Big Data training in

  • Get Certified at the Best Big Data Training Institute.
  • Get trained by industry experts.
  • Big DataClassroom and Online training.
  • 20+ Real-time projects
1500+ Students Enrolled
4.7 Rating (124) Ratings
45 Days Duration

Course Preview

Roles for Big DataCourse
  • Sr Hadoop Developer
  • Hadoop Architect
  • Hadoop Admin
  • Big Data Engineer
  • Big Data Analyst

Why Big DataCourse?

  • Learn Think of a business that relies on quick, agile decisions to stay competitive, and most likely big data analytics is involved in making that business tick. 53% Of Companies Are Adopting Big Data.
  • Acces The average base pay for at least six big data skills itself is well over $120,000 a year.
  • Keyfeature Big data has seen massive exponential growth leading to numerous career opportunities.

Why Big Data Training at Digital Lync?

  • Learn Digital Lync offers one of the best Big Data training in Hyderabad with a comprehensive course curriculum.
  • Acces Elevate your practical knowledge with quizzes, assignments, Competitions and Hackathons to give a boost to your confidence with our hands-on Big Data Training.
  • Acces Big Data training in Hyderabad at Digital lync makes you industry ready with coaching sessions, interview prep advice, and resume with 1-1 Mentoring.

Course Curriculum

It stretches your mind, think better and create even better.

4V ( Volume, Velocity, Variety and Veracity) characteristics

Structured and Unstructured Data

Application and use cases of Big Data

Limitations of traditional large Scale systems

How a distributed way of computing is superior (cost and scale)

Opportunities and challenges with Big Data

Introduction to Linux and Big Data Virtual Machine (VM)

Introduction to Linux - Why Linux? -

Windows and the Linux equivalents

Different flavors of Linux

Unity Shell (Ubuntu UI)

Basic Linux

Commands (enough to get started with Hadoop)

HDFS Overview and Architecture

Deployment Architecture

Name Node, Data Node and Checkpoint Node ( aka Secondary Name Node)

Safe mode

Configuration files

HDFS Data Flows ( Read v/s Write)

Load Balancer

Dist Cp

HDFS Federation

HDFS High Availability

Hadoop Archives

CRC Checksum

Data replication

Rack awareness and Block placement policy

Small files problem

Command Line Interface

File System

Web Interface

Legacy MR v/s Next Generation MapReduce ( aka YARN/ MRv2)

Slots v/s Containers


Shuffling, Sorting

Hadoop Data Types

Input and Output Formats

Input Splits - Partitioning ( Hash Partitioner v/s Customer Partitioner)

Speculative execution


JVM Reuse


Word Count

Term Frequency

Inverse Document Frequency

Log Data Analysis

Different ways of joining data

Purchases Data Analysis

Max Temperature


Inverted Index

Introduction and Architecture

Different Modes of executing Pig constructs

Data Types

Dynamic invokers Pig streaming Macros

Pig Latin language Constructs (LOAD, STORE, DUMP, SPLIT, etc)

User Defined Functions

Use Cases

Introduction and Architecture

Different Modes of executing Hive queries

Metastore Implementations

HiveQL (DDL & DML Operations)

External v/s Managed Tables Views

Partitions & Buckets

Joins, Group by, Order by

User Defined Functions

Review of RDBMS

Need for NoSQL

Brewers CAP Theorem


Schema on Read vs. Schema on Write

Different levels of consistency

Key Value




HBase Architecture

Master and Region Server

Catalog Tables (Root and Meta)

HBase Data Modeling

Loading data in HBase

Apache Sqoop

Data movement from Relational databases to Hadoop

Sqoop Commands

Sqoop Advanced features

Apache Flume

Components of Flume

Log Data ingestion to Hadoop

Introduction to RDD

Installation and Configuration of Spark

Spark Architecture

Different interfaces to Spark

Data frames and Datasets

Querying massive data using SparkSql

Sample Python programs in Spark

Data Visualization using Apache Zeppelin

Cloudera Hadoop cluster on the Amazon Using EMR (Elastic Map Reduce)

Using EC2 (Elastic Compute Cloud)

Importing/ exporting data across RDBMS and HDFS using Sqoop

Getting real- time events into HDFS using Flume

Creating workflows in Oozie

Introduction to Graph processing

Graph processing with Neo4J

Processing data in real time using Storm

Interactive Adhoc querying with Impala

Live Projects

Big Data Live Projects

HR Analytics for Attrition Prediction using Logistic Regression

Description: Employee Attrition is an important subject to gauge the satisfaction of the employee in a..

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Big Data Live Projects

Predicting Housing prices using Regression:

Predict the sales price for each house based on input features provided for the...

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Big Data Live Projects

Retail Customer segmentation based on spending patterns

Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the...

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Big Data Live Projects

Stock market price prediction

This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors...

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Big Data Live Projects

Exploratory Data analysis of Crime records in Boston

This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the...

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Big Data Live Projects

Market Basket Analysis using Apriori Algorithm

Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased...

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Case Studies

Retail Data Analysis
Retail Data Analysis

The organizations have developed the capacity to gain greater insight into customer behavior. It is essential to use innovative analytics practices to succeed.

But there’s a big gap between having the data and putting it to work. As big data analytics tools become more affordable and usable, the technology's influence is both growing and expanding into Retail sectors.

Technologies such as HDFS, Map-Reduce, Hive, Pig, Spark, Oozie, Sqoop are tooled to extract and analyze the necessary data.

Stream Analysis
Click Stream Analysis

Many of the e- commerce sites had been making a quite an impact on the overall economy for quite some time in many of the countries.

All the e- commerce portals store the user activities on their site as clickstream activity and later they analyze it to identify what the user has browsed and show the appropriate recommendations when the user visits the site again or to send a personalized email.


Our Big Data training has precisely been developed to reach out to the demand of the learners by keeping in mind the industry standards.
This Big Data course will particularly be helpful for the career advancement of the following audience -

Graduates from the College.

Currently working employees looking to upskill themselves.

Candidates looking for a change in the IT Field.

As such, there are no specific prerequisites for Big Data institutes in Hyderabad. If you are familiar with programming and foundation skills with a sense of curiosity and willingness to learn you are all set for the Big Data training.

Big Data Training Classes are conducted over the Weekdays and Weekends through classroom and online sessions. Please get in touch with the Digital Lync team to get the exact schedule and timings.

Our Big Data faculty has over 12 years of experience.

Big Data Course duration is 50 hours.

Weekday Big Data Training classes will be one hour long and Weekend classes will be three hours long.

Please find the detailed Big Data course curriculum in the Digital Lync Big Data training curriculum section.

Yes, we will assist our students with all the interview preparation techniques

Life at Digital Lync

Life at Digital Lync

The environment at Digital Lync is colorful and creative. It is where ideas are incubated and generated. An apt place to explore yourself.

Inspiring Student Stories.

Here are stories of real knowldege, real people,
under real innovation.

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