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
1. Understand the Fundamentals
Learn the basics of Apache Hadoop & data ETL, ingestion, and processing with Hadoop tools.
2. Learn Pig framework
Understand how to join multiple data sets and analyze disparate data with the Pig framework.
3. Understand the Hive framework
How to organize data into tables, perform transformations, and simplify complex queries with Hive.
4. Perform Real-time analysis
How to perform real-time interactive analyses on huge data sets stored in HDFS using SQL with Impala.
5. Choose the best tool
How to pick the best tool in Hadoop, achieve interoperability, and manage repetitive workflows.
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.
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.
KnowledgeHut Experience
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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
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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.
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System ArchitectFAQs
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.
the high frequency of tools such as Hadoop can help identify new sources of Data to help businesses to make quick decisions, To understand market trends and develop new products
- Fresher’s Who Would like to build the carrier in the Distributed World, this is an introductory course.
- Laterals which want to learn Framework like SPARK, Hadoop Knowledge Will add benefit.
- 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 Up cast 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 are 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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