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Big Data Analytics

An Interactive Live Training

Getting intellectuals ready to become Big Data Experts!

Training Description


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 Live Training Model. We are committed to empower you by making our trainings accessible, interactive, and well curated specifically to your objective.



27th FEB’ 2021

Duration & Timings

7 Weeks (Sat & Sun)

11 AM – 3.30 PM


Urud / Hindi


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 6+ years of diversified experience in Telecom Business Support System, Business Intelligence & Commercial B2B departments. His expertise includes Big Data, Business Intelligence, Data Analytics, Data Modelling and Visualization, Hadoop, Apache Spark, Apache Kafka, HiveQL, IBM Cognos, NoSQL, MongoDB, RDBMS.



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 training on Big Data



Frequently Asked Questions

Who should attend the course?

Recent graduates, third year and final year students from the computer science disciplines.

Professionals from the computer science domain who want to shift the profession to Big Data Analytics.

Executives who want to build the initial knowledge about the impact of the Big Data ecosystem on organization growth.

Who are the Instructors?

What is the timing of the course?

Duration: 7 weeks

Class Days: Saturdays & Sundays

Timings: 11:00 AM – 03:30 PM

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.

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 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.

What are the PC requirements?

For Big Data Professional course, you need to have Minimum Core i3 PC, 4th Generation with 12GB RAM and ideally Core i7, 5th Generation with 16GB RAM.

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.

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