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Data engineering Course

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Data Engineering with Hadoop

Our offering of Data Engineering with HADOOP is an intensive amalgamation of all the core components such as MapReduce, HBASE, HDFS, HIVE, SQOOP, PIG, and OOZIE, among others. This course is targeted at filling the evident gap in the demand and supply of industry-specific skills. The completion of this course will take your career to new heights.

Fulfil your Career Goals in a Booming Industry

Spot a Promising Future in your Favorite Industry!

Big Data market size in Rs. Crore

Big Data MARKET SIZE

Your Indicative Salary in BIG DATA!

(India)

Indicative Salary for data Scientist

Our Students Work in Companies like

The demand for Big Data is ever increasing in both domestic and international job market. Its Indian market is expected to shoot up to INR 28000 Crores by the end of 2019. This makes it the best time to make a career in this field, and for this, we at EduPristine will assist you in every possible way— end-to-end career support, right from skill-based training to job assistance.

Here is how we bridge the skill gap and prepare you to be job ready


Practical & Experiential Training

  • Industry relevant content
  • Hands-on covering all Hadoop Ecosystem

    tools

  • Training by Subject Matter Experts
  • Tools – Hadoop, HDFS, MapReduce, HIVE,

    PIG, HBASE, SQOOP, OOZIE, Spark, Scala, Yarn, HUE, Zookeeper,

    • Real time project
  • Real time cluster configuration
  • Dedicated Discussion Forums
  • After Course Engagement (ACE)
=

Market Ready

  • Frequent job updates
  • Customised career guidance
  • Resume preparation assistance
  • Career Services
  • Get Data Engineering Certification

Program Highlights

Classroom Training

60 hrs Instructor led Training

Self-paced learning content

Real-life case studies

Post Session practice projects/ case studies

Tools – Hadoop, HDFS, MapReduce, HIVE, PIG, HBASE, SQOOP, OOZIE, Spark, Scala

Experienced and Passionate Trainers

24*7 Learning Management System (LMS) access with a 1 year validity

Dedicated Discussion Forums

After Course Engagement (ACE)

Career Services

Corporate Training

Customized Training Solutions

Requirement based Training Modules

Foundation to Expert level Curriculum

Industry Expert Trainers

Learning Management System (LMS) access

Training at Your Premises

In Person/ Online Live Virtual Classes (LVC)

Case based Training/Simulations

Comprehensive Study Notes

Single Point of Contact for all communications

Course Curriculum

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

SQL Basics (Recording) »

  1. SQL Intro
  2. Data Types-
  3. DDL –
    1. Create/Alter Table
    2. Create Alter VIEWS
    3. INDEX
  4. DML –
    1. Insert
    2. Update
    3. Delete
  5. Query
    1. Select
    2. Like
    3. In
    4. Between
    5. Distinct
    6. Where
    7. And/or
    8. Orderby
    9. Null
    10. Not Null
  6. Joins
    1. Inner
    2. Left
    3. Right
    4. Full
    5. Union
  7. Functions
    1. Avg
    2. Count
    3. First
    4. Min
    5. Max
  8. Group By – Having

Installing Hadoop and Spark Components on local machine »

  1. Linux
  2. Java
  3. Scala
  4. Python
  5. MySQL
  6. PIG
  7. HIVE
  8. HBASE
  9. SPARK
  10. Anaconda
  11. MongoDB

Introdution to Big Data-Hadoop »

  1. Big Data (What, Why)
  2. 5 V’s
  3. Overview of Hadoop Ecosystem
  4. Role of Hadoop in Big data
  5. Who is using Hadoop
  6. Current Scenario in Hadoop Ecosystem
  7. Installation - Configuration
  8. Use Cases of Hadoop

Hadoop Distributed File System (HDFS) »

  1. Concepts - Architecture –
  2. Data Flow (File Read , File Write)–
  3. Fault Tolerance
  4. Shell Commands –
  5. Data Integrity –
  6. Hadoop Demons
    1. NameNode (Name Node Failure and Recovery)
    2. DataNode
    3. Role of Secondary NameNode
    4. Limitations in Hadoop-1.0   
    5. Hadoop-2.0 HDFS Federation
    6. High Availability in HDFS
    7. Other Improvements in HDFS2
    8. Commands (fsck , dfsadmin)
    9. Schedulers in YARN
    10. Rack Awareness Policy
    11. Balancing
    12. Compression Codecs

MapReduce »

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

Deep Dive in MapReduce Programming »

  1. Counters (Built In and Custom)
  2. Uber Mode
  3. Distributed Cache
  4. Joins(MapSide, Reduce Side)
  5. ToolRunner

Hive Query Language »

  1. Architecture – Installation –Configuration –
  2. Hive Server(JDBC,ODBC, Thrift)
  3. Metastore
  4. Hive vs. RDBMS
  5. Database
  6. Tables
    1. External Table
    2. Managed Table
  7. DDL
  8. Virtual Column in Hive
  9. JOINs
    1. Inner Join
    2. Left Outer join
    3. Right Outer join
    4. Full join
  10. VIEWs
  11. Index in Hive
    1. BITMAP
    2. COMPACT
  12. DML
  13. Hive Functions
  14. UDF
  15. Partitioning
    1. Static Partition
    2. Dynamic Partition
  16. Bucketing
    1. Bucketing benefits
    2. TABLESAMPLE
  17. SerDe
    1. LazySimpleSerDe
    2. OrcSerde
    3. ColumnarSerDe
    4. ParquetHiveSerDe
    5. AvroSerDe
  18. Hive-Hbase Integration
  19. File Formats
    1. RCFile
    2. ORCFile
    3. Parquet
    4. AVRO
  20. Choosing right storage format and compression in Hive
  21. Performance Tuning

Pig Latin »

  1. Architecture - Installation
  2. Hive vs. Pig
  3. Pig Execution modes
    1. LOCAL
    2. MR
  4. Pig Latin Syntax
    1. Load
    2. Store
  5. Data Types
    1. Tuple,
    2. Bag
    3. Map
  6. File System Commands in PIG
  7. PIG Relational Operators
  8. GROUP BY
  9. COGROUP BY
  10. JOIN
    1. Skewed
    2. Replicated
    3. Merge
  11. Functions
    1. Eval
    2. String
  12. Pig Server with HCatalog
  13. Macros
  14. UDFs
  15. PIG Parameter substitution
    1. Run Vs Exec
  16. Performance

HBASE »

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

SQOOP – SQL to Hadoop »

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

SPARK with SCALA »

  1. Introduction to SCALA programming.
  2. Core Spark Concepts
  3. RDDs and its operations
  4. Transformations
  5. Actions
  6. Accumulator
  7. Broadcast
  8. Persist RDD
  9. Introduction to Spark SQL

Live Project »

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

Must Know Big Data Tools that You will Learn in Class


Digital Marketing tools

Our Alumni Speak

profile Picture1

It was great to learn from EduPristine, with highly skilled Trainers to resolve every doubt quickly. Course content is good, simple, and in depth. Plus point is access to the cluster for practice, even after completing the course. Overall EduPristine is best with positive infrastructure and environment!

Aatish Shinde 
profile Picture2

My confidence in the Big Data Analytics education I obtained through EduPristine reflects in the regular insights I share with my team mates and the stake holders. The elevation in my role to a Senior Analyst was a testament to my willingness to learn and grow in the Big data industry. My success is directly related to my Edupristine learning experience. By learning and incorporating the Big Data Techniques and tools learned at EduPristine, an organization can intentionally disrupt the status quo and really begin to recognize its potential. Edupristine is affecting change in teams by offering this affordable, new educational program.

Perrin Shah

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Apply for the Data Engineering with Hadoop

We only take 30 students per batch.

Note: The regular decision deadline for the enrollment is 2 weeks before the batch starts.



Why EduPristine


Our Training Methodology:

Our students can expect a mix of case studies, tools, and the hallmark pedagogy of learning through a plethora of hands-on and real-life scenarios. Finishing the course with our live project helps the participants to season their skills as a Data Engineer professional; thus, making them ready for the job role. The methodology offers real-life challenges, compelling learners to apply concepts in the class and prepare for career realities.

What EduPristine is known for:

EduPristine is a member of Adtalem Global Education (NYSE: ATGE), a global education provider headquartered in the United States. Adtalem is a $1.2 billion (INR 7560 crores) company that has nine institutions and companies with more than 16,000 employees spread across 145 locations. Adtalem takes pride in training 142,000 degree-seeking students all over the world. The organization's purpose is to empower students to achieve their goals, find success and make inspiring contributions to our global community. EduPristine is one of India's leading training providers in Analytics, Accounting, Finance, and Marketing. Founded in 2008, EduPristine has a strong online platform and network of classrooms across India and caters to self-paced learning and online learning, in addition to classroom learning.

A large alumni network of 50,000+ students

15+ locations and 35 Industries

A faculty pool of 250+ Industry experts

Soft skills training & Career Services

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