Data Engineering with Hadoop

BY
Edupristine

This course equips you with industry-specific skills and Big Data tools like Hadoop, MapReduce, HBase, HDFS, Pig and more.

Mode

Online

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based

Course overview

One can hardly talk about Big Data without mentioning Hadoop. The explosive growth witnessed in the data sciences and engineering industry has created ample job vacancies. EduPristine’s Data Engineering with Hadoop programme is the most comprehensive and intensive training in data engineering and trains the participants in the skill-set required to thrive in this industry.

This Data Engineering with Hadoop course is targeted towards IT and analytics professionals, who are trained by EduPristine’s subject matter experts in various Big Data tools and given hands-on experiential training. Post the course completion; candidates also get end-to-end career support and job assistance. Candidates learn about the Big Data tools, including MapReduce, Apache HBase, HDFS, Apache Spark, Hive, Sqoop, Pig, Scala, Apache Oozie, among others. 

The curriculum is an amalgamation of theoretical and practical learning and provides you with a data engineering certification at the end of the course. Participants are taught by our expert instructors and provided guidance every step of the way, from installing the software to learning its niche functions and mastering it. Data Engineering with Hadoop certification training also requires you to complete a live project.

The highlights

  • Sixty hours of instructor-led training
  • Big Data tools
  • Real-life case studies
  • Hadoop ecosystem tools
  • Experienced trainers
  • Self-paced education
  • Dedicated discussion forums
  • Real-time cluster configuration
  • Access to LMS 
  • Real-time projects
  • Case simulations
  • After Course Engagement (ACE)
  • Comprehensive study notes
  • Data Engineering Certification
  • Career services

Program offerings

  • Post session case studies
  • Access to lms
  • Comprehensive study notes
  • Job update
  • Interview preparations
  • Live project
  • Customized training options
  • Expert level curriculum
  • Big data tool
  • Hadoop ecosystem tool

Course and certificate fees

certificate availability

Yes

certificate providing authority

Edupristine

Who it is for

This Data Engineering with Hadoop programme can be undertaken by IT professionals and analysts who wish to enhance their career growth by getting a certification in this ever-expanding field. 

Eligibility criteria

There is no minimum eligibility criteria or requirements to become eligible for this course. However, this is a niche specialization in the Big Data industry, and thus candidates are expected to have a basic knowledge of some programming languages and how they function. 

What you will learn

Sql knowledge

After successful completion of the course in Data Engineering with Hadoop, candidates will be adept in the following:

  • Comprehensive understanding of Hadoop and its components:  Hadoop Distributed File System (HDFS), Hadoop MapReduce, and Hadoop YARN
  • Installing Spark components like MySQL, Hive, Pig, SparkR, and more
  • Using more than ten big data tools to analyze large datasets
  • Familiarity with concepts of data setup, data flow, and data integrity in software

The syllabus

Linux Basics

File Handling
Text Processing
Process Management
System Administration
Archival-Network-File Systems
Advanced Commands

Core Java and Maven Project (Recordings)

Introduction - Oops concept
  • Object
  • Class
  • Inheritance
  • Polymorphism
  • Abstraction
  • Encapsulation
String
  • Concept of String
  • Immutable String
  • String Comparison
  • String Concatenation
  • Concept of Substring
  • String class methods and its usage
  • String Buffer class - String Builder class)
Exception Handling
  • (try - throw -catch
  • Advance( throws - finally)
Input and Output (I/O) function
Collections
  • Map(hash map - tree map -linked hash map - multi key map)
  • List (array list - linked list)
  • Set(Hash set - tree set)
JDBC Connectivity

SQL Basics (Recordings)

SQL Intro
Data Types
DDL
  • Create/Alter Table
  • Create Alter VIEWS
  • INDEX
DML
  • Insert
  • Update
  • Delete
Query
  • Select
  • Like
  • In
  • Between
  • Distinct
  • Where
  • And/or
  • Orderby
  • Null
  • Not Null
Joins
  • Inner
  • Left
  • Right
  • Full
  • Union
Functions
  • Avg
  • Count
  • First
  • Min
  • Max
Group By – Having

Installing Hadoop and Spark Components on local machine.

Linux
Java
Scala
Python
PIG
HIVE
HBASE
SPARK
Anaconda
MongoDB

Introduction to Big Data Hadoop

Big Data (What, Why)
5 V’s
Overview of Hadoop Ecosystem
Role of Hadoop in Big data
Who is using Hadoop
Current Scenario in Hadoop Ecosystem
Installation - Configuration
Use Cases of Hadoop

Hadoop Distributed File System (HDFS)

Concepts - Architecture
Data Flow (File Read , File Write)
Fault Tolerance
Shell Commands
Data Integrity
Hadoop Demons
  • NameNode (Name Node Failure and Recovery)
  • DataNode
  • Role of Secondary NameNode
  • Limitations in Hadoop-1.0   
  • Hadoop-2.0 HDFS Federation
  • High Availability in HDFS
  • Other Improvements in HDFS2
  • Commands (fsck , dfsadmin)
  • Schedulers in YARN
  • Rack Awareness Policy
  • Balancing
  • Compression Codecs

MapReduce

Theory
MR 2 – Limitations in MR1
Data Flow (Map – Shuffle - Reduce)
MapRed vs. MapReduce APIs
Programming
  • Mapper
  • Reducer
  • Combiner
  • Partitioner  
Writable
InputFormat
  • TextInputFormat
  • KeyValueInputFormat
  • NLINEInputFormat
  • MultiInputFormat
Outputformat
  • MultiOutPutFormat
  • TextOutPutFormat
Inherent Failure Handling using Speculative Execution
Architecture of YARN
MapReduce Job Flow in YARN

Deep Dive in MapReduce Programming

Counters (Built In and Custom)
Uber Mode
Distributed Cache
Joins(MapSide, Reduce Side)
ToolRunner

Hive Query language

Architecture – Installation –Configuration
Hive Server(JDBC,ODBC, Thrift)
Metastore
Hive vs. RDBMS
Database
Tables
  • External Table
  • Managed Table
DDL
Virtual Column in Hive
JOINs
  • Inner Join
  • Left Outer join
  • Right Outer join
  • Full join
VIEWs
Index in Hive
  • BITMAP
  • COMPACT
DML
Hive Functions
UDF
Partitioning
  • Static Partition
  • Dynamic Partition
Bucketing
  • Bucketing benefits
  • TABLESAMPLE
SerDe
  • LazySimpleSerDe
  • OrcSerde
  • ColumnarSerDe
  • ParquetHiveSerDe
  • AvroSerDe
Hive-Hbase Integration
File Formats
  • RCFile
  • ORCFile
  • Parquet
  • AVRO
Choosing right storage format and compression in Hive
Performance Tuning

Pig Latin

Architecture - Installation
Hive vs. Pig
Pig Execution modes
  • LOCAL
  • MR
Pig Latin Syntax
  • Load
  • Store
Data Types
  • Tuple
  • Bag
  • Map
File System Commands in PIG
PIG Relational Operators
GROUP BY
COGROUP BY
JOIN
  • Skewed
  • Replicated
  • Merge
Functions
  • Eval
  • String
Pig Server with HCatalog
Macros
UDFs
PIG Parameter substitution
  • Run Vs Exec
Performance

HBASE

Introduction to NoSQL - Classification of NoSQL
CAP Theorem
Hbase and RDBMS
HBASE Architecture - Installation
  • COLUMN FAMILY
  • ROW KEY
  • VERSIONING in HBase
  • Read/Write Path
  • WAL
  • Configuration - Role of Zookeeper
HBase Shell
  • Create
  • PUT
  • GET
  • SCAN
  • DELETE/DELETE ALL
  • DISABLE and DROP Tables
  • Truncate
Java Based APIs (Scan, Get, other advanced APIs )
HBASE integration with Hive
HBASE integration with PIG
Backup and Disaster Recovery
Performance

SQOOP - SQL to Hadoop

Architecture , Installation,
Import
  • Data Setup in MYSQL
  • Import Data from MySQL to HDFS using SQOOP
  • Target-Dir vs. Warehouse-Dir
  • Loading Password from configuration File (HDFS and UNIX config file)
  • Storing Data in advance File formats (Parquet, AVRO etc.)
  • Compressing Imported Data to BZIP and Snappy etc. using Codec
  • SQOOP Import using –QUERY and $Condition
  • Incremental Imports
  • Append
  • Lastmodified
  • Import All tables
  • Hive-Import
Hbase Import
Codegen
SQOOP Jobs
Export

SPARK with SCALA

Introduction to SCALA programming.
Core Spark Concepts
RDDs and its operations
Transformations
Actions
Accumulator
Broadcast
Persist RDD
Introduction to Spark SQL

Live Project

Customer 360 degree and Retail Analysis – Live projects
  • Using all Hadoop Components like PIG, HIVE, SQOOP, HBASE
  • Scheduling in OOZIE using Apache HUE

How it helps

By pursuing Online Data Engineering with Hadoop Training, participants can reach new heights in their careers as they get to learn various big data tools, all in one place. EduPristine not only provides quality classroom sessions but also assists the participants with career guidance, interview preparation, and gives regular job updates.

After completion of this course, candidates can enter the highly sought-after data engineering sector, where the indicative package for a beginner with 0-1 years experience is Rs. 4 lakh and only goes up from there. This is a highly rewarding field. With the data engineering certification from EduPristine, candidates can get their expertise in working with the Hadoop ecosystem validated.

FAQs

What are the future opportunities in Big Data?

Big Data is still relatively new, and its demand is increasing quickly. Big data and Business Analytics market size are likely to shoot up to $512.04 billion by 2026. In India, this sector was worth $27 billion in 2019. Thus, this is the best time to step into this emerging field and create a firm footing.

Why should I pursue the Data Engineering with Hadoop course from EduPristine?

EduPristine combines classroom training with post-session case studies, live projects, real-time cluster configuration, and industry-relevant content to provide an all-round approach, in addition to theoretical training as well. 

Where can I work after this course?

EduPristine’s students work in companies like Tata, Deloitte, HDFC Bank, Infosys, Cognizant, and FedEx, to name a few. 

What is the indicative salary of data engineers in India?

In India, entry-level professionals start with a salary of Rs. 4lpa with just 0-1 years of experience required. As your experience increases, the salary also grows exponentially and can reach Rs 12 LPA in only 6-9 years and Rs 23 LPA after ten years.

What are the career assistance services offered by EduPristine during this course?

EduPristine offers an expertly curated curriculum, industry-specific study material with a one-year-long access period. Aside from this, candidates also get customized career guidance with frequent job updates, resume preparation assistance, and industry-recognized data engineering certification.

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