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Big Data Analytics

An Interactive Live Training

Getting intellectuals ready to become Big Data Experts!

An Industry-Expert Led Live Training

During this interactive training on Zoom you will learn about the different ingredients of Big Data such as Hadoop, Spark, Pig, Hive & Sqoop.


Further you will have hands-on experience on different pillars of the Big Data Ecosystem starting from parallel processing frameworks like Map Reduce & Spark, Distributed Storage techniques like HDFS, Big Data Administration Ambari etc.

At the end the training you will have in-depth understanding & hands-on related to Big Data solutions like Cloudera & HortonWorks.

View Course Outline

Our Approach for Zoom Interactive Classes

After establishing a reputable Physical Training Model, based on our stellar records and customer earned trust we gradually progressed to establish the same reputation in the Zoom based Live Training Model. We are committed to empower you by making our trainings accessible, interactive, and well curated specifically to your objective.

live training process 2



5th June’ 2021

Duration & Timings

7 Weeks (Sat & Sun)

11:00 AM – 4:00 PM


Urdu / Hindi

Seats Capacity

Limited seats!


Meet the trainer of this course who is Big Data Expert!


Mr. Moeed Tariq

Big Data Analyst | Data Engineer | Trainer

Mr. Moeed Tariq has 8+ years of diversified experience in Telecom, IT consultancy and video streaming companies. His expertise includes Big Data, BI, Data Analytics, Data Modelling, Hadoop, Apache Spark, Apache Kafka, HiveQL, NoSQL and RDBMS. He is currently working with Gulf based OTT streaming company as Solution Architect.



Course Outline



Week 1

What is Big Data?
The Big Data Era
Big Data – Data Sources
4 V’s of Big Data
Conventional Data Warehouse Architecture
Modern Data Warehouse Architecture
What is Data Discovery?
Distributed Computing & its Advantage
Big Data Processing Frameworks (Hadoop, Apache Spark, NoSQL Databases)
What is Hadoop & its History?
Introduction to Apache Hadoop Stack (HDFS, MapReduce, Flume, Sqoop, Zookeeper, Ozie, HBase,
Hive, Pig)
Introduction to Big data distributions (On-prem and cloud)
Components of Hadoop Cluster (Master Node, Data Node, Namenode, Job Tracker, Task Tracker)
Sandbox (virtual machine) Installation
Introduction to Hadoop Distributed File System (HDFS)
How HDFS Works
HDFS Block Size & Replication Factor
HDFS Read & Write pipeline
Sandbox tour – Understanding Ambari

Week 2

Sandbox Configuration & Overview
HDFS Commands
HDFS Data Ingestion (Lab)
Parallel Processing Basics
What is MapReduce
How MapReduce works
Introduction to Apache Hive
Hive Alignment with SQL
Hive Query Process
Hive Data Loading
Hive Managed Tables
Hive External Tables
Hive Table Location
Hive Bucketing & Partitioning
Apache Hive (Lab)
Hive Views & Hive use for XML
Hive Supported File Formats
Hive Data Model
Block Compression and Storage Formats in Hive

Week 3

Built-In and External SerDes in Hive (Lab)
Hive complex data types (Array, Map, Struct)
Loading complex data in Hive (Lab)
Hive vs. Impala
Impala Architecture
Hadoop 1.0 vs. Hadoop 2.0
Introduction to YARN Architecture
YARN Resource Manager
YARN Node Manager
YARN Application Manager
YARN Schedulers
YARN Performance Gauging
YARN Performance Measuring
YARN System Health
Resource Allocation in YARN
Containers Concept in Hadoop
YARN Queue Management and Container allocation (Lab)
Handling jobs in YARN Resource Manager UI
Project 01: Building a Sentiment Analysis Application to find the sentiment of tweets

Week 4

Introduction to Apache Tez
Tez vs MapReduce
Tez DAGs
Introduction Apache Pig
Pig vs. Hive
PIG Architecture
Grunt Shell & PIG Scripting (Lab)
PIG Commands
Loading Data in PIG
PIG Filter
PIG Joins
Debugging Using PIG
PIG Execution Modes
PIG Execution Mechanism
Pig integration with Hive – HCatalog
Introduction to Apache Sqoop
Sqoop Architecture
Sqoop Execution Modes
Migrating data with Sqoop (Lab)

Week 5

  • Introduction to Apache Spark
  • Spark vs. MapReduce
  • Spark Architecture
  • Spark Driver
  • Spark Context
  • Spark Executors
  • Spark Core Abstraction – RDDs, DataFrames, Datasets
  • Transformations vs. Actions
  • Spark Transformations (Map, Flatmap, Filter, Distinct)
  • Spark Actions (Collect, First, Take, Count, Reduce, Save-as-text)
  • Lazy Execution
  • SparkContext, HiveContext, SqlContext
  • Scala vs. Pyspark
  • Spark as a In memory processing engine (Lab)
  • Troubleshooting Jobs in Spark UI

Week 6

Introduction to Streaming Analytics
Bounded data vs. Unbounded data
Spark as a stream processing engine
Spark Streaming
Structured Streaming
Streaming Analytics in Spark (Lab)
What are Messaging (Pub/Sub) systems
Introduction to Apache Kafka
Kafka – Core capabilities and Use cases
Topic, Partitions and Offsets
Kafka Brokers
Kafka Producers and Consumers
Kafka as a messaging system (Lab)
Introduction to Data Flow
Apache Nifi as a Data Flow tool
Installing Nifi as a service (Lab)
Flow files, Processors and Connectors
Nifi Templates
Understanding Nifi UI and Creating data flows(Lab)

Week 7

Project 02: Building a Real-Time data pipeline with Nifi, Kafka and Spark
Components of a Big data platform
Big Data Architectures
Lambda and Kappa Architecture
Building batch mode and real time big data pipelines – case studies (Lab)
Realm of NoSQL databases
NoSQL databases types
MongoDB as a NoSQL database
Up and running with MongoDB (Lab)
Next Steps


Following is price for this extensive live training on Big Data

prices slider January 2021 a


Frequently Asked Questions

Who should attend the course?

Graduate or Masters Students with IT, CS or SE background who want to start their career in the Big Data Analytics domain

People who are working in the Big Data Analytics domain and want to advance their career

Executive who want to build a Big Data Analytics department in their start-ups/organizations

What is the timing of the course?

Duration: 7 weeks (Sat-Sun)
Timings: 11AM – 4PM

Who are the Instructors?

How much hands-on will be performed in this course?

Since our courses are led by Industry Experts so it is made sure that content covered in course is designed with hand on knowledge of more than 70-75 % along with supporting theory.

What are the PC requirements?

For Big Data Analytics Professional course, you need to have a PC with minimum 8GB RAM and maximum 16GB RAM.

What if I miss any of the lectures?

Don’t worry! We have got you covered. You shall be shared recorded lectures after each session, in case you want to revise your concepts or miss the lecture due to some personal or professional commitment.

How will this training ensure hands-on practice?

For executing the practical’s included in the Big Data Training, you will set-up tool on your machine. The installation manual for tool prep will be provided to help you install and set-up the required environment.

What sort of projects will be part of this Live Training?

This Certification Training course includes multiple real-time, industry-based projects, which will hone your skills as per current industry standards and prepare you for the future career needs.

Will I get a certificate after this course?

Yes, you will be awarded with a course completion certificate by Dice Analytics. We also keenly conduct an annual convocation for the appreciation and recognition of our students.

Can I get a job after this course?

Since our instructors are industry experts so they do train the students about practical world and also recommend the shinning students in industry for relevant positions.

Reserve your Seat

You can reserve your seat  by filling the form below

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