Big Data and Hadoop

In recent years, enterprise functioning is becoming more and more dependent on the proper detection and implementation of the customer-preference data. To use the data that we generate on a daily basis, skills, and instruments that handle complex data and extract relevant information from it are in gradually increasing demand.

Big data is such an important and specialized part of data science which deals with the collection, collation, and analysis of huge amounts of complex data. Consequently, the institute that teaches the course to manage and provide engineering service for Big Data is also in demand.

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Course Duration

30 Days

Course Fee

INR 25000

Course Module

Understanding Big Data and Hadoop

  • Introduction to Big Data & Big Data Challenges
  • Limitations & Solutions of Big Data Architecture
  • Hadoop & its Features
  • Hadoop Ecosystem
  • Hadoop 2.x Core Components
  • Hadoop Storage: HDFS (Hadoop Distributed File System)
  • Hadoop Processing: MapReduce Framework
  • Different Hadoop Distributions

Hadoop Architecture and HDFS

  • Hadoop 2.x Cluster Architecture
  • Federation and High Availability Architecture
  • Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands
  • Hadoop 2.x Configuration Files
  • Single Node Cluster & Multi-Node Cluster set up
  • Basic Hadoop Administration

Hadoop MapReduce Framework

  • Traditional way vs MapReduce way
  • Why MapReduce
  • YARN Components
  • YARN Architecture
  • YARN MapReduce Application Execution Flow
  • YARN Workflow
  • Anatomy of MapReduce Program
  • Input Splits, Relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner
  • Demo of Health Care Dataset
  • Demo of Weather Dataset

Advanced Hadoop MapReduce

  • Counters
  • Distributed Cache
  • MRunit
  • Reduce Join
  • Custom Input Format
  • Sequence Input Format
  • XML file Parsing using MapReduce

Apache Pig

  • Introduction to Apache Pig
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset

Apache Hive

  • Introduction to Apache Hive
  • Hive vs Pig
  • Hive Architecture and Components
  • Hive Metastore
  • Limitations of Hive
  • Comparison with Traditional Database
  • Hive Data Types and Data Models
  • Hive Partition
  • Hive Bucketing
  • Hive Tables (Managed Tables and External Tables)
  • Importing Data
  • Querying Data & Managing Outputs
  • Hive Script & Hive UDF
  • Retail use case in Hive
  • Hive Demo on Healthcare Dataset

Advanced Apache Hive and HBase

  • Hive QL: Joining Tables, Dynamic Partitioning
  • Custom MapReduce Scripts
  • Hive Indexes and views
  • Hive Query Optimizers
  • Hive Thrift Server
  • Hive UDF
  • Apache HBase: Introduction to NoSQL Databases and HBase
  • HBase v/s RDBMS
  • HBase Components
  • HBase Architecture
  • HBase Run Modes
  • HBase Configuration
  • HBase Cluster Deployment

Advanced Apache HBase

  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model
  • Zookeeper Service
  • HBase Bulk Loading
  • Getting and Inserting Data
  • HBase Filters

Processing Distributed Data with Apache Spark

  • What is Spark
  • Spark Ecosystem
  • Spark Components
  • What is Scala
  • Why Scala
  • SparkContext
  • Spark RDD

Oozie and Hadoop Project

  • Oozie
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Talend Integration

Eligibility

There are no hard and fast rules for the position; anyone who has an interest in making a career in digital marketing and preferably has a background in science, mathematics, or engineering can enroll their name in this course. Knowledge of coding and programming is often essential for this course. A section head, CEO, and a programmer can also take the course to make a name in the field of digital marketing.

Why Choose Us?

Learning the intricate steps of handling data is an important part of the digital marketing process. We, at INAUSCO Digital, provide courses that cover all the necessary aspects. Our courses are designed to give an idea of the use and implementation of Big Data into the information coming from the client-side and take the important points to make an informed prediction about the changes in the revenue generation of the company. We provide real-time training on which to data to extract, warehouse the important parts for future use, and give the skills to build immense data pools and highly scalable and fault-tolerant disseminated systems. Give us a call to know more and get the chance of a lifetime.

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