Big Data

Big Data 

About Big data

Big Data describes a large amount of data (structured or unstructured) that is generated by businesses on a daily basis. The organization faces a great quantity of diverse information that arrives in increasing volumes rising at an ever-increasing rate. But not all the data is important. What is more important is to understand how organizations deal with the data that matters. 

Big data is the field defined to identify ways to analyze, analytically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

In general terms, big data refers to a large set of data that is complex and difficult or impossible to process using traditional methods. The concept of big data came into momentum during early 2000s when Doug Laney, Industry Analyst, expressed the definition of big data using 3 V’s - 

• Volume: Companies often require collecting and maintaining a large amount of data from various sources like business transactions, smart (IoT) devices, industrial equipment, videos, social media and many more. This could have led to a huge problem in the past – but in today’s time storage on platforms like data lakes and Hadoop have eased the burden
• Velocity: In this growing era of IoT (Internet of Things), the overall data flow in businesses is at an unprecedented speed and should be handled properly and timely. Hence, RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real-time.
• Variety: Data is stored in various types of formats –structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions. There has to be proper segregation  and handling of data for storage and interpretation

 

Why is Big Data important?

In order to understand the importance of big data do you must understand how an organization uses the collected data and not focus on how much data a company holds. Moreover, every organization have their own methodology to uses data; the more efficiently the data is being used, the greater potential it has to grow. The importance of big data can be briefed in the following ways - 

 

• Helps in Cost Savings: Big Data Tools like Hadoop and Cloud-Based Analytics help a lot in a cost saving that is way more advantageous to the business. Moreover, when large amounts of data have to be stored then big data tools assist in building efficient ways of performing business tasks.
• Saves Time: Popular tools like Hadoop and in-memory analytics with their speed and agility can help identify new sources of data to helps businesses analyze data immediately and thereby respond quickly based on the inferences.
• Interpret market situation: It is very important to understand and interpret the market condition correctly for better results. Therefore by analyzing big data you can get a better understanding of current market conditions and respond accordingly. Moreover, with proper data handling and interpretation, you can get ahead of your competitors.
• Helps build online reputation: Big data tools also help perform sentiment analysis. Thereby, providing proper customer feedback about your company. Therefore in order to monitor and improve the online presence of an organization, big data tools can prove to be really helpful.

 

Important Big Data Concepts 

Big Data is one of the most trending concepts in the present times. Companies are realizing the potential of Big Data holds and hence are on the lookout for Big Data Analysts/Experts who can carry out the process efficiently. In order to be successful in this field, one must have a thorough knowledge and must learn all the concepts and its implementation. Some of the important concepts Big Data covers include –

• Relational database management system (RDBMS): RDBMS refers to the structured data in a predetermined schema (tables), scalable vertically through large SMP servers, or horizontally through clustering software. These databases are usually easy to create, access, and extend. The standard language for relational database interoperability is the Structured Query Language (SQL).
• Non-relational database: Used for databases that do not store data into tables, but keep them accessible through special query APIs. The standard language used is Not Only SQL (NoSQL), it does not present a fixed schema, it uses the BASE system to scale vertically (basically available, soft-state, eventually consistent), and sharding (horizontal partitioning) to scale horizontally.
• Programming language: A programming language is a formally constructed language designed to communicate instructions to a machine. Some of the important data science applications include Java, C, C++, C#, R, and Matlab.
• MapReduce: It is software for parallel processing a huge amount of data.
• Flume: It is a service used to gather, aggregate, and move chunks of data from several sources to a centralized system.
• Cassandra: It is an open-source database system used for analyzing a large amount of data on a distributed system. It is characterized by high performance and by a high availability with no single point of failure
• Distributed System: The distributed system allows multiple terminals to communicate between them. The problem is divided into many tasks and assigned to each terminal. It is one of the most highly scalable systems as further nodes are added.
• Google File System: It is a proprietary distributed file system used for managing efficiently large datasets.
• HBase: It is an open-source non-relational database (column-oriented) developed on an HDFS. It is considered useful for real-time random read and write access to data, as well as to store sparse data. The relational counterpart is referred to as Big Table.
• Enterprise Data Warehouse (EDW): It is a system used for analysis and reporting consisting of central repositories of integrated data from a wide spectrum of different sources. The general form of an EDW is the extract-transform-load (ETL), one of the most representative cases of bulk data movement. The three most important examples of these systems are data marts, OLAP and OLTP.
• Resilient Distributed Datasets (RDD): It is a logical collection of data partitioned across machines. One of the most popular examples of RDD is Spark, an open-source clustering computing that has been designed for accelerating analytics on Hadoop.
• Hive: This is an example of an EDW infrastructure that facilitates data summarization, ad-hoc queries, and specific analysis.
• Pig: It is a platform used for processing a huge amount of data through a native programming language called Pig Latin. It runs at the same time sequences of MapReduce.
• Scripting Language: This is a programming language that supports scripts, which are pieces of code written for a run-time environment that interprets and automates the execution of tasks. Some of the important scripting languages in the big data field are Python, JavaScript, PHP, Perl, Ruby and Visual Basic Script.
• Data Mart: Datamart is a subset of the data warehouse used for a specific purpose. Data marts are then department-specific or related to a single line of business (LoB). Further, the next level of data marts is the Virtual Data Marts (it is a virtual layer that creates various views of data slices). The latest development is known as Data Lakes that represent massive repositories of unstructured data with an incredible computational capability. 

 

Knowledge and Skills required for the Big Data

Candidates gain quick success in Big Data career if they have skills of critical thinking and good communication skills.

 

Big Data Practice Exam Objectives

Big Data exam focuses on assessing your skills and knowledge in Apache Hadoop, Mapreduce and HDFS.

 

Big Data Practice Exam Pre-requisite

There are no prerequisites for the Big Data exam. Candidates who are well versed  in data management or programming can easily clear the exam.

 

Big Data Certification Course Outline

The Big Data Certification exam covers the following topics - 

1. Big Data

1.1. Big Data Definition

1.2. Big Data Types

1.3. Big Data Source

1.4. Big Data Challenges

1.5. Big Data Benefits

1.6. Big Data Applications

1.7. Netflix Application

 

2. Apache Hadoop

2.1. Introduction

2.2. Advantages & Disadvantages

2.3. History of Hadoop Project

2.4. Need for Hadoop

2.5. Hadoop Architecture

2.6. RDBMS vs Hadoop

2.7. Vendor Comparison

2.8. Hardware Recommendations

2.9. Hadoop Installation

 

3. HDFS

3.1. Basics (Blocks, Namenodes and Datanodes)

3.2. HDFS Architecture

3.3. Data Read and Write Process

3.4. HDFS Permissions

3.5. Data Replication

3.6. HDFS Accessibility

3.7. HDFS Filesystem Operations

3.8. HDFS Interfaces

3.9. Heartbeats

3.10. Rack Awareness

3.11. distcp

 

4. MapReduce

4.1. MapReduce Basics

4.2. MapReduce Work Flow

4.3. MapReduce Framework

4.4. Hadoop Data Types

4.5. MapReduce Internals

4.6. Job Formats

4.7. Debugging and Profiling

4.8. Distributed Cache

4.9. Combiner Functions

4.10. Streaming

4.11. Counters, Sorting and Joins

 

5. YARN

5.1. YARN Infrastructure

5.2. ResourceManager

5.3. ApplicationMaster

5.4. NodeManager

5.5. Container

 

6. Pig

6.1. Pig Architecture

6.2. Installation and Modes

6.3. Grunt and Pig Script

6.4. Pig Latin Commands

6.5. UDF and Data Processing Operator

 

7. HBase

7.1. HBase Architecture

7.2. HBase Installation

7.3. HBase Configuration

7.4. HBase Schema Design

7.5. HBase Commands

7.6. MapReduce Integration

7.7. HBase Security

 

8. Sqoop and Flume

8.1. Sqoop

8.2. Flume

 

9. Hive

9.1. Hive Architecture

9.2. Hive shell

9.3. Hive Data types

9.4. HiveQL

 

10. Workflow

10.1. Apache Oozie

 

11. Hadoop Cluster Management

11.1. Cluster Planning

11.2. Installation and Configuration

11.3. Testing

11.4. Benchmarking

11.5. Monitoring

 

12. Administration

12.1. dfsadmin, fsck and balancer

12.2. Logging

12.3. Data Backup

12.4. Add and removal of nodes

 

13. Security

13.1. Authentication

13.2. Data Confidentiality

13.3. Configuration

 

14. NextGen Hadoop

14.1. HDFS HA

14.2. HDFS Federation

 

 

Who should take the Big Data exam?

Big Data Certification has been designed for professionals aspiring to make a career in Big Data and Hadoop Framework. The certification is suitable for Students, Software Professionals, Analytics Professionals, ETL developers, Project Managers, Architects, and Testing Professionals. Also, the Big Data Certification can be taken by professionals who are looking forward to acquire a solid foundation on Big Data Industry can also opt for this exam. 

With more than 1.8 trillion gigabytes of structured and unstructured data in the world, and the volume doubling every two years, the requirement for Big Data Analysts and Business Intelligence Professionals has never been greater. It adds up to an incredible need for Big Data and Hadoop professionals who understand how to develop, process and manage half of world's data. 

 

Exam Format and Information

• Certification name – Big Data Certification
• Exam duration – 60 minutes
• Exam type - Multiple Choice Questions
• Eligibility / pre requisite - None
• Exam language - English
• Exam format - 
• Passing score - 25
• Exam Fees  - INR 999