- Big Data Hadoop helps in storing huge data which is beyond storage capacity and processing power. It also assures managing virtually limitless concurrent jobs. Croma Campus offers the best Big Data Hadoop Training in Gurgaon to students looking to gain knowledge in the discipline and get a secured job in a leading MNC or a corporate giant. In fact, you will find Big Data Hadoop Training being especially designed to offer you an in-depth knowledge of the framework utilizing Hadoop and Spark. Here, you will receive a hands-on Hadoop training along with real industry-based projects using Integrated Lab.
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Right at the beginning of the course, our trainers will help you know its basic fundamentals.
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You will get the chance to analyze Hadoop features like HDFS, YARN, Hive, MapReduce, Pig, Spark, HBase, Sqoop, Flume, Oozie, Hive, etc.
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By taking projects as a freelancer, you will make some good additional income also.
- To be precise, Hadoop is one sort of field that provides various opportunities to develop and grow your career. Hadoop is genuinely one of the most valuable skills to learn today that can assist you in acquiring a rewarding job. If your interest lies in this direction, approaching this direction will be suitable for your career in numerous ways.
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- In recent times, Hadoop Big Data has genuinely become a mandatory skill as industries are expanding, the aim is to assemble information and find hidden facts behind the data. To be precise, Data defines how industries can improvise their activity and affair. A large number of industries are evolving across the data, and there is a large amount of data that has been gathered and examined through various processes with various tools. So, if your interest lies in this process, enrolling with Big Data Hadoop Training in Gurgaon will eventually be a good decision for your career.
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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
- 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
- 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 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 Running Jobs
- Scheduling Hadoop Jobs
- Configuring the Fair Scheduler
- Impala Query Scheduling
- Apache Sqoop
- Sqoop and Its Uses
- Sqoop Processing
- Sqoop Import Process
- Sqoop Connectors
- Importing and Exporting Data from MySQL to HDFS
- Apache Flume
- Flume Model
- Scalability in Flume
- Components in Flume’s Architecture
- Configuring Flume Components
- Ingest Twitter Data
- 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
- 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
- What is a Hadoop Client
- Installing and Configuring Hadoop Clients
- Installing and Configuring Hue
- Hue Authentication and Authorization
- Checking HDFS Status
- Copying Data between Clusters
- Adding and Removing Cluster Nodes
- Rebalancing the Cluster
- Cluster Upgrading
- 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
- General System Monitoring
- Monitoring Hadoop Clusters
- Common Troubleshooting Hadoop Clusters
- Common Misconfigurations
- General Planning Considerations
- Choosing the Right Hardware
- Network Considerations
- Configuring Nodes
- Planning for Cluster Management
- Advanced Configuration Parameters
- Configuring Hadoop Ports
- Explicitly Including and Excluding Hosts
- Configuring HDFS for Rack Awareness
- Configuring HDFS High Availability
- 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
- Introduction to Pig
- Components of Pig
- Pig Data Model
- Pig Interactive Modes
- Pig Operations
- Various Relations Performed by Developers
- 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
- NoSQL Databases HBase
- NoSQL Introduction
- HBase Overview
- HBase Architecture
- Data Model
- Connecting to HBase
- HBase Shell
- 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 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
- 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
- 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
- The Combiner
- MapReduce Example: Word Count
- Word Count with Combiner
- Specifying a Combiner
- Demonstration: Writing and Implementing a Combiner
- 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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