- 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.
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At the initial level, you will make around Rs. 3 Lakh to Rs. 3.5 Lakh, which is quite good for freshers.
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Firstly, you will have to build, imply and maintain Big Data solutions.
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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.).
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Often you will have to execute the testing scripts and analyze the results.
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Plenary for Big Data Hadoop Training
Track | Week Days | Weekends | Fast Track |
---|---|---|---|
Course Duration | 40-45 Days | 7 Weekends | 8 Days |
Hours | 1 Hrs. Per Day | 2 Hrs. Per Day | 6+ Hrs. Per Day |
Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
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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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Mock Interviews
Projects
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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
Address: - G-21, Sector-03, Noida (201301)
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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 cont
Read More...Sachin Tyagi
Big Data Hadoop