Advance Diploma in Big Data Engineering

Learn to describe the large volume of data - both structured and unstructed that inundates a business on day-to-day basics. Start your career with Big data Hadoop from Techmed academy the Best Institute. Big Data Hadoop training course in Bangalore provides in-depth knowledge on Hadoop Ecosystem tools and Big Data.

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Program Overview

Key Highlights

  • Big Data Hadoop training course in Bangalore Industry recognised Techmed Certificate
  • big data course Designed for Working Professionals
  •  big data course online Case Studies and Projects
  • Big Data Hadoop Training in Bangalore Timely Doubt Resolution
  • Hadoop Training in Bangalore Practical Hands-on Workshops
  • Big Data Hadoop training course in Bangalore Personalised Resume Feedback
  • , big data course Job Placement Assistance with Top Firms

Advance Diploma in Big Data Engineering in Bangalore

  • TOP SKILLS YOU WILL LEARN
    Responsive Websites Development, Interactive Website, Web UI, PHP, jQuery UI, Creative Tools, Icons & Image Creating, Image Editing, and More.
  • JOB OPPORTUNITIES
    Frontend Developer, Web Developer, WordPress Developer, Web Designer, Graphic Designer, UI Designer, PHP Developer, JavaScript Developer, Responsive Web Developer, and More.
  • THIS PROGRAM IS FOR WHOM?
    Freshers, Professional Designers, Entrepreneurs, Branding Manager, Developers.
  • ELIGIBILITY
    Bachelor’s or Equivalent Degree & Master Degree.
Big Data Hadoop Training in Bangalore

Why Techmed Is your choice?

Syllabus

Best-in-class content by leading faculty and industry leaders in the form of classes, cases and projects, assignments and live sessions

Big Data Introduction And Hadoop

  • 6 Hours 05 Assignments
Fundamental
Data Storage and Analysis
Comparison with RDBMS
HDFS Acrchitecture

  • 2 Weeks 06 Assignments
Basic Terminologies
HDFS Block Concepts
Replication Concepts
Basic reading & writing of files in HDFS
Basic processing concepts in MapReduce
Data Flow
Anatomy of file READ and WRITE
Hadoop Administrator

  • 3 Weeks 10 Assignments
Hadoop Gen1 vs Hadoop Gen 2(Yarn)
Linux commands
Single and Multimode cluster installation (HADOOP Gen 2)
AWS (EC2, RDS, S3, IAM and Cloud formation)
Cloudera and Hortonworks distribution installation on AWS
Cloudera Manager and Ambari
Hadoop Security and Commissioning and Decommissioning of nodes
Sizing of Hadoop Cluster and Name Node High Availability
Data Ingestion

  • 4 Weeks 20 Assignments
Sqoop:

  • Migration of data from MYSQL/ ORACLE to HDFS.b. History/timelines of python
  • Creating SQOOP job.
  • Scheduling and Monitoring SQOOP job using OOZIE and Crontab.
  • Incremental and Last modified mode in Sqoop.
Talend:

  • Installation of Talend big data studio on windows server.
  • Creating and Scheduling talent Jobs.
  • Components: tmap, tmssqlinput, tmssqloutput,tFileInputDelimited, tfileoutputdelimited, tmssqloutputbulkexec, tunique, tFlowToIterate,tIterateToFlow, tlogcatcher, tflowmetercatcher, tfilelist, taggregate, tsort, thdfsinput, thdfsoutput, tFilterRow, thiveload.
Flume:

  • Flume Architecture
  • Data Ingest in HDFS with Flume
  • Flume Sources
  • Flume Sinks
  • Topology Design Considerations
Data Processing

  • 4 Weeks 20 Assignments
MapReduce:

  • Env Setup
  • Tool and Tool Runner
  • Mapper
  • Reducer
  • Driver program
  • How to package the job?
  • MapReduce WebUI
  • How MapReduce Job run?
  • Shuffle & Sort
  • Speculative Execution
  • Input Formats
  • Input Splits and Record Reader
  • Default Input Formats
  • Implement Custom Input Format
  • Output Formats
  • Default Output formats
  • Output Record Reader
  • Compression
  • Map Output
  • Final Output
  • Data types – default
  • Writable vs Writable Comparable
  • Custom Data types – Custom Writable/Comparable
  • File Based Data structures
  • Sequence file
  • Reading and Writing into Sequence file
  • Map File
  • Tuning MapReduce Jobs
  • Advanced MapReduce
  • Sorting
  • Partial Sort
  • Total Sort
  • Secondary Sort
  • Joins
Hive:

  • Comparison with RDBMS
  • HQL
  • Data types
  • Tables
  • Importing and Exporting
  • Partitioning and Bucketing – Advanced.
  • Joins and Join Optimization.
  • Functions- Built in & user defined
  • Advanced Optimization of HQL
  • Storage File Formats – Advanced
  • Loading and Storing Data
  • SerDes – Advanced
Pig:

  • Important basics
  • Pig Latin
  • Data types
  • Functions – Built-in, User Defined
  • Loading and Storing Data
Spark:

  • Spark introduction
  • Spark vs MapReduce
  • Intro to spark lib (SparkSql, SparkStreaming, Spark Core)
Python For Hadoop

  • 8 Weeks 20 Assignments
An Introduction to Python

  • a. Brief about the course
  • b. History/timelines of python
  • c. What is python?
  • d. What python can do?
  • e. How the name was put up as python
  • f. Why python?
  • g. Who all are using python?
  • h. Features of python
  • i. Python installation
  • j. Hello world
    • i. Using CMD
    • ii. IDLE
    • iii. By python script
    • iv. Python command line
Beginning Python Basics

  • a. The print statements
  • b. Comments
  • c. Python Data Structures
  • d. Variables & Data Types
    • i. Rules for variable
    • ii. Declaring variables
    • iii. Assignment in variables
    • iv. Operations with variables
    • v. Reserved keyword
  • e. Operators in Python
  • f. Simple Input & Output
  • g. Examples for variables, Data Types, Operators
Python Program Flow

  • a. Indentation
  • b. The If statement and its' related statement
  • c. An example with if and it's related statement
  • d. The while loop
  • e. The for loop
  • f. The range statements
  • g. Break
  • h. Continue
  • i. Pass
  • j. Examples for looping
Functions & Modules

  • a. System define function (number system and its sdf, String and its sdf)
  • b. Create your own functions (user define function)
  • c. Functions Parameters
  • d. Variable Arguments
Exceptions

  • a. Errors
  • b. Exception Handling with try
  • c. Handling Multiple Exceptions
  • d. raise
  • e. finally
  • f. else
File Handling

  • a. File Handling Modes
  • b. Reading Files
  • c. Writing & Appending to Files
  • d. Handling File Exceptions
Data Structures and Data Structures functions

  • a. List and its sdf
  • b. Tuple and its sdf
  • c. Dictionary and its sdf
  • d. Set and its sdf
  • e. Use cases and practical examples
Casting

a. Intro to casting
NOSQL

  • 2 Weeks 10 Assignments
Cassandra

  • Cassandra cluster installation
  • Cassandra Architecture
  • Cqlsh
  • Replication strategy
  • Tools: Opscenter, Nodetool and CCM
  • Cassandra use cases
Labs

Real Time use cases and Data sets covered (10+ Real Time datasets) Word count, Sensors (Weather Sensors) Dataset, Social Media data sets like YouTube, Twitter data analysis
Scala & Spark training Course Outline Spark Batch processing API

  • 8 Weeks 20 Assignments
Introduction

  • Why Spark?
  • Evolution of Distributed systems
  • Challenges with existing distributed systems
  • Need of new generation
  • Hardware/software evolution in last decade
  • Spark History
  • Unification in Spark
  • Spark ecosystem vs HadoopSpark with Hadoop
  • Spark with Hadoop
  • Who are using Spark?
Scala Basics Required for Spark

  • Spark Architecture
  • RDD
  • Immutability
  • Laziness
  • Type inference
  • Cacheable
  • Spark on cluster management frameworks
  • Spark task distribution
Spark installation

  • Local
  • Spark on YARN
  • Stand alone
  • Spark on Mesos
Spark API Hands on

  • RDD operations
  • Key-value pair RDD
  • Map Reduce
  • Double RDD
Advanced operations

  • Aggregate
  • Fold
  • mapPartitions
  • glom
  • Broadcasters
Integration with HDFS

  • Introduction to HDFS
  • HDFS architecture
  • Using HDFS
Caching and Lineage

  • RDD caching
  • Fault recovery
Spark streaming API

  • Introduction
  • Spark streaming Architecture
  • DStreams
  • DStream vs RDD
  • Receivers
  • Batch vs Streaming
Input Streams

  • Socket
  • HDFS
  • Twitter
  • Kafka
Streaming API Hands-on

  • DStream creation
  • Transformations
  • Stateful operations
Check pointing

  • Recoverable computations
  • Error handling
Combining batch and Streaming

  • Foreach
  • Transform
  • Joins
Persist and Caching

  • Saving DStream
  • Caching DStream
Window Operations

  • Window
  • ountByWindow
  • reduceByWindow
Deploying Spark Streaming

  • Clustering
  • Check pointing
  • Driver fallback
Spark SQL

Introduction
Spark SQL DDL

  • Case classes
  • Inferred schema
  • Parquet files
  • JSON
  • Schema RDD
Spark SQL DML

  • Projection
  • Condition
  • groupBy
  • joins
  • partitioning
Extending Spark SQL

  • User defined functions
  • User defined aggregate function
Spark SQL in Streaming

  • Querying DStreaming
  • DStream joins
Demo Project

Ecommerce Log Analytics using Kafka, Spark Streaming and Cassandra.
Master Project

  • 4 Weeks 10 Assignments
Real-time Data Warehouse migration
Real-time concepts
Hive - Advanced topics
Sqoop import/export
Oozie Scheduling
How Hadoop MR/Spark used in DW
RDBMS concepts
ETL tool concepts
Integration with Reporting tools
Interview Preparation & Placement Assistance

  • 2 Weeks 10 Assignments
Interview Questions
Creative Resume Preparation
Personality Development & Public Speaking Skills
How to Crack Interviews

Career Impact

Career Counselling

Every student wants to follow a success oriented carrier which can provide them a satisfactory profession.
Techmed carrier council help them to realize their dreams by providing various opportunities in terms of placement and career guidance.

Career Transition

If you want to return to the workforce, you have to manage and overcome the unspoken assumptions about who you are and what you’re capable of.
We, Techmed lay out very clearly what you have learned about managing, inside or outside of a professional setting.

Hiring Partners

Program Fee


What's Included in the Price?

  • Certification in Techmed Academy
  • Study Materials provided
  • Resolution to all your queries
  • Healping In Portfolio Preparation
  • Workshops and seminars
  • Interview Assistance

Let us know you are Interested!

Students Reviews

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