.
- By deep-delving into its topic, you will surely get to know how Big Data Hadoop helps in storing a huge mass of data efficiently. So, if you are looking for any sort of professional training, then Croma Campus is the genuinely the best place for you to have a detailed insight of Big Data Hadoop Training in Delhi. Here, you will not only gain theoretical information, but will also receive placement assistance.
Big Data Hadoop Training in Delhi
- Big Data Hadoop is one of the most demanding courses belonging to the IT domain. For beginners, Big Data Hadoop Training in Delhi 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 Delhi, you will come across the 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 this course.
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 hand, you will end up obtaining a decent salary package.
At the initial level, you will earn 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 taking projects as a freelancer, you will make some good additional income also.
- To be precise, 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, choosing this field 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.
Well, withholding a proper accreditation of Big Data Hadoop Developer, you will be offered an excellent salary package right from the beginning of your career.
Getting to know each side of Big Data Hadoop Developer will also push you forward to come up with innovative applications.
Knowing this skill 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 Delhi, 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.
You will know the various features and offerings of this technology by getting in touch with a well-established Big Data Hadoop Training Institute in Delhi.
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 Delhi 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 Delhi will be a suitable move for your career. So, getting associated with a decent Big Data Hadoop Training Institute in Delhi 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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Big Data Hadoop Training
- Croma Campus offers the best Hadoop development Training in Noida with most experienced professionals. Our Instructors are working in Big Data Space and related technologies for years in MNC's.
- We aware of industry needs and we are offering Hadoop development Training in more practical way. Our team of Hadoop trainers offers the in-classroom training with best industry practices.
- We framed our syllabus to match with the real-world requirements for beginner level to advanced level. Our training will be handled in either weekday or weekends programme depends on participants requirements.
- In this program you will learn:
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
- Introduction to Big Data
Overview of Course
What is Big Data
Big Data Analytics
Challenges of Traditional System
Distributed Systems
- Introduction to Hadoop
Components of Hadoop Ecosystem
Commercial Hadoop Distributions
Why Hadoop
Fundamental Concepts in Hadoop
- Security 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
- Initial Setup and Configuration
Deployment Types
Installing Hadoop
Specifying the Hadoop Configuration
Performing Initial HDFS Configuration
Performing Initial YARN and MapReduce Configuration
Hadoop Logging
- HDFS
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
- YARN
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
- Managing and Scheduling Jobs
Managing Running Jobs
Scheduling Hadoop Jobs
Configuring the Fair Scheduler
Impala Query Scheduling
- Apache Sqoop
Apache Sqoop
Sqoop and Its Uses
Sqoop Processing
Sqoop Import Process
Sqoop Connectors
Importing and Exporting Data from MySQL to HDFS
- Apache Sqoop
Apache Flume
Flume Model
Scalability in Flume
Components in Flume’s Architecture
Configuring Flume Components
Ingest Twitter Data
- Getting Data into HDFS
Data Ingestion Overview
Ingesting Data from External Sources with Flume
Ingesting Data from Relational Databases with Sqoop
REST Interfaces
Best Practices for Importing Data
- Apache Kafka
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
- Hadoop Clients
What is a Hadoop Client
Installing and Configuring Hadoop Clients
Installing and Configuring Hue
Hue Authentication and Authorization
- Cluster Maintenance
Checking HDFS Status
Copying Data between Clusters
Adding and Removing Cluster Nodes
Rebalancing the Cluster
Cluster Upgrading
- Cloudera Manager
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
- Cluster Monitoring and Troubleshooting
General System Monitoring
Monitoring Hadoop Clusters
Common Troubleshooting Hadoop Clusters
Common Misconfigurations
- Planning Your Hadoop Cluster
General Planning Considerations
Choosing the Right Hardware
Network Considerations
Configuring Nodes
Planning for Cluster Management
- Advanced Cluster Configuration
Advanced Configuration Parameters
Configuring Hadoop Ports
Explicitly Including and Excluding Hosts
Configuring HDFS for Rack Awareness
Configuring HDFS High Availability
- MapReduce Framework
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
- Apache PIG
Introduction to Pig
Components of Pig
Pig Data Model
Pig Interactive Modes
Pig Operations
Various Relations Performed by Developers
- Apache HIVE
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
- No SQL Databases HBase
NoSQL Databases HBase
NoSQL Introduction
HBase Overview
HBase Architecture
Data Model
Connecting to HBase
HBase Shell
- Functional Programming using Scala
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
- Apache Spark
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
- Data warehouse in Hadoop
Hadoop and the Data Warehouse
Hadoop Differentiators
Data Warehouse Differentiators
When and Where to Use Which
- Augmenting Enterprise Data Warehouse
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
Advance Programming in Hadoop
- Writing MapReduce Program
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
- Introduction to Combiner
The Combiner
MapReduce Example: Word Count
Word Count with Combiner
Specifying a Combiner
Demonstration: Writing and Implementing a Combiner
- Sorting & Searching large data sets
Introduction
Sorting
Sorting as a Speed Test of Hadoop
Shuffle and Sort in MapReduce
Searching
- Performing a secondary sort
Secondary Sort: Motivation
Implementing the Secondary Sort
Secondary Sort: Example
- Indexing data and inverted Index
Indexing
Inverted Index Algorithm
Inverted Index: Data Flow
Aside: Word Count
- Term Frequency - Inverse Document Frequency (TF- IDF)
Term Frequency Inverse Document Frequency (TF-IDF)
TF-IDF: Motivation
TF-IDF: Data Mining Example
TF-IDF Formally Defined
Computing TF-IDF
- Calculating Word co- occurrences
Word Co-Occurrence: Motivation
Word Co-Occurrence: Algorithm
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FAQ's
This specific course will hardly take 45-50 days to completely understand its functions.
Yes, they are hugely in demand. So, choosing this field will be beneficial for your career.
Yes, there are two ways of training, online/offline.
Yes, along with study material, you will also get access to our LMS Portal which will help you review your sessions.
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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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