BigData Training

Master Big Data:
Get in-depth knowledge of the Big Data Framework using Hadoop and Spark, and unlock the value in massive datasets

  • 10 - 20 weeks

  • 102 Lectures

  • 502 Student Enrolled
4.5 3572 Reviews


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BigData Training

BigData Training

Course Overview

Kode Campus presents to you a comprehensive up-to-date Big Data training program. This course will empower you with the job-relevant skills and power you ahead in your career.
Curated by Big Data instructors with over 10+ years of experience, this course covers all the dimensions of Data Engineering using SQL, NoSQL, Hadoop ecosystem, and employs the most widely used technologies like HDFS, Sqoop, Hive, and Spark.
Packed with practical exercises based on real-life examples, the course would teach how to manage, store, interpret, process and analyze Big Data. This course is suitable for those with computer backgrounds, analytic mindset, and some coding knowledge.

What you'll learn

  • All the V's of Big Data (volume, velocity, variety, veracity, valence, and value), data-based terminologies, and other core concepts. Understand how a Big Data Management System differs from a traditional Database Management System.
  • Get familiar with the technologies in the Hadoop Stack. Learn to design distributed systems that manage "big data" using Hadoop and Hadoop-based technologies, including Hive, Pig, and Spark.
  • Understand how MapReduce can be used to analyze big data sets and write MapReduce jobs using Python.
  • Frame big data analysis problems as Spark problems and employ scalable machine learning algorithms of Spark to analyze them. Answer common data mining questions using the MLLib machine learning library.
  • Get a quick course in the Scala programming language and use it to develop distributed code.

In computing, data is information (of quantities, characters, or symbols), which can be stored and translated into a form that's efficient for transmitting or processing. The data is mostly in binary format, with a bunch of ones and zeros.

Big Data term is used to describe the large volume of data. The data is a combination of structured, unstructured, and semi-structured data types, and is so complex that traditional data management tools often have difficulty processing and analyzing them.

Big data is super important as it acts as a foundation for machine learning projects, predictive modeling, or other advanced analytical applications, allowing to dig insights that lead to better decision making and strategic business moves.

Facebook: Producing 500+terabytes of new data every day in the form of photos and video uploads, message exchanges, putting comments, etc.
New York Stock Exchange: Generating one terabyte new trade data is every day.
Jet Engine: Generating 10+terabytes of data in 30 minutes of flight time.

Types of Big Data
Structured: Data that can be easily stored, accessed, and processed using conventional databases and spreadsheets.
Ex- An employee table in a company's database with details like ID, job position, experience, salary, etc.
Unstructured: Heterogeneous data having a combination of text files, images videos, etc.
Ex- Email copy with text, images, and video.
Semi-structured: This data type has a structure to it, but the structure can't be defined by a table definition (as in relational DBMS). The semi-structured data type has both structured and unstructured data types.
Ex- Data represented in an XML file.

Characteristics of Big Data
Big Data can be described with its 6V’s (volume, velocity, variety, veracity, valence, and value). Let's look at each one in brief.
Volume: The quantity of data generated. To qualify as big data, the data size should be larger than terabytes and petabytes.
Variety: Data types in the Big Data. Variety also refers to the various data sources or channels pouring in the data.
Veracity: Quality of data, or how reliable or true is the data. To ensure data quality is great, businesses must link, match, cleanse and transform data across systems.
Velocity: How fast is big data being generated and how fast it needs to be processed in order to meet the demands.
Value: Probable Revenue (or worth or profit) that can be obtained after the processing and analysis of big data.
Variability: Define changing formats, structures, or sources of big data.
Enroll in this course today to thoroughly learn all these data-based terminologies and other core concepts with real-life examples.
Storing and processing big data requires great compute infrastructures, optimally managed server clusters, and considerable investment to keep everything running smoothly. That's why public cloud computing is now a primary vehicle for hosting big data systems, especially for small and medium businesses.

There are several advantages to using public cloud computing.

  • Easy storage of even petabytes of data
  • Ability to scale up the required number of servers just long enough to complete a big data project
  • Pay just for the storage and compute time actually used
  • Feature to turn off the cloud instances if not required

Here's how Big Data can be stored in cloud environments:

  • Hadoop Distributed File System (HDFS)
  • Amazon Simple Storage Service (S3)
  • NoSQL Databases
  • Relational Database

Public cloud providers offering big data capabilities through managed services

  • Amazon EMR (formerly Elastic MapReduce)
  • Microsoft Azure HDInsight
  • Google Cloud Dataproc

Apache open source technologies like YARN, MapReduce programming framework, and HBase database are suitable to deploy for on-premise big data systems. Explore and get hands-on in all these technologies with this course.

Explained below are various ways how Big Data is proving beneficial for all sorts of businesses

Cost Optimization
Through the analysis of large amounts of data, Big Data tools can identify efficient and cost-saving ways of doing business.

The logistics industry can be a great example of this. Product returns cost 1.5 times greater than that of actual shipping costs and eat away the industry's profits. Big Data Analytics now allows them to save money by predicting the likelihood of product returns as well as the specific products that are most likely to be returned.

Improve efficiency
Big data is allowing businesses to reduce outages and anticipate future demands. It's doing that by analyzing and assessing production, buying behavior, feedback, returns, customer preferences, pain points, etc.

Employ competitive pricing
Big Data Analytics allow businesses to implement competitive pricing based on real-time monitoring of the market and their competitors to maximize their profits. Further, businesses can even automate this pricing process to maintain consistency and eliminate manual errors.

Personalize offerings, retain customers and boost sales
A business is bound to prosper if it can cater to the specific needs of various customer segments. Big Data allows businesses to personalize their products or services, examine trends, and deliver the precise products and services by gathering and analyzing the digital footprints of customers.

Product Development
With the help of Big Data, Companies like Netflix and Procter & Gamble anticipate customer demand. To accomplish that, a two step data-process gets followed.
  • Classify key attributes of past and current products or services, and
  • Model the relationship between those attributes and the commercial success of the offerings.
Based on this, these companies build predictive models for new products and services.

Hire the right employees
Data-based tools allow recruiters to weed out and hire the right employees by scanning candidate's resumes and LinkedIn profiles for keywords with that of the job description.

To have a great career and earn a handsome salary! You want that, right?

The investment in the big data domain and the demand for data professions continue to soar as big data acts as a foundation for machine learning (and AI) projects, predictive modeling, and other advanced analytical applications.

IDC (International Data Corporation), in its publication “Semiannual Big Data and Analytics Spending Guide” indicated that Big Data-related hardware, software, and services are expected to maintain a compound annual growth rate (CAGR) of 11.9% through 2020, with total revenue of more than $210 billion.

Listed below are a few reasons why you should learn Big Data:

  • From Healthcare to finance, industries from all sorts of domains are looking to apply Big Data to gain a competitive edge, save cost and personalize their marketing campaigns, creating huge demands for data-skilled professionals. But there is a huge gap between demand and supply, with only a few skilled workers to meet the ever-growing demand.
  • Big Data is a lucrative career option with an abundance of high-paying job opportunities. Data Science Analytics professionals with MapReduce skills are earning $115,907 a year on average (Forbes). In India, analytics professionals get paid on average 50% more than their counterparts in other IT-based professions, with the average salary of a big data engineer at Rs 729,359/yr (Glassdoor).
  • The field offers numerous job profiles to choose from, in terms of the domain as well as the nature of the job. You can become an associate analyst or Big Data solution Architect or choose from various other job titles.
  • Learning Big Data will expand your problem-solving skills, a skill that's not useful for the professional world, but also in everyday life as well. Develop a better understanding of systems and tools that you interact with on a daily basis.
  • You can generate some side income after learning Big Data (with things like Freelancing, an informative blog/YouTube channel, selling a data-based course, or creating something innovative with your data knowledge)

No matter what your background is, you can take this AI course provided you're passionate about numbers, and love challenging problems.
But your journey to becoming a data professional scientist would be much easier if:

  • - You have a background in analytical disciplines such as mathematics, physics, computer science, or engineering.
  • - You love coding and have a basic understanding of programming languages (not necessary as we do provide crash courses in required programming languages).
  • - You are patient enough to keep working on the project even when it seems to have hit a roadblock

- Most comprehensive and well-structured course covering all the basic concepts, to allow you to develop a solid machine learning foundation.

- Certified Trainers with extensive real-time experience in the Machine Learning domain and an immense passion for teaching.

- Top-notch course with a perfect blend of theory, case studies, and capstone projects with an assignment for every taught concept.

- 100% Job Placement assistance. Frequent mock interviews to evaluate and improve your knowledge and expertise. Facilitation of interviews with various top companies. Help in building a great resume, optimizing LinkedIn profile, and improving your marketability.

Listed below are some of the leading Data-based career options you can break into after completing the Big Data course:

  • Big Data Analytics Business Consultant
  • Big Data Analytics Architect
  • Big Data Solution Architect
  • Analytics Associate
  • Database Administrator
  • Database Developer
  • Data Scientist
  • Big Data Engineer
  • Data Modeler
  • Business Intelligence and Analytics Consultant
  • Metrics and Analytics Specialist

Course Circulum

  • What is Big Data
  • Need and significance of innovative technologies
  • 3 Vs (Characteristics)
  • Forms of Data & Sources
  • Various Hadoop Distributions
  • Significance of HDFS in Hadoop
  • HDFS Features
  • Daemons of Hadoop and functionalities
  • Data Storage in HDFS
  • Accessing HDFS
  • Data Flow
  • Hadoop Archives
  • Introduction to MapReduce
  • MapReduce Architecture
  • MapReduce Programming Model
  • MapReduce Algorithm and Phases
  • Data Types
  • Input Splits and Records
  • Basic MapReduce Program
  • Introduction to Apache Pig
  • MapReduce Vs. Apache Pig
  • SQL Vs. Apache Pig
  • Different Data types in Apache Pig
  • Modes of Execution in Apache Pig
  • Execution Mechanism
  • Data Processing Operators
  • How to write a simple PIG Script
  • UDFs in PIG
  • The Metastore
  • Comparison with Traditional Databases
  • HiveQL
  • Tables
  • Querying Data
  • User-Defined Functions
  • Introduction to HBase
  • HBase Vs HDFS
  • Use Cases
  • Basics Concepts
  • HBase Architecture
  • Zookeeper
  • Clients
  • MapReduce integration
  • MapReduce over HBase
  • Schema definition
  • Basics of MySQL database
  • Install and Configuration
  • Load/Update/Delete – DML transactions on database
  • Import and Export data
  • Other MySQL functions
  • Introduction to Sqoop
  • Sqoop Architecture and Internals
  • MySQL client and server installation
  • How to connect relational database using Sqoop
  • Sqoop Commands
  • overview
  • Installation
  • The basic syntax
  • Data types
  • Programming practice
  • Basics of Python
  • Variables, expressions and statements
  • Functions, Structures, Strings
  • Strings and Files
  • Basic visualizations
  • Basic Statistics
  • Spark Architecture (Eco System)
  • SparkR setup
  • Pyspark and Spark-Shell (scala) interfaces
  • Spark SQL
  • Spark MLLib
  • Spark Streaming

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FAQs

Hadoop is an open-source software framework and procedure (free for anyone to use or modify, with a few exceptions) for storing and developing data processing applications that get executed in a distributed computing environment. What makes it so popular is its massive storage, enormous processing power, and the capability to handle virtually limitless concurrent tasks or jobs. Explore and learn Hadoop fundamentals and its applications with real projects in this comprehensive course.

Apache Spark is an open-source, distributed processing system designed to meet big data workloads. Fast, flexible, and developer-friendly, it provides native bindings for the Java, Scala, Python, and R programming languages, and is the leading platform for large-scale SQL, batch processing, stream processing, graph processing, and machine learning.

Scala is a high-level language providing support for both object-oriented and functional programming. Designed to address criticism of Java, it allows you to build high-performance systems with its JVM and JavaScript runtimes. Enroll in this course to learn Scala fundamentals and its applications with real projects.

No! Just a basic laptop should be sufficient for most of your personal projects as we'll leverage the cloud for most of the works. But you should be able to install applications and utilize a virtual machine to complete the hands-on assignments.

Here are a few datasets sources you can rely on:

  • Kaggle
  • Socrata
  • Non-profit research group websites

We'll provide you with more sources for getting datasets in this comprehensive Big Data course.

Our team has compiled a list of the best big data resources including study materials, cheat sheets, data sets, videos, which you get access to upon enrolling in the course.

Kode Campus has its dedicated Placement Assistance Team(PAT). The team helps you in all the aspects of securing your dream job, from improving your marketability to conducting mock interviews.

NO! Our assistance program will only maximize your chances of landing a successful job as the final selection decision is always dependent on the recruiter.

This Big Data course is the most comprehensive, relevant, and contemporary, meeting all the present demands of the Data Industry. Don't expect it to be some repurposed or repackaged content of redundant archaic course materials.
What's more is that we continually upgrade the content of this course with the changes in technology, trends, and demands to provide you the best learning resource.

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Jonathan Campbell

  • 72 Videos
  • 102 Lectures
  • Exp. 4 Year

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4.2 out of 5.0
5 Star 85%
4 Star 75%
3 Star 53%
1 Star 20%

Item Reviews

Manoj Tiwari27 Oct 2019

4.9

"This Big Data course is not only well-organized, but it's easy to follow too. It covers all the important Big Data technologies. I liked the trainer who was clear and concise in explaining all the relevant technologies. I especially liked how easily complex topics of Pyspark and Scala interfaces were explained. Strongly recommended."

Sashank Shishodia 2 Nov May 2019

3.7

"For a beginner like me, this course met all the expectations. The course provided "hands-on" in almost all the data-based technologies. After starting the course, I realized how I was using a different version of SandBox when compared to the training program. But with the Kode Campus's incredible support, I was quickly able to find out the solution. Had a great journey and experience here."

Zeba Zaaman10 Nov 2019

4.2

" As per my friend's recommendation, I decided to opt for a Big Data Course at Kode Campus. Had high expectations and I can say that they were met. I especially loved how the trainer used simple and real-time analogies to help me understand the core concepts."

P Durga 12 Nov May 2019

4

"Keeping all things aside, what captivated me most was the trainer's patience and efforts in explaining topics multiple times. Not to mention that the trainer has vast knowledge on topics and an excellent way of delivering. Just go for the course!"

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BigData Training
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Course Features

  • Student Enrolled:1740
  • lectures:10
  • Quizzes:4
  • Duration:60 hours
  • Skill Level:Beginner
  • Language:English
  • Assessment:Yes
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