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Master the key concepts of Hadoop and Big data. Join and learn from a Big Data/Hadoop expert.
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30 Hrs.Live Project
2 ProjectCertification Pass
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Corporate Served- Big Data Hadoop is one of the most popular open-source utility software platforms that help store a huge amount of data and also has a massive processing power in it. It can multitask, i.e., it can handle unlimited data simultaneously.
- Things you will learn:
- If you want to enhance your career potential, then Big Data Hadoop could be an ideal choice. so, what are you waiting for Simply enrol for the best Big Data Hadoop course without breaking your bank.
In-depth knowledge of Big Data Hadoop features like HDFS, YARN, Hive, Map Reduce, Pig, Spark.
Get the opportunity to work on real-life industry cases.
Master the desired skill with or without any technical background.
Big Data Hadoop is the most in-demand skills at present.
Popular Big Data job titles Developer, Administrator, Data Analyst, Tester, and Solution Architect.
Big Data Hadoop Training
- With our Big Data Hadoop training and certification program online, you are likely to learn a multitude of skills and implement them at work. The main objective of our training program is:
Learn YARN, Map Reduce, and become an expert in writing apps using the mentioned tools.
To impart an in-depth knowledge of HDFS, Sqoop, Pig, Hive, Oozie, shell scripting, Spark, Flume, Zookeeper online.
You will understand Big Data Hadoop cluster and learn Big Data Hadoop analytics as well.
Configure various ETL tools and know-how to set the pseudo nodes.
Work on real-life projects and hands-on assignments to facilitate the learning process.
- According to the recent survey conducted, the estimated salary of a Big Data Hadoop Developer ranges between $119,250 to $168,250 per year. when you choose us for your Big Data Hadoop training, you are likely to get higher salary packages, unlike other IT departments.
- So, acknowledge the coming opportunity, focus on sharpening the required skills and get ready to grasp the chance when it comes.
Big Data Analytics is proven to be the most preferred software or data analytic tool.
These tools are considered to increase the efficiency of any organization.
Organizations use Big Data Analytic tools to get better insights into their sales.
Big data is also helping businesses to promote marketing on social media to meet their goals.
There is an acute shortage of skilled analysts and managers in the coming years.
- Our Big Data Hadoop training is backed by accredited experts along with the facility of real-time sessions. After the completion of your course, you will get significant career growth;
Our Big Data Hadoop training lessons include the perfect combination of theoretical as well as practical components to make the training conducive for learning.
After completing the basic level Big Data Hadoop training, learners are qualified for the next level and thus are prepared for it.
In Big Data Hadoop training, individuals are provided with the basic concepts to recap and sharpen their skills.
Get Big Data Hadoop Live sessions by providing recordings of the live class, study material, ppt, projects, etc.
Having proper training and the right certification can reap you a perfect result.
- Big Data Hadoop is one of the first frameworks for extracting and transforming huge volumes of data and hence is widely used making it the most popular big data framework. The best part of the Big Data Hadoop system is its flexibility to control the overall system and store data in HDFS and supports data compression. Data can be installed using tools such as Map Reduce, Pig, and Hive.
There is a great demand for Big Data Managers and it will increase with time.
It is predicted by 2022, the Big Data Hadoop market will reach $99.31.
In the US, there is a shortage of Big Data Hadoop skilled experts up to 1.6 million.
Almost every top MNCs are adopting the Big Data Hadoop system.
Get the most outstanding learning experience from Coma Campus.
- Here are some job roles and responsibilities covered as a part of the Big Data Hadoop training program:
You should know how to develop, implement and maintain Big Data solutions.
You should conduct database modelling and development, data mining, and warehousing.
You must be well-versed in designing and deploying data platforms.
You should know to improve data efficiency, reliability & quality, create data enrichment, build high performance, etc.
You must have substantial knowledge in MongoDB, RDBMS, Spark core, HBase or Cassandra, Pig, Yarn, SQL, etc.
You must take a pivotal part in the development and deployment of innovative big data platforms.
- One of the remarkable flairs of Big Data Hadoop is the ease with which it can be scaled up. The capability of processing a large amount of data in very little time makes it even more appealing.
- Many top MNCs are on the lookout for professional Big Data and Analytics for their businesses.
You can clear any certification exam successfully.
You will be offered a certificate that is accepted worldwide.
Take up a lucrative and promising career ahead.
Increase your worth in today’s tech-driven landscape.
Our certification will let you bag higher salary packages.
- Training is vital to future proof your employability but getting the right training will cement your position. Our Big Data Hadoop certification training may help you learn Data Hadoop concepts without any difficulty.
Help you clear any certification exam successfully.
After the course, you will be offered a certificate thereafter.
You can establish yourself as a certified Developer, Administrator, Data Analyst, Tester, or Solution Architect.
Register now and ramp up your career and own it.
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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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Course Duration
30 Hrs.
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