Course Curriculum

Hadoop and Spark Course Content

Module 1 :  Introduction to Hadoop and Big data 

• What is Big data?
• Sources of Big data
• Categories of Big data
• Characteristics of Big data
• Use-cases of Big data
• Traditional RDBMS vs Hadoop
• What is Hadoop?
• History of Hadoop
• Understanding Hadoop Architecture
• Fundamental of HDFS (Blocks, Data Node, Name Node, Secondary Name Node)
• Block Placement &Rack Awareness
• HDFS Read/Write
• Drawback with 1.X Hadoop
• Introduction to 2.X Hadoop
• High Availability

Module 2 – Linux

• Making/creating directories
• Removing/deleting directories
• Print working directory
• Change directory
• Manual pages
• Help
• Vi editor
• Creating empty files
• Creating file contents
• Copying file
• Renaming files
• Removing files
• Moving files
• Listing files and directories
• Displaying file contents

Module 3 –HDFS

• Understanding Hadoop configuration files
• Hadoop Components- HDFS, MapReduce
• Overview of Hadoop Processes
• Overview of Hadoop Distributed File System
• The building blocks of Hadoop
• Hands-On Exercise: Using HDFS commands

Module 4 – Map Reduce

• Map Reduce 1(MRv1) o Map Reduce Introduction o How Map Reduce works? o Communication between Job Tracker and Task Tracker o Anatomy of a Map Reduce Job Submission
• MapReduce-2(YARN) o Limitations of Current Architecture o YARN Architecture o Node Manager & Resource Manager

Module 5-Hive

• What is hive?
• Why hive?
• What hive is not?
• Meta store DB in hive
• Architecture of hive
• Internal table
• External table
• Hive operations
• Static Partition
• Dynamic Partition
• Bucketing
• Bucketing with sorting
• File formats
• Hive performance tuning

Module 6 – Sqoop

• What is Sqoop?
• Architecture of Sqoop
• Listing databases
• Listing tables
• Different ways of setting the password
• Using options file
• Sqoop eval
• Sqoop import into target directory
• Sqoop import into warehouse directory
  • Setting the number of mappers
• Life cycle of Sqoop import
• Split-by clause
• Importing all tables
• Import into hive tables
• Export from hive tables
• Setting number of mappers during the export

Module 7 – Python Core

• What is Python?
• Why Python?
• Installation of python
• Conditions
• Loops
• Break statement
• Continue statement
• Range functions
• Command line arguments

Module 8 – Strings & Collections

• String Object Basics
• String Methods
• Splitting and Joining Strings
• String format functions
• List Object Basics
• List Methods
• Tuples
• Sets
• Frozen sets
• Dictionary
• Iterators
• Generators
• Decorators
• List Set Dictionary comprehensions

Module 9 – Python Advanced concepts

• Creating Classes and Objects
• Inheritance
• Multiple Inheritance
• Working with files
• Reading and Writing files
• Using Standard Modules
• Creating custom modules
  • Exceptions Handling with Try-except
• Finally, in exception handling

Module 10-Getting started with Spark
• What is Apache Spark & Why Spark?
• Spark History
• Unification in Spark
• Spark ecosystem Vs Hadoop
• Spark with Hadoop
• Overview of the Python and Scala Shells in Spark
• Spark Standalone Cluster Architecture and its application flow

Module 11 –Programming with RDDS, DFs & DSs
•RDD Fundamentals, RDD Characteristics, and RDD Creation
• RDD Operations
• Transformations
• Actions
• RDD Types
• Lazy Evaluation
• Persistence (Caching)
• Module-Advanced spark programming
• Accumulators and Fault Tolerance
• Broadcast Variables
• Custom Partitioning
• Dealing with different file formats
• Hadoop Input and Output Formats
• Connecting to diverse Data Sources
• Module-Spark SQL
• Linking with Spark SQL
• Initializing Spark SQL
• Data Frames &Caching
• Case Classes, Inferred Schema
• Loading and Saving Data
• Apache Hive
• Data Sources/Parquet
• JSON
• Spark SQL User Defined Functions (UDFs)

Module 12-KAFKA & Spark Streaming
• Getting started with Kafka
• Understanding Kafka Producer and Consumer APIs
• Deep dive into producer & consumer APIs
• Ingesting Web Server logs into Kafka
• Getting started with Spark Streaming
• Getting started with HBASE
• Integrating Kafka-Spark Streaming-HBASE

Module 13 – Spark on Amazon Web Services (AWS)
• Introduction
• Sign up for an AWS account
• Setup Cygwin on Windows
• Quick Preview of Cygwin
• Understand Pricing
• Create first EC2 Instance
• Connecting to EC2 Instance
• Understanding EC2 dashboard left menu
• Different EC2 Instance states
• Describing EC2 Instance
• Using elastic IPs to connect to EC2 Instance
• Using security groups to provide security to EC2 Instance
• Understanding the concept of bastion server
• Terminating EC2 Instance & relieving all the resources
• Create security credentials for AWS account
• Setting up AWS CLI in Windows
• Creating s3 bucket
• Deleting root access keys
• Enable MFA for root account
• Introduction to IAM users & customizing sign in link
• Create first IAM user
• Create group and add user
• Configure IAM password policy
• Understanding IAM best practices
• AWS managed policies & creating custom policies
• Assign policy to entities (group or user)
• Creating role for EC2 trusted entity with permissions on s3
• Assigning role to EC2 instance
• Introduction to EMR
• EMR concepts
• Pre-requisites before setting up EMR cluster
• Setting up data sets
• Setup EMR with Spark cluster using options
• Connecting to EMR cluster
• Submitting spark job on EMR cluster
• Validating the results
• Terminating EMR Cluster

Module 14-Airflow
• What is Airflow?
• Airflow terminology
• Why Airflow?
• What is Airflow Scheduler?
• What is DAG RUN?
• Airflow Operators
• Create first DAG/Workflow
• Run Pyspark job with Airflow

Module 15-Interview Preparation
• 3 Real-Time Projects
• Deployment on multiple platforms
• Discussion on project explanation in interview
• Data engineer roles and responsibilities
• Data engineer day-to-day work
• One-on-one discussion of a résumé that includes a project, technology, and experience.
• Mock interview for every student
• Real time Interview Questions

 

Course Overview

Big Data, Hadoop and Spark: The Ultimate Guide to online training in Bangalore

A perfect blend of in-depth Hadoop theoretical knowledge and strong practical skills via implementation of real-time Hadoop projects to give you a head start and enable you to bag top Hadoop jobs in the Big Data industry.

What is Big Data?

Hadoop is a general-purpose big data platform, capable of processing and managing big data sets. It is an open-source software framework that runs distributed computing across cluster of nodes. Most data processing is still done in batch manner but with the introduction of Hadoop and its cluster manager Hadoop YARN, Hadoop is capable of efficiently processing large data sets. Hadoop can process both structured and unstructured data. In simple terms, a Big Data dataset would be a set of unorganized, frequently used records stored in a distributed file system. This kind of datasets are used to forecast events of marketing value or social trends, and diagnose and prevent disease.

What is Hadoop?

Hadoop is an open-source distributed file system that is primarily designed for workloads which require parallel processing for analytics, machine learning, and data mining. What are the Best Top Hadoop Courses out there for BANGALORE Clients? Bestway Technologies is the right solution for the students who is looking for Hadoop. This course will introduce you to the major elements of data warehousing, Hadoop and Spark, and hands-on data processing in Hadoop.

What is Spark?

Spark, an open-source engine for statistical computing, designed specifically for the analysis of very large amounts of data across all kinds of storage formats such as HDFS, HBase, and Cassandra. With high flexibility and performance, Spark lets you run machine learning algorithms using Spark without writing any software code. You can learn Spark in three months at AWS: Data Science Track Real-time Analytics: Apache Spark How to hack on Spark in a Day? Spark is an amazing piece of software which enables interactive and interactive analytics. So, your data is not a pile of random values that you can only calculate some critical mathematical equation over. You can ask the Spark program to find a specific answer based on your query and much more.

Hadoop Key Concepts

An Introduction to MapReduce: MapReduce is a distributed processing paradigm in which the applications are divided into different pieces of work which can be executed in parallel. They can be executed on local machines, across different computer clusters, across a local cluster as well as on high-speed supercomputers in the cloud. MapReduce can be implemented to efficiently and effectively solve a range of complex problems such as search and retrieval, object processing, data compression, and data flow transformation. Hadoop: Machine Learning and Data Analysis A real-time big data framework by MapR, offering features such as full ACID transactions, storage of JSON-formatted data files, as well as access to a variety of advanced storage options to aid storage and recovery capabilities.

Spark Key Concepts

In this training, you will learn Spark concepts and how to take a deep dive into the worlds of Spark and Hadoop through knowledge and hands-on examples. Spark Mastery Course for Top Hadoop Developers Learn to provide the performance, reliability, scalability, security and control of large distributed systems to drive your career in Big Data. Rocker – Advanced Hadoop Training by AWS Professional Rocker- Advanced is one of the most comprehensive eLearning training courses on Big Data and the online training platform for professionals. Spark Mastery Course for Data Engineers and Data Scientists Master the Hadoop framework for designing Spark pipelines for applications including ad targeting, advanced data science, machine learning, and much more.

Hadoop and Spark Key Concepts

Hadoop anopen-source distributed file system and data processing framework developed by the Apache Software Foundation. Hadoop implements a distributed computing model. Aka: HDFS. HDFS is basically an RDBMS inspired filesystem. Hadoop-based Distributed file system where you can store any type of data. HDFS is extensible and interoperable. Hadoop MapReduce It is a process or an algorithm that implements distributed computing paradigm with an incremental processing approach, optimizing performance in the face of size limitations of fixed compute clusters. Hadoop Job Market the Big Data Industry is experiencing growth at a brisk pace and the number of job postings related to Hadoop is increasing significantly.

Hadoop Practical Implementation

Compact database for Hadoop. Explore the inner workings of Hadoop with this unique tool to create your own database. How-to write a basic query to get data in a Hadoop cluster. Easily get some data in your Hadoop cluster in just a few minutes! Topics: Hadoop, distributed processing, MapReduce, Hadoop architecture, Big Data. Data warehousing. SQL.

Spark Practical Implementation

The Spark Language is the basic component of Apache Spark, so while learning about Spark, the emphasis is mostly on Spark itself. This course shows you how to write the basic Spark code and how to create Spark-based web applications as well as Spark programs. Learn all about Hadoop and how it can transform your career by taking this Big Data and Big Analytics course in Bangalore and Hyderabad. This course will teach you how to use data visualizations to present your analysis.

Conclusion

Building on its legacy of offering premium video courses to students of Indian Institute of Management Bangalore, online training institute Kaggle is launching a new initiative named Big Data and Hadoop: The Ultimate Guide to online training in Bangalore. With this initiative, the company aims to offer Big Data and Hadoop Training and Certification courses online in both – Java and Hadoop. These courses are all about online classes where you will get expert-led, hands-on tutorials on Hadoop and Big Data for the courses.

 

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Reviews

Hadoop and Spark Rated 4.9 based on 7 reviews.

By: Sreedhar Reddy, Rating:
I have joined BESTWAY Technologies for Hadoop and Spark course. Mr. Vamsi Kirshna Sir guide me so well that after completing half of the course I got a job. Thank you so much Vamsi Sir for your guidance and support. You are great.

By: Asif, Rating:
When I was searching for a Hadoop and Spark online training Bangalore, I came across Bestway Tech. Then, I attended the demo class, I found the trainer (Mr. Vamsi Krishna) very professional and got to know about her students, who made several great projects under her guidance. I was very impressed by Sir, so I decided to join the training, and I am glad that I joined it. Though it was just an introductory training, (I was expecting a little more from the training) but it was a nice experience.

By: Nimesh, Rating:
It was really a very good experience with BESTWAY Technologies, I had online training on Big Data and Hadoop and Spark. Mr. Vamsi Krishna sir thought us. He is very excellent, and humble to he never frustrate explaining same topic again and again, help us when we stuck, we really enjoyed.

By: Sreenivas, Rating:
I joined BESTWAY Training for Big data Hadoop online training. Mr. Vamsi Sir has been the best technical trainer I have come across in my entire career. In short, it is the best Training Center for anyone looking for Data Hadoop. I found Vamsi sir BESTWAY, I interacted with him for a few minutes and got to know how much knowledge he has on the subject. What made me choose BESTWAY training is Vamsi sir’s experience and the curriculum. I got more than what I had expected.

By: Anurag, Rating:
According to me the best HADOOP and SPARK online training institute in Ameerpet Hyderabad. They are providing placement assistance also. I completed my Hadoop course here. I’m completely satisfied with the trainer Mr. Vamsi Sir.

By: Hitesh, Rating:
The Hadoop and Spark Online Training from Hyderabad, India, with the best trainer, Mr. Vamsi Krishna, was phenomenal! Mr. Vamsi Krishna's expertise in Hadoop and Spark was exceptional, and his teaching style was engaging. The course content was comprehensive, and the practical hands-on exercises were invaluable. This training has significantly enhanced my knowledge and skills in Big Data technologies.

By: Pranjal, Rating:
I had an exceptional learning experience with the Hadoop and Spark Online Training in Hyderabad, India, under the guidance of the best trainer, Mr. Vamsi Krishna. His in-depth knowledge and passion for Hadoop and Spark were evident in every session. The course content was well-structured, and Mr. Vamsi Krishna's real-world insights added immense value. This training has prepared me exceptionally well for Big Data projects and certifications. Highly recommended!

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