Demand for Analyzing Big Data with Hadoop
Deciphering raw data to come up with actionable insights lie at the crux of data analysis. According to the latest research, nearly 2.5 quintillion bytes of data is created, and the number is slowly edging upwards. The storage and processing power needed to handle these large volumes of data cannot be handled in an efficient manner with traditional frameworks and platforms. To understand and make sense of these large volumes or big data, there arose a need to explore distributed storages and parallel processing operations. Hadoop by Apache provides the much-needed power that is required to manage such situations.
Simply put, big data would be the problem and Hadoop would be one of the solutions leveraged to make sense of it. With the inclusion of a much needed HDFS component, the distributed storage problem is taken care of while the MapReduce component optimizes parallel data processing. According to Gartner data, nearly 26% of analysts are leveraging Hadoop in their daily tasks which makes it imperative to learn the platform and stay ahead of the curve. In addition to its ability to handle concurrent tasks, Hadoop is scalable and cost-effective as well, making the lives of analysts much easier than before.
Benefits of getting skills on Hadoop to analyze BigData
With most businesses facing a data deluge, the Hadoop platform helps in processing these large volumes in a rapid manner, thereby offering numerous benefits to both organizations and individuals.
Individual Benefits:
Undergoing training in Hadoop and big data is quite advantageous to the individual in this data-driven world:
- Enhance your career opportunities as more organizations work with big data and need professionals with experience in Hadoop
- Improve your salary with a new skill-set. According to ZipRecruiter, a Hadoop professional earns an average of $132,931
- Secure a position with leading companies like Google, Microsoft and Cisco with skills in Hadoop and big data
Organizational Benefits:
Training in Big Data and Hadoop has certain organizational benefits as well:
- Relative to other traditional solutions, Hadoop is quite cost-effective because of its seamless scaling capabilities across large volumes of data
- Expedited access to new data sources which allows an organization to reach its full potential
- Boosts the security of your system as Hadoop boasts of a feature called HBase security
Given the ease with which it allows you to make sense of huge volumes of data and leverage frameworks to transform the same into actionable insights, training and certification courses for Hadoop & Big Data are in great demand in the field of data science.
What You Will Learn
1. Learn the fundamentals
Understand what Big Data is and gain in-depth knowledge of Big Data Analytics concepts and tools.
2. Efficient data extraction
Learn to Process large data sets with Big Data tools to extract information from disparate sources.
3. MapReduce
Learn about MapReduce, Hadoop Distributed File System (HDFS), YARN, and how to write MapReduce code.
4. Debugging techniques
Learn best practices and considerations for Hadoop development as well as debugging techniques.
5. Hadoop frameworks
Learn how to use Hadoop frameworks like ApachePig™, ApacheHive™, Sqoop, Flume, among other projects.
6. Real-world analytics
Perform real-world analytics by learning advanced Hadoop API topics with an e-courseware.
Prerequisites
You don't need to meet any prerequisites to attend Big Data and Hadoop training, but you're recommended to have:
- Basic knowledge of programming languages.
- Basic knowledge of SQL and RDBMS.
Who should attend
- Data Architects
- Data Scientists
- Developers
- Data Analysts
- BI Analysts
- BI Developers
- SAS Developers
- Others who analyze Big Data in Hadoop environment
- Consultants who are actively involved in a Hadoop Project
- Java software engineers who develop Java MapReduce applications for Hadoop 2.0.
KnowledgeHut Experience
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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 objectives:
This module will introduce you to the various concepts of big data analytics, and the seven Vs of big data—Volume, Velocity, Veracity, Variety, Value, Vision, and Visualization. Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3.
Topics:
- Understanding Big Data
- Types of Big Data
- Difference between Traditional Data and Big Data
- Introduction to Hadoop
- Distributed Data Storage In Hadoop, HDFS and Hbase
- Hadoop Data processing Analyzing Services MapReduce and spark, Hive Pig and Storm
- Data Integration Tools in Hadoop
- Resource Management and cluster management Services
Hands-on: No hands-on
Learning Objectives:
Here you will learn the features in Hadoop 3.x and how it improves reliability and performance. Also, get introduced to MapReduce Framework and know the difference between MapReduce and YARN.
Topics:
- Need of Hadoop in Big Data
- Understanding Hadoop And Its Architecture
- The MapReduce Framework
- What is YARN?
- Understanding Big Data Components
- Monitoring, Management and Orchestration Components of Hadoop Ecosystem
- Different Distributions of Hadoop
- Installing Hadoop 3
Hands-on: Install Hadoop 3.x
Learning Objectives: Learn to install and configure a Hadoop Cluster.
Topics:
- Hortonworks sandbox installation & configuration
- Hadoop Configuration files
- Working with Hadoop services using Ambari
- Hadoop Daemons
- Browsing Hadoop UI consoles
- Basic Hadoop Shell commands
- Eclipse & winscp installation & configurations on VM
Hands-on: Install and configure eclipse on VM
Learning Objectives:
Learn about various components of the MapReduce framework, and the various patterns in the MapReduce paradigm, which can be used to design and develop MapReduce code to meet specific objectives.
Topics:
- Running a MapReduce application in MR2
- MapReduce Framework on YARN
- Fault tolerance in YARN
- Map, Reduce & Shuffle phases
- Understanding Mapper, Reducer & Driver classes
- Writing MapReduce WordCount program
- Executing & monitoring a Map Reduce job
Hands-on :Use case - Sales calculation using M/R
Learning Objectives:
Learn about Apache Spark and how to use it for big data analytics based on a batch processing model. Get to know the origin of DataFrames and how Spark SQL provides the SQL interface on top of DataFrame.
Topics:
- SparkSQL and DataFrames
- DataFrames and the SQL API
- DataFrame schema
- Datasets and encoders
- Loading and saving data
- Aggregations
- Joins
Hands-on:
Look at various APIs to create and manipulate DataFrames and dig deeper into the sophisticated features of aggregations, including groupBy, Window, rollup, and cubes. Also look at the concept of joining datasets and the various types of joins possible such as inner, outer, cross, and so on
Learning Objectives:
Understand the concepts of the stream-processing system, Spark Streaming, DStreams in Apache Spark, DStreams, DAG and DStream lineages, and transformations and actions.
Topics:
- A short introduction to streaming
- Spark Streaming
- Discretized Streams
- Stateful and stateless transformations
- Checkpointing
- Operating with other streaming platforms (such as Apache Kafka)
- Structured Streaming
Hands-on: Process Twitter tweets using Spark Streaming
Learning Objectives:
Learn to simplify Hadoop programming to create complex end-to-end Enterprise Big Data solutions with Pig.
Topics:
- Background of Pig
- Pig architecture
- Pig Latin basics
- Pig execution modes
- Pig processing – loading and transforming data
- Pig built-in functions
- Filtering, grouping, sorting data
- Relational join operators
- Pig Scripting
- Pig UDF's
Learning Objectives:
Learn about the tools to enable easy data ETL, a mechanism to put structures on the data, and the capability for querying and analysis of large data sets stored in Hadoop files.
Topics:
- Background of Hive
- Hive architecture
- Hive Query Language
- Derby to MySQL database
- Managed & external tables
- Data processing – loading data into tables
- Hive Query Language
- Using Hive built-in functions
- Partitioning data using Hive
- Bucketing data
- Hive Scripting
- Using Hive UDF's
Learning Objectives:
Look at demos on HBase Bulk Loading & HBase Filters. Also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.
Topics:
- HBase overview
- Data model
- HBase architecture
- HBase shell
- Zookeeper & its role in HBase environment
- HBase Shell environment
- Creating table
- Creating column families
- CLI commands – get, put, delete & scan
- Scan Filter operations
Learning Objectives:
Learn how to import and export data between RDBMS and HDFS.
Topics:
- Importing data from RDBMS to HDFS
- Exporting data from HDFS to RDBMS
- Importing & exporting data between RDBMS & Hive tables
Learning Objectives:
Understand how multiple Hadoop ecosystem components work together to solve Big Data problems. This module will also cover Flume demo, Apache Oozie Workflow Scheduler for Hadoop Jobs.
Topics:
- Overview of Oozie
- Oozie Workflow Architecture
- Creating workflows with Oozie
- Introduction to Flume
- Flume Architecture
- Flume Demo
Learning Objectives:
Learn to constantly make sense of data and manipulate its usage and interpretation; it is easier if we can visualize the data instead of reading it from tables, columns, or text files. We tend to understand anything graphical better than anything textual or numerical.
Topics:
- Introduction
- Tableau
- Chart types
- Data visualization tools
Hands-on: Use Data Visualization tools to create a powerful visualization of data and insights.
Learning Objectives:
Learn a simple way to access servers, storage, databases, and a broad set of application services over the internet.
Topics:
- Cloud computing basics
- Concepts and terminology
- Goals and benefits
- Risks and challenges
- Roles and boundaries
- Cloud characteristics
- Cloud delivery models
- Cloud deployment models
Hands-on: Implement Cloud computing and deploy models.
Meet your instructors
Tarun Sukhani
DirectorProject
Analysis of Aadhar
Aadhar card Database is the largest biometric project of its kind currently in the world. The Indian government needs to analyse the database, divide the data state-wise and calculate how many people are still not registered, how many cards are approved and how they can bifurcate it according to gender, age, location, etc.
Analyzing in Banking Sector (CITI Bank)
The Citi group of banks is one of the world’s largest providers of financial services, In recent years, they adopted a fully Big Data-driven approach to drive business growth and enhance the services provided to customers because traditional systems are not able to handle the huge amount of data pouring in. Using Hadoop, they will be storing and analyzing banking data to come up with multiple insights.
E-commerce Website based Analysis (Clickstream Analysis)
On Ecommerce Web sites, clickstream analysis is the process of collecting, analyzing and reporting aggregate data about which pages a website visitor visits and in what order. With increasing number of ecommerce businesses, there is a need to track and analyse clickstream data. When using traditional databases to load and process clickstream data, there are several complexities in storing and streaming customer information and it also requires a huge amount of processing time to analyse and visualize it.
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The Course
Hadoop has now become the de facto technology for storing, handling, evaluating and retrieving large volumes of data. Big Data analytics has proven to provide significant business benefits and more and more organizations are seeking to hire professionals who can extract crucial information from structured and unstructured data. KnowledgeHut brings you a full-fledged course on Big Data Analytics and Hadoop development that will teach you how to develop, maintain and use your Hadoop cluster for organizational benefit.
This course will prepare you for everything you need to learn about Big Data while gaining practical experience on Hadoop.
After completing our course, you will be able to understand:
- What is Big Data, its need and applications in business
- The tools used to extract value from Big data
- The basics of Hadoop including fundamentals of HDFs and MapReduce
- Navigating the Hadoop Ecosystem
- Using various tools and techniques to analyse Big Data
- Extracting data using Pig and Hive
- How to increase sustainability and flexibility across the organization’s data sets
- Developing Big Data strategies for promoting business intelligence
There are no restrictions but participants would benefit if they have elementary computer knowledge.
Yes, KnowledgeHut offers this training online.
Your instructors are Hadoop experts who have years of industry experience.
Finance Related
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund
KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.
The Remote Experience
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.
Minimum Requirements:
- Operating system such as Mac OS X, Windows or Linux
- A modern web browser such as FireFox, Chrome
- Internet Connection
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