Big Data Analyst Salary

Learn about how much you can earn as a Big Data Analyst

Find the answer to how much does a Big Data Analyst earn in lieu of importing, cleaning, converting, and analyzing a huge amount of data? Learn about the salary of Big Data Analysts in various countries. Moreover, you’ll be able to understand how much you can earn as a Big Data Analyst in terms of experience and then finally identify the companies offering the best salary to the Big Data Analysts across the globe.

Certification FAQs

Big Data Analytics Course

Big Data refers to large amounts of structured and unstructured data that can be analysed using traditional databases and multiple software techniques to reveal patterns that can be used to meet business objectives. Analyses of such large amounts of unstructured data helps in understanding and predicting human behaviour and solving complex business problems. Big Data is huge and consists of complex data sets that traditional data processing software cannot manage.

Big data contains in it patterns and information which when mined can given an insight into customer behavior and preferences. This leads to new innovations, satisfied customers, smoother operations and higher profits. Let’s see the attributes that are making Big Data so popular today:

  • Reduced Cost:

With the help of Big Data technologies like Hadoop and cloud-based analysis, organizations find out more efficient ways of doing business and bring cost advantages when it comes to storing huge amounts of data.

  • Quick & Improved Decision Making:

With the help of Hadoop and in-memory analytics, organizations can quickly analyze the data and will be able to make decisions based on their learnings.

  •  Latest products and services:

Big Data helps businesses gauge customer requirements and preferences, based on which they can develop new products or improve existing products to meet customer needs.

Tons of data are generated every second from our activities on social networks, the internet, or even from traditional business systems. This data generated from various sources is very complex and unstructured, and requires analysis to make it useful.

Data Analytics technologies provide organizations the means to analyse the data and draw conclusions, which further helps them improve their business models and create a better experience for their customers.  Big Data Analytics is an advanced form of analytics which involves several elements like statistical analysis, what-if, and predictive models. There are a lot of tools and applications that enable analysts and data scientists to analyse different forms of data that cannot be handled by the usual BI applications.

The difference between Big Data and Big Data analytics is that the former is a dataset that is not refined and is unstructured and is collected from various sources. Big Data Analytics, on the other hand, is clean and clear data that is easier to access and more concise.

To elaborate, Data Analytics is more accurate and focused than Big Data because instead of gathering dumps of unstructured data, analysts have a specified goal and sort through data to look for solutions to specific problems. Whereas Big Data is just a collection of a huge volume of data that requires a lot of filtering to derive any sort of usage or insight from it.  Additionally, another notable difference between them is that Big Data employs more complex tech tools to handle data, but Analytics uses statistics and predictive modelling with simpler tools.

Big Data Analytics tools include:

  1. Tableau Public
  2. OpenRefine
  3. KNIME
  4. RapidMiner
  5. Google Fusion Tables
  6. NodeXL
  7. Wolfram Alpha
  8. Google Search Operators
  9. Solver
  10. Dataiku DSS

There are 7 widely used Big data techniques:

  1. Association rule learning
  2. Classification tree analysis
  3. Genetic algorithms
  4. Machine learning
  5. Regression analysis
  6. Sentiment analysis
  7. Social network analysis

There are several techniques that are used for Big Data analysis. Some of them are:

  1. A/B Testing: It is a technique in which a control group is compared with a variety of test groups in order to determine what treatments (i.e., changes) will improve a given objective variable, such as say, marketing response rate. This technique is also known as split testing or bucket testing.
  2. Association rule learning: This is a set of techniques for discovering interesting relationships, i.e., “association rules,” among variables in large databases. These techniques consist of a variety of algorithms to generate and test possible rules. One application is market basket analysis, in which a retailer can determine which products are frequently bought together and use this information for marketing purposes (a commonly cited example is the discovery that many supermarket shoppers who buy diapers also tend to buy beer).
  3. Classification: A set of techniques to identify the categories to which new data points belong, based on a training set containing data points that have already been categorized. One application is the prediction of segment-specific customer behaviour (e.g., buying decisions, churn rate, consumption rate) where there is a clear hypothesis or objective outcome.
  4. Cluster analysis: A statistical method for classifying objects that split a diverse group into smaller groups of similar objects, whose characteristics of similarity are not known in advance. An example of cluster analysis is segmenting consumers into self-similar groups for targeted marketing.
  5. Crowdsourcing: A technique for collecting data submitted by a large group of people or community (i.e., the “crowd”) through an open call, usually through networked media such as the Web.
  6. Data fusion and data integration: A set of techniques that integrate and analyze data from multiple sources in order to develop insights in ways that are more efficient and potentially more accurate than if they were developed by analyzing a single source of data.
  7. Data mining: A set of techniques to extract patterns from large datasets by combining methods from statistics and machine learning with database management. These techniques include association rule learning, cluster analysis, classification, and regression.

Big Data comprises of specific attributes in the form of 4 V’s. They are:

  1. Volume
  2. Variety
  3. Velocity
  4. Veracity

Big data is present in 3 forms:

  • Structured

Structured data is the data that can be processed, stored, and retrieved in a preset form. 

  • Unstructured

Unstructured data is the data that lacks any specific structure.

  • Semi-structured

Semi-structured data is the data that is the combination of both the formats i.e. structured and semi-structured.

Data is basically the quantities, characters, or symbols on which operations are performed by a computer, which may be stored, transmitted or recorded. Big Data is also data but with a large size. Big Data is a term used to describe a collection of data that is large in size and may grow exponentially with time. Big data sets are so large and complex that none of the traditional data management tools are able to store or process it efficiently. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.

Data Warehousing is extracting data from one or more homogeneous or heterogeneous data sources, transforming the data and loading that into a data repository to do data analysis which further helps in taking better decisions to improve one’s performance and in reporting.

Big data refers to volume, variety, and velocity of the data. How big the data is, the speed at which it is coming and the variety of data, determine the so-called “Big Data”.  The 3 V’s of big data was articulated by industry analyst Doug Laney in the early 2000s.

Organizations want a big data solution because in many corporations there are many operations associated with a large amount of data. And in those corporations, if data contains much valuable information then it can lead to better decisions, which subsequently leads to more revenue, more profit, and more clients.

Organizations need a data warehouse in order to access reliable data and make informed decisions. Big data solution is a technology whereas data warehousing is an architecture.

Top big data technologies are divided into 4 fields which are classified as follows:

  • Data Storage
  • Data Mining
  • Data Analysis
  • Data Visualization


Data storage: For big data projects, cloud-based storage tools are vital to maximizing the amount of information you can store. Cloud storage options let you store data in a secure and accessible fashion, for ease of use.

Data mining: Once you have your data stored, you will need some tools to find the information you want to analyze or visualize. This tool helps you to extract the data you need without the hassle of manually trawling through it all (a task that's impossible for humans to do anyway if you hold thousands or more records).

Data analysis: Got the data you need? Now it's time to find the most powerful tools to help you analyze it in order to glean key insights into your business, your customers or the wider world.

Data Visualization: Not everyone is adept at taking key insights from a list of data points or understanding what they mean. The best way to present your data is by turning it into visuals that everyone can comprehend easily.

Big data analytics is the complex process of examining large and varied data sets, or big data, to uncover information -- such as hidden patterns, unknown correlations, market trends and customer preferences -- that can help organizations make informed business decisions. On a broad scale, data analytics technologies and techniques provide a means to analyze data sets and draw conclusions about them, which helps organizations make informed business decisions. Business intelligence (BI) queries answer basic questions about business operations and performance.

Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.

Driven by specialized analytics systems and software, as well as high-powered computing systems, big data analytics offers various business benefits, including:

  • New revenue opportunities
  • More effective marketing
  • Better customer service
  • Improved operational efficiency
  • Competitive advantages over rivals

Use of Big Data

Big Data technology is used to store, retrieve and analyze large amounts of data to reveal hidden patterns, correlations, and other company-related insights. Big Data technology stacks help analyze data faster and help come up with better strategies to develop products that meet the customer’s requirements. Big Data analysis provides several advantages such as:

  1. Make correct business decisions
  2. Perform efficient operations
  3. Higher profits
  4. Ensure happy and satisfied customers

Big Data is used to discover hidden patterns, market trends and customer preferences. These in turn help organizations make informed business decisions, innovate products to meet customer preferences, cut costs, enhance efficiency and reduce time to market.

Data is the new oil and Big data is all about helping organizations beat competition and ensure market survival. In this volatile market where costs are hitting the roof and customers are volatile, it is important that businesses understand the pulse of the market and come up with innovations that meet the customers needs while also being cost, process and time efficient. The analyses of Big Data helps do all of that, and also helps in cost reduction and better decision making, which is why it has become indispensable to organizations.

Big Data analytics is indeed a revolution in the field of IT. It helps organizations to harness their data and use it to identify new opportunities and gain an insight to run their business efficiently. Companies can improve their strategies by keeping in mind customer preference. For example, Netflix Uses Big Data to Improve their Customer Experience.

Professionals who are experts in the field of big data analytics are in huge demand. High profile companies from all sectors hire Big Data Analysts to mine data for useful insights. Below are the list of Companies hiring Big Data Analysts

  • Amazon
  • EY
  • Accenture
  • Oracle
  • Wipro
  • Walmart Labs
  • JP Morgan Chase & Co

In Big Data lies the key to unlocking insights that help understand customer preferences and design business strategies. Big data helps a business to figure out what the customer wants and  create new experiences, services, and products to meet these demands. Using big data has been crucial for many leading companies to beat competition and emerge successful. Today, Big Data has changed the way that businesses market themselves and their products. Big data has helped companies innovate, reduce operational costs, increase profits and gain customer loyalty.

Learn Big Data Analytics

There are many ways to learn Big Data Analytics. You can learn through tutorials, blogs, articles, books, etc. But the best way to learn about Big Data Analytics is by attending a training. The training will equip you with the skills to make a career shift to a promising field.

Yes, you need to have a good understanding of programming languages to learn Big Data Analytics.

No, Big Data is very easy to learn. Especially if you are familiar with a programming language.

  • A high level of mathematical ability
  • Knowledge of Programming languages
  • Business Knowledge
  • Familiarity with frameworks such as Apache Spark, Apache Storm, etc.

Big Data Analytics Certification and Training

Here is a list of some of the best training institutes for Big Data Analytics:

  • Intellipaat
  • Mindmajix
  • KnowledgeHut
  • Mytectra
  • Udemy
  • Zeolearn

Among these, KnowledgeHut has been widely endorsed by industry experts. It is the best training institute to learn Big Data Analytics with hands-on and practical sessions.

To become a Big Data Analyst, you can take up the Big Data Analytics course. The course will help you understand the fundamentals and basics of Apache Hadoop and data ETL, ingestion, processing with Hadoop tools. Learn and understand the Pig framework and Hive framework. Moreover, you will get the opportunity to work on real-world projects as the course is curated by industry experts.

The following lists out the best training providers for Big Data Analytics :

  • Upgrad
  • Intellipaat
  • KnowledgeHut
  • Simplilearn
  • Edureka
  • EDX

If you have not added Big Data Analytics skills to your resume yet, then this is the best time to do so. Industry experts suggest KnowledgeHut as your training partner for the Big Data Analytics course. It provides you with the best possible theoretical knowledge along with hands-on training to face the industry. Build your concepts from scratch and assist your learning through a step-by-step guidance on tools and techniques.

The Big Data Analytics course completion certificate by KnowledgeHut has lifetime validity.

Career scope and Salary

Attending this Big Data Analytics training will help you in getting a job in the following ways:

  • It will increase the possibility of landing  highly-coveted roles and increase the likelihood of you getting hired.
  • It will make you eligible for various domains such as e-commerce, government, finance, healthcare, etc.
  • Obtaining certification for the same defines your credibility as it stands as a validation of your skills.
  • Enjoy an Increase in salary as your experience grows.
  • It helps you stay updated with the latest industry trends.
  • Will provide you with an improved career path.

Big Data Analytics expertise will benefit you in the following ways :

  • Helps you gain problem-solving skills
  • New opportunities for skilled professionals in a variety of industries like aviation, finance, e-commerce, etc.
  • You can select any career path such as project management, security, system architecture, banking etc.
  • Increase in salary as the experience grows.

According to IDC, worldwide spending on big data and analytics is growing at a compound annual growth rate (GAGR) of 11.9 percent, and revenues will likely total more than $210 billion by 2020. This growth will only increase in the coming years as more and more data gets generated and the need to analyse it for business benefits increases. This has led to an increased demand for data scientists, analysts, and data management experts. Moreover, according to IDC, big data staffing shortage is expected to expand from analysts and scientists to include architects as well as experts in data management. Hence gaining expertise in this domain is sure to help you reap rich dividends in the future.

By the end of this course, you will learn about Big Data and Big Data Challenges. You will also gain knowledge of the Hadoop Daemon Processes, HDFS, Hadoop Installation Modes, and HDFS, Hadoop Developer Tasks, Hadoop Ecosystems, Data Analytics using Pentaho as an ETL tool.

Enterprises have realised the value that Big data analyses provides to their business, which is why there is an increase in demand for Big Data Analytics professionals. The following states the reasons why big data is in high demand:

  • Influences project outcomes: Lack of Big Data may cause failure in project management, which might further lead to losses or pitfalls. Big Data analysis deals with trends, patterns, and various other parameters related to the project being worked on. This way, an unexpected variable can be overcome, hence improving project performance.
  • Predictive Analytics: To make predictions for the future, the predictive analysis uses various techniques from data mining, statistics, modelling, machine learning, and AI. As per the patterns found in historical and transactional data, risks can be identified along with the various opportunities for the future.
  • User experience: Big Data is in high demand because it helps enhance customer experience. Taking data from call logs, social media channels, customer feedback, etc. allows businesses to improve their products as well as customer experience.

Big Data Analysts could earn a gross pay of $84,955 per year.

Note: All information mentioned is based on data obtained in September 2019.

Today, Big Data Analysts are in great demand. Industries like Professional Services, Manufacturing, IT, Retail and Finance hire individuals who are experts in Big Data technology. Here is a list of some of the companies hiring Big Data professionals:

  1. Accenture Analytics
  2. Fractal Analytics
  3. Mu Sigma analytics
  4. Cartesian Consulting
  5. Hewlett Packard Enterprise
  6. Quest HR
  7. IFINTALENT GLOBAL Pvt Ltd
  8. Data Analytics Company

How Much Does A Big Data Analyst Earn?

Salaries of Big Data Analysts across the globe

When the entire world is dependent on data, the Big Data Analyst profile plays a pivotal role in driving various businesses towards success. Now, let’s compare the salary of a Big Data Analyst in various countries in the following chart:


Country Name
Currency
Salary (per annum)
India
Rupees (INR)
4,14,628
USA
Dollar ($)
59,546
UK
Pound Sterling (£)
26,179
Canada
Canadian Dollar (C$)
55,004


The above data can be compared more clearly through the following figure in order to compare the country-wise earning of a Big Data Analyst:


(Fig: 1)

The above figure shows us that a Big Data Analyst based in the US earns a higher salary compared to the counterparts based in India, UK, and Canada. Now, let’s try to compare the salary earned by the Big Data Analysts across the globe based on experience through the following chart:

Country Name
Experience
Currency
Salary (per annum)
India
Entry-level
Rupees (INR)
3,21,470
Mid-career
6,12,850
Experienced
9,97,193
USA
Entry-level
Dollar ($)
53,958
Mid-career
66,347
Experienced
68,510
UK Entry-level
Pound Sterling (£)
23,982
Mid-career
30,226
Experienced
33,202
Canada
Entry-level
Canadian Dollar (C$)
49,325
Mid-career
64,292
Experienced
69,216

Now, let’s try to analyze the above data with the help of the following figure:

(Fig: 2)

In Fig:2, we can draw a clear comparison between the experience-wise salary that a Big Data Analyst earns in India, USA, UK, and Canada. The figure makes it clear that the highest salary for the profile is earned in the US by the experienced Big Data Analysts. Moreover, the above chart further enables you to make a clear distinction between entry-level, mid-career and experienced Big Data Analyst.

Company-wise salary

So, that was about the salaries that a Big Data Analyst earns based on the location and experience. But the major question that arises here is, which are the companies that pay the highest salaries in the above countries. The following table will help you to check the salary paid by the top companies in India:

Company Name
Salary (in Rupees per annum)
Tata Consultancy Services
4,97,336
Cognizant Technology Solutions
5,21,638
Accenture
5,34,210
Tech Mahindra
4,62,673
IBM
4,30,624
Amazon
5,84,937


Fig: 1 & 2 give us a clear picture of the fact that the earning of a Big Data Analyst is more in the US compared to other nations.  Now let’s take a look at the salary paid to Big Data Analysts by companies based in the US.

Company Name
Salary (in US Dollars per annum)
CITI
104,000
HP Inc.
77,000
Auto Club of Southern California
69,000
JB Micro
110,000


Now, let’s check out the salaries paid by companies to Big Data Analysts based in United Kingdoms.

Company Name
Salary (in Pound Sterlings per annum)
Bloomberg L.P.
42,515
Her Majesty’s Revenue & Customs
32,015
Sport England
46,296
Sky
41,786


The following table will help you to identify the salary paid by the companies based in Canada to the Big Data Analysts while enabling you to compare the same with the other countries:

Company Name
Salary (in Canadian Dollars per annum)
TD
67,699
Scotiabank
59,938
Aimia
67,663
RBC
56,545
Rogers Communications
55,000

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Why Learn Big Data Analytics?

Big Data analytics is the process of gathering, managing, and analyzing large sets of data (Big Data) to uncover patterns and other useful information.  These patterns are a minefield of information and analysing them provide several insights that can be used by organizations to make business decisions. This analysis is essential for large organizations like Facebook who manage over a billion users every day, and use the data collected to help provide a better user experience.  

Similarly, LinkedIn provides its users with millions of personalized suggestions on a regular basis. LinkedIn does it with the help of components like HDFS features and MapReduce in Big Data Analytics. Big Data has thus become an indispensable part of technology and our lives; and big data analyses provides solutions that are quick and require reduced effort to generate. It is no wonder then that big data has spread like wild fire and so have the solutions for its analyses.

According to a recent McKinsey report the demand for ‘Big Data’ professionals could outpace the supply by 50 to 60 percent in the coming years, and U.S.-based companies will be looking to hire over 1.5 million managers and big data analysts with expertise on how big data can be applied. Big Data investments have also sky rocketed, with several top profile companies spending their resources on Big Data related research and hiring big data analysts to change their technology landscape.

An IBM listing states that the demand for data science and analytics is expected to grow from 3,64,000 to nearly 27,20,000 by 2020. According to a recent study done by Forrester, companies only analyze about 12% of the data at their disposal. 88% of the data is ignored, mainly due to the lack of analytics and repressive data silos. Imagine the market share of big data if all companies start analysing 100% of the data available to them. Hence the conclusion is that there is no time like now to start investing in a career in big data. It is paramount that developers upskill themselves with analytical skills and get ready to take a share of the big data career pie.

Benefits

Big data analytics certification is growing in demand and is most relevant in data science today than in other fields. The field of data analytics is new and there are not enough professionals with the right skills. Hence, the credibility of big data analytics certification promises many growth opportunities for organizations as well as individuals in the booming field of data science.

Many big companies like Google, Apple, Adobe, and so on are investing in Big Data. Let’s take a look at the benefits of Big Data that organizations and individuals are experiencing:

  • An individual with Big Data analytics skills can make decisions more effectively
  • Based on the IBM survey, the Big Data analytics job market is expected to grow by 15% in the year 2020
  • According to Glassdoor, Big Data Engineers are earning an average of $116,591 per annum
  • An individual with Big Data skills can earn a better salary, good career growth, and more chances of getting hired by top companies
  • Big Data allows organizations to understand consumer needs and make informed decisions
  • Big data tools can identify efficient ways of doing business through sentiment analysis
  • Businesses can get ahead of the competition by better understanding market conditions
  • With Big Data Analytics, organizations understand ongoing trends and develop products accordingly.

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What you will learn

Who should attend the Apache Storm course?

  • Data Architects
  • Data Scientists
  • Developers
  • Data Analysts
  • BI Analysts
  • BI Developers
  • SAS Developers
  • Project Managers
  • Mainframe and Analytics Professionals
  • Professionals who want to acquire knowledge on Big Data
Prerequisites

There are no specific prerequisites required to learn Big Data.

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the latest training!

Learn through Doing

Learn theory backed by practical case studies, exercises, and coding practice. Get skills and knowledge that can be applied effectively.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objective:

You will get introduced to real-world problems with Big data and will learn how to solve those problems with state-of-the-art tools. Understand how Hadoop offers solutions to traditional processing with its outstanding features. You will get to Know Hadoop background and different distributions of Hadoop available in the market. Prepare the Unix Box for the training.

Topics:

1.1 Big Data Introduction

  • What is Big Data
  • Data Analytics
  • Big Data Challenges
  • Technologies supported by big data

1.2 Hadoop Introduction

  • What is Hadoop?
  • History of Hadoop
  • Basic Concepts
  • Future of Hadoop
  • The Hadoop Distributed File System
  • Anatomy of a Hadoop Cluster
  • Breakthroughs of Hadoop
  • Hadoop Distributions:
  • Apache Hadoop
  • Cloudera Hadoop
  • Horton Networks Hadoop
  • MapR Hadoop

Hands On:

Installation of Virtual Machine using VMPlayer on Host Machine. And work with Some basics Unix Commands needs for Hadoop.

Learning Objective:

You will learn what are the different Daemons and their functionality at a high level.

Topics:

  • Name Node
  • Data Node
  • Secondary Name Node
  • Job Tracker
  • Task Tracker

Hands On:

Creates a Unix Shell Script to run all the deamons at one time.

Starting HDFS and MR separately.

Learning Objective:

You will get to know how to Write and Read files in HDFS. Understand how Name Node, Data Node and Secondary Name Node take part in HDFS Architecture. You will also know different ways of Accessing HDFS data.

Topics:

  • Blocks and Input Splits
  • Data Replication
  • Hadoop Rack Awareness
  • Cluster Architecture and Block Placement
  • Accessing HDFS
  • JAVA Approach
  • CLI Approach

Hands On:

Writes a shell Script which write and read Files in HDFS. Changes Replication factor at three levels. Use Java for working with HDFS.

Writes different HDFS Commands and also Admin Commands.

Learning Objective:

You will learn different modes of Hadoop, understand Pseudo Mode from scratch and work with Configuration. You will learn functionality of different HDFS operation and Visual Representation of HDFS Read and Write actions with their Daemons Namenode and Data Node.

Topics:

  • Local Mode
  • Pseudo-distributed Mode
  • Fully distributed mode
  • Pseudo Mode installation and configurations
  • HDFS basic file operations

Hands On:Install Virtual Box Manager and install Hadoop in Pseudo distributed mode. Changes the different Configuration files required for Pseudo Distributed mode. Performs different File Operations on HDFS.

Learning Objective:

Understand different Phases in Map Reduce including Map, Shuffling, Sorting and Reduce Phases.Get a deep understanding of Life Cycle of MR in YARN submission. Learn about Distributed Cache concept in detail with examples.

Write Wordcount MR Program and monitor the Job using Job Tracker and YARN Console. Also learn about more use cases.

Topics:

  • Basic API Concepts
  • The Driver Class
  • The Mapper Class
  • The Reducer Class
  • The Combiner Class
  • The Partitioner Class
  • Examining a Sample MapReduce Program with several examples
  • Hadoop's Streaming API

Hands On:

  • Learn about writing MR job from scratch, writing different Logics in Mapper and Reducer and submitting the MR Job in Standalone and Distributed mode.
  • Also learn about writing Word Count MR job, Calculating Average Salary of employee who meets certain conditions and Sales Calculation using MR.

6.1 PIG

Learning Objective:

Understand the importance of Pig in Big Data World, PIG architecture and PIG Latin commands for doing different complex operation on Relations, and also Pig UDF and Aggregation functions with piggy bank library. Learn how to pass dynamic arguments to Pig Scripts.

Topics

  • PIG concepts
  • Install and configure PIG on a cluster
  • PIG Vs MapReduce and SQL
  • Write sample PIG Latin scripts
  • Modes of running PIG
  • PIG UDFs.

Hands On:

Login to Pig Grunt shell to issue Pig Latin commands in different Execution modes. Different ways of loading and transformation on Pig relations lazily. Registering UDF in grunt shell and perform Replicated Join Operations

6.2 HIVE

Learning Objective:

Understand importance of Hive in Big Data World. Different ways of configuring HIVE Metastore. Learn different types of tables in hive. Learn how to optimize hive jobs using Partitioning and Bucketing and Passing dynamic Arguments to Hive scripts. You will get an understanding of Joins,UDFS,Views etc.

Topics:

  • Hive concepts
  • Hive architecture
  • Installing and configuring HIVE
  • Managed tables and external tables
  • Joins in HIVE
  • Multiple ways of inserting data in HIVE tables
  • CTAS, views, alter tables
  • User defined functions in HIVE
  • Hive UDF

Hands On:

    Executes Hive Queries in different Modes. Creates Internal and External tables. Perform Query Optimization by creating tables with Partition and Bucketing Concepts. Run System defined and User Define Functions including Explode and Windows Functions.

6.3 SQOOP

Learning Objectives:

Learn how to import normally and Incrementally data from RDBMS to HDFS and HIVE tables, and also learn how to export the data from HDFS and HIVE table to RDBMS.Learns Architecture of Sqoop Import and Export.

Topics:

  • SQOOP concepts
  • SQOOP architecture
  • Install and configure SQOOP
  • Connecting to RDBMS
  • Internal mechanism of import/export
  • Import data from Oracle/MySQL to HIVE
  • Export data to Oracle/MySQL
  • Other SQOOP commands.

Hands On:

Triggers Shell script to call Sqoop import and Export Commands. Learn to automate Sqoop Incremental imports with entering the last value of the appended Column. Run Sqoop export from HIVE table directly to RDBMS.


6.4 HBASE

Learning Objectives:

Understand different types of NOSQL databases and CAP theorem. Learn different DDL and CRUD operations of HBASE. Understand Hbase Architecture and Zookeeper Importance in managing HBase. Learns Hbase Column Family optimization and client Side Buffering.

Topics:

  • HBASE concepts
  • ZOOKEEPER concepts
  • HBASE and Region server architecture
  • File storage architecture
  • NoSQL vs SQL
  • Defining Schema and basic operations
  • DDLs
  • DMLs
  • HBASE use cases

Hands On:

Create HBASE tables using Shell and perform CRUD operations with JAVA API. Change the column family properties and also perform sharding process. Also create tables with multiple splits to improve the performance of HBASE query.


6.5 OOZIE

Learning Objectives:

Understand Oozie Architecture and monitor Oozie Workflow using Oozie. Understand how Coordinator and Bundles work along with Workflow in Oozie. Also learn Oozie Commands to submit, Monitor and Kill the Workflow.

Topics:

  • OOZIE concepts
  • OOZIE architecture
  • Workflow engine
  • Job coordinator
  • Installing and configuring OOZIE
  • HPDL and XML for creating Workflows
  • Nodes in OOZIE
  • Action nodes and Control nodes
  • Accessing OOZIE jobs through CLI, and web console
  • Develop and run sample workflows in OOZIE
  • Run MapReduce programs
  • Run HIVE scripts/jobs.

Hands on:

Create the Workflow to incremental Imports of Sqoop. Create the Workflow for Pig, Hive and Sqoop Exports. And also execute Coordinator to Schedule the Workflows.


6.6 FLUME

Learning Objectives:

Understand Flume Architecture and its components Source, Channel and Sinks. Configure flume with Socket, File Sources and HDFS and Hbase Sink. Understand Fan In and Fan Out Architecture.

Topics:

  • FLUME Concepts
  • FLUME Architecture
  • Installation and configurations
  • Executing FLUME jobs

Hands on:

Create flume Configurations files and configure with Different Source and Sinks.Stream Twitter Data and create hive table.

Learning Objective:You will learn Pentaho Big Data Best Practices, Guidelines, and Techniques documents.

Topics:

  • Data Analytics using Pentaho as an ETL tool
  • Big Data Integration with Zero Coding Required

Hands on:You will use Pentaho as ETL tool for data analytics.

Learning Objective:

You will see different Integrations among hadoop ecosystem in a Data engineering Flow. Also understand how important it is to create a flow for ETL process.

Topics:

  • MapReduce and HIVE integration
  • MapReduce and HBASE integration
  • Java and HIVE integration
  • HIVE - HBASE Integration

Hands On:Uses Storage Handlers for integrating HIVE and HBASE. Integrates HIVE and PIG as well.

Project

Recommendation Engine

Creating Recommendation system for Online Video Channels with the Historical Data using Cubing Comparing with the Benchmark Values.

Sentimental Analytics

Creating Sentimental Analytics by Downloading the Tweets from Twitter and Feeds the trending data to the Application.

Clickstream Analytics

Performing Clickstream Analytics on the Application data and engaging Customers by Customizing the Articles to the Customer for a UK Web Based Channel.

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FAQs

Big Data Analytics Course

There are no prerequisites for attending this course.

Big Data analytics is important for companies and individuals to utilise data in the most efficient manner to cut costs. Tools such as Hadoop can help identify new sources of Data to help businesses to make quick decisions, understand market trends and develop new products

  • Freshers who would like to build their career in the world of data (this is an introductory course).
  • Those who want to learn Hadoop and Spark
  • Software Developers and Architects
  • Analytics Professionals
  • Senior IT professionals
  • Testing and Mainframe professionals
  • Data Management Professionals
  • Business Intelligence Professionals
  • Project Managers
  • Aspiring Data Scientists
  • Graduates looking to build a career in Big Data Analytics

RAM: Minimum - 8 GB Recommended - 16GB DDR4

Hard Disk Space: 40 GB Recommended - 256 GB

Processor: i3 and above

  • Understanding the Core Concepts of Hadoop which includes Hadoop Distributed File System (HDFS) and Map-Reduce(MR)
  • Understanding NO-SQL databases like HBASE and CASSANDRA.
  • Understanding Hadoop Ecosystem like HIVE, PIG, SQOOP and FLUME
  • Acquiring knowledge in other aspects like scheduling Hadoop jobs using Python, R, Ruby. Etc.
  • Developing Batch Analytics applications for UK Web-Based News Channels to Upcast the News and Engaging customer with the Customized Recommendations.
  • Integrating Clickstream and Sentimental Analytics to the UK Web Based News Channel.
  • Hadoop course is divided into five phases:Ingestion Phase(FLUME AND SQOOP), Storage Phase(HDFS and HBASE), Processing Phase(MR, HIVE, PIG, and SPARK), Cluster Management(Standalone and YARN) and Integrations(HCATALOG, ZOOKEEPER and OOZIE)
  • Accelerated career growth.
  • Increased pay package due to Hadoop skills.

The Big Data Analytics training does not have any restrictions although participants would benefit slightly if they’re familiar with basic programming languages.

Workshop Experience

All of the training programs conducted by us are interactive in nature and fun to learn as a great amount of time is spent on hands-on practical training, use case discussions, and quizzes. An extensive set of collaborative tools and techniques are used by our trainers which will improve your online training experience.

The Big Data Analytics training conducted at KnowledgeHut is customized according to the preferences of the learner. The training is conducted in three ways:

Online Classroom training: You can learn from anywhere through the most preferred virtual live and interactive training

Self-paced learning: This way of learning will provide you lifetime access to high-quality, self-paced e-learning materials designed by our team of industry experts

Team/Corporate Training: In this type of training, a company can either pick an employee or entire team to take online or classroom training. Flexible pricing options, standard Learning Management System (LMS), and enterprise dashboard are the add-on features of this training. Moreover, you can customize your curriculum based on your learning needs and also get post-training support from the expert during your real-time project implementation.

The sessions that are conducted include 30 hours of live sessions, with 15 hours MCQs and 8 hours of Assignments and 20 hours of hands-on sessions.

Course Duration information:

Online training:

  • Duration of 15 sessions.
  • 2 hour per day.

Weekend training:

  • Duration of 5 Weekends.
  • Class held 2 days per week on Saturday, Sunday.
  • Note: Each session of 3 hours.

Yes, our lab facility at KnowledgeHut has the latest version of hardware and software and is very well-equipped. We provide Cloudlabs so that you can get a hands-on experience of the features of Big Data Analytics. Cloudlabs provides you with real-world scenarios can practice from anywhere around the globe. You will have an opportunity to have live hands-on coding sessions. Moreover, you will be given practice assignments to work on after your class.

Here at KnowledgeHut, we have Cloudlabs for all major categories like cloud computing, web development, and Data Science.

This Big Data Analytics training course have three projects, viz Recommendation Engine, Sentimental Analytics, Clickstream Analytics

  • Recommendation Engine: Creating Recommendation system for Online Video Channels with the Historical Data using Cubing Comparing with the Benchmark Values.
  • Sentimental Analytics: Creating Sentimental Analytics by Downloading the Tweets from Twitter and Feeds the trending data to the Application.
  • Clickstream Analytics: Performing Clickstream Analytics on the Application data and engaging Customers by Customizing the Articles to the Customer for a UK Web Based Channel
  • VMWare workstation or player [Depending on the OS]
  • The Image for Hadoop - 2.7.2 and Pig
  • Winscp or FileZilla [ Depending on OS ]
  • Putty or a simple console [ Depending on OS ]

The Learning Management System (LMS) provides you with everything that you need to complete your projects, such as the data points and problem statements. If you are still facing any problems, feel free to contact us.

After the completion of your course, you will be submitting your project to the trainer. The trainer will be evaluating your project. After a complete evaluation of the project and completion of your online exam, you will be certified a Big Data Analyst.

Online Experience

We provide our students with Environment/Server access for their systems. This ensures that every student experiences a real-time experience as it offers all the facilities required to get a detailed understanding of the course.

If you get any queries during the process or the course, you can reach out to our support team.

The trainer who will be conducting our Big Data Analytics certification has comprehensive experience in developing and delivering Big Data applications. He has years of experience in training professionals in Big Data. Our coaches are very motivating and encouraging, as well as provide a friendly learning environment for the students who are keen about learning and making a leap in their career.

Yes, you can attend a demo session before getting yourself enrolled for the Big Data Analytics training.

All our Online instructor-led training is an interactive session. Any point of time during the session you can unmute yourself and ask the doubts/ queries related to the course topics.

There are very few chances of you missing any of the Big Data Analytics training session at KnowledgeHut. But in case you miss any lecture, you have two options:

  • You can watch the online recording of the session
  • You can attend the missed class in any other live batch.

The online Apache Spark course recordings will be available to you with lifetime validity.

Yes, the students will be able to access the coursework anytime even after the completion of their course.

Opting for online training is more convenient than classroom training, adding quality to the training mode. Our online students will have someone to help them any time of the day, even after the class ends. This makes sure that people or students are meeting their end learning objectives. Moreover, we provide our learners with lifetime access to our updated course materials.

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

This will be live interactive training led by an instructor in a virtual classroom.

We have a team of dedicated professionals known for their keen enthusiasm. As long as you have a will to learn, our team will support you in every step. In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: https://www.knowledgehut.com/contact-us

We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, then we will facilitate a one-on-one discussion session with one of our trainers.

Finance Related

We accept the following payment options:

  • PayPal
  • American Express
  • Citrus
  • MasterCard
  • Visa

KnowledgeHut offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.

If you find it difficult to cope, you may discontinue within the first 48 hours of registration and avail a 100% refund (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding). Refunds will be processed within 30 days of receipt of a written request for refund. Learn more about our refund policy here.

Typically, KnowledgeHut’s training is exhaustive and the mentors will help you in understanding the concepts in-depth.

However, if you find it difficult to cope, you may discontinue and withdraw from the course right after the first session as well as avail 100% money back.  To learn more about the 100% refund policy, visit our Refund Policy.

Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary up to 50% of the course fees.

To avail scholarships, feel free to get in touch with us at the following link: https://www.knowledgehut.com/contact-us

The team shall send across the forms and instructions to you. Based on the responses and answers that we receive, the panel of experts takes a decision on the Grant. The entire process could take around 7 to 15 days

Yes, you can pay the course fee in installments. To avail, please get in touch with us at https://www.knowledgehut.com/contact-us. Our team will brief you on the process of installment process and the timeline for your case.

Mostly the installments vary from 2 to 3 but have to be fully paid before the completion of the course.

Visit the following page to register yourself for the Big Data Analytics Training: https://www.knowledgehut.com/big-data/big-data-analytics-training/schedule/

You can check the schedule of the Big Data Analytics Training by visiting the following link: https://www.knowledgehut.com/big-data/big-data-analytics-training/schedule/

We have a team of dedicated professionals known for their keen enthusiasm. As long as you have a will to learn, our team will support you in every step. In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: https://www.knowledgehut.com/contact-us

We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, then we will facilitate a one-on-one discussion session with one of our trainers.

Yes, there will be other participants for all the online public workshops and would be logging in from different locations. Learning with different people will be an added advantage for you which will help you fill the knowledge gap and increase your network.

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