This document provides an overview of big data. It begins by defining big data and noting that it first emerged in the early 2000s among online companies like Google and Facebook. It then discusses the three key characteristics of big data: volume, velocity, and variety. The document outlines the large quantities of data generated daily by companies and sensors. It also discusses how big data is stored and processed using tools like Hadoop and MapReduce. Examples are given of how big data analytics can be applied across different industries. Finally, the document briefly discusses some risks and benefits of big data, as well as its impact on IT jobs.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
A Seminar Presentation on Big Data for Students.
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Guest Speaker in the 2nd National level webinar titled "Big Data Driven Solutions to Combat Covid 19" on 4th July 2020, Ethiraj College for Women(Auto), Chennai.
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Deadlocks operating system To develop a description of deadlocks, which prevent sets of concurrent processes from completing their tasks
To present a number of different methods for preventing, avoiding, or detecting deadlocks in a computer system
e.t.c
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2. INTRODUCTION
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the
21st century.
The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the beginning.
Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a computing
task, or new product and service offerings.
3. WHAT IS BIG DATA?
‘Big Data’ is similar to ‘small data’, but bigger in size
but having data bigger it requires different
approaches:
Techniques, tools and architecture
an aim to solve new problems or old problems in a
better way
Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with traditional
computing techniques.
4. WHAT IS BIG DATA
Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
5. THREE CHARACTERISTICS OF BIG DATA
V3S
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
6. 1ST CHARACTER OF BIG DATA
VOLUME
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every
day.
•Boeing 737 will generate 240 terabytes of flight data during a
single flight across the US.
7. 2ND CHARACTER OF BIG DATA
VELOCITY
Clickstreams and ad impressions capture user behavior
at millions of events per second
high-frequency stock trading algorithms reflect market
changes within microseconds
machine to machine processes exchange data between
billions of devices
infrastructure and sensors generate massive log data in
real-time
on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
8. 3RD CHARACTER OF BIG DATA
VARIETY
Big Data isn't just numbers, dates, and strings.
Big Data is also geospatial data, 3D data, audio
and video, and unstructured text, including log
files and social media.
Traditional database systems were designed to
address smaller volumes of structured data,
fewer updates or a predictable, consistent data
structure.
Big Data analysis includes different types of data
9. STORING BIG DATA
Analyzing your data characteristics
Selecting data sources for analysis
Eliminating redundant data
Establishing the role of NoSQL
Overview of Big Data stores
Data models: key value, graph, document, column-family
Hadoop Distributed File System
HBase
Hive
10. PROCESSING BIG DATA
Integrating disparate data stores
Mapping data to the programming framework
Connecting and extracting data from storage
Transforming data for processing
Subdividing data in preparation for Hadoop MapReduce
Employing Hadoop MapReduce
Creating the components of Hadoop MapReduce jobs
Distributing data processing across server farms
Executing Hadoop MapReduce jobs
Monitoring the progress of job flows
11. THE STRUCTURE OF BIG DATA
Structured
• Most traditional data
sources
Semi-structured
• Many sources of big data
Unstructured
• Video data, audio data
11
12. WHY BIG DATA
• Growth of Big Data is needed
– Increase of storage capacities
– Increase of processing power
– Availability of data(different data types)
13. WHY BIG DATA
•FB generates 10TB
daily
•Twitter generates 7TB
of data
Daily
•IBM claims 90% of
today’s
stored data was
generated
in just the last two years.
14. HOW IS BIG DATA DIFFERENT?
1) Automatically generated by a machine
(e.g. Sensor embedded in an engine)
2) Typically an entirely new source of data
(e.g. Use of the internet)
3) Not designed to be friendly
(e.g. Text streams)
14
17. BIG DATA ANALYTICS
Examining large amount of data
Appropriate information (about data)
Identification of hidden patterns, unknown correlations
Better business decisions: strategic and operational
Effective marketing, customer satisfaction, increased
revenue
18. TYPES OF TOOLS USED IN BIG-DATA
Where processing is hosted?
Distributed Servers / Cloud (e.g. Amazon EC2)
Where data is stored?
Distributed Storage (e.g. Amazon S3)
What is the programming model?
Distributed Processing (e.g. MapReduce)
How data is stored & indexed?
High-performance schema-free databases (e.g. MongoDB)
What operations are performed on data?
Analytic / Semantic Processing
19. Application Of Big Data analytics
Homeland
Security
Smarter
Healthcare Multi-channel
sales
Telecom
Manufacturing
Traffic Control Trading
Analytics
Search
Quality
20. RISKS OF BIG DATA
• Will be so overwhelmed
• Need the right people and solve the right problems
• Costs escalate too fast
• Isn’t necessary to capture 100%
• Many sources of big data
is privacy
• self-regulation (data compression)
• Legal regulation
20
21. HOW BIG DATA IMPACTS ON IT
• Big data is a troublesome force presenting
opportunities with challenges to IT organizations.
By 2015 4.4 million IT jobs in Big Data ; 1.9 million
is in US itself
In 2017, Data scientist’s was No. 1 Job in the
Harvard’s ranking.
22. BENEFITS OF BIG DATA
•Real-time big data isn’t just a process for storing
petabytes or exabytes of data in a data warehouse,
It’s about the ability to make better decisions and take
meaningful actions at the right time.
•Fast forward to the present and technologies like
Hadoop give you the scale and flexibility to store data
before you know how you are going to process it.
•Technologies such as MapReduce,Hive and Impala
enable you to run queries without changing the data
structures underneath.