Big data converted

Mar 18, 20190 likes45 views

This document discusses big data and its key characteristics. It notes that big data is characterized by volume, velocity, and variety. It provides examples of how much data is being generated from various sources like social media and sensors. It also discusses how the velocity of data capture is increasing with real-time data from devices and algorithms. Finally, it notes big data comes in many varieties beyond just numbers, including unstructured text, audio, video and more.

Big Data
• Lots of data is being collected
and warehoused
– Web data, e-commerce
– purchases at department/
grocery stores
– Bank/Credit Card
transactions
– Social Network

Three Characteristics of Big Data: V3s
Volume
•Data quantity
Velocity
•Data Speed
Variety
•Data Types

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.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.

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 realtime
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.

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

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

Selecting Big Data stores
• Choosing the correct data stores based on
your data characteristics
• Moving code to data
• Implementing polyglot data store solutions
• Aligning business goals to the appropriate
data store

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

Type of Data
• Relational Data (Tables/Transaction/Legacy Data)
• Text Data (Web)
• Semi-structured Data (XML)
• Graph Data
– Social Network, Semantic Web (RDF), …
• Streaming Data
– You can only scan the data once

The Structure of Big Data
Structured
• Most traditional data sources
Semi-structured
• Many sources of big data
Unstructured
• Video data, audio data

Why Big Data
• Growth of Big Data is needed
– Increase of storage capacities
– Increase of processing power
– Availability of data(different data types)
– Every day we create 2.5 quintillion bytes of data; 90% of the data in the
world today has been created in the last two years alone

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Big data refers to very large data sets that cannot be analyzed using traditional methods. It is characterized by volume, velocity, and variety. The volume of data is growing exponentially from various sources like social media and sensors. This data is generated and processed at high speeds. It also comes in different formats like text, images, videos. Storing and analyzing big data requires different techniques and tools than traditional data due to its scale. It can provide valuable insights when mined properly and has applications in many domains like healthcare, manufacturing, and retail. However, it also poses risks regarding privacy, costs and being overwhelmed by the data.

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Content 1. Introduction 2. What is Big Data 3. Characteristic of Big Data 4. Storing,selecting and processing of Big Data 5. Why Big Data 6. How it is Different 7. Big Data sources 8. Tools used in Big Data 9. Application of Big Data 10. Risks of Big Data 11. Benefits of Big Data 12. How Big Data Impact on IT 13. Future of Big Data 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. • ‘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. What is BIG DATA? 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. Three Characteristics of Big Data V3s Volume • Data quantity Velocity • Data Speed Variety • Data Types 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. • The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video. 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. 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 stru.

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Big data converted

1. SUBMITTED BY M.ABINAYA II M.SC.,CS

2. Big Data • Lots of data is being collected and warehoused – Web data, e-commerce – purchases at department/ grocery stores – Bank/Credit Card transactions – Social Network

3. Three Characteristics of Big Data: V3s Volume •Data quantity Velocity •Data Speed Variety •Data Types

4. 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. • The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.

5. 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 realtime • on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.

6. 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

7. 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

8. Selecting Big Data stores • Choosing the correct data stores based on your data characteristics • Moving code to data • Implementing polyglot data store solutions • Aligning business goals to the appropriate data store

9. 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

10. Type of Data • Relational Data (Tables/Transaction/Legacy Data) • Text Data (Web) • Semi-structured Data (XML) • Graph Data – Social Network, Semantic Web (RDF), … • Streaming Data – You can only scan the data once

11. The Structure of Big Data Structured • Most traditional data sources Semi-structured • Many sources of big data Unstructured • Video data, audio data

12. Why Big Data • Growth of Big Data is needed – Increase of storage capacities – Increase of processing power – Availability of data(different data types) – Every day we create 2.5 quintillion bytes of data; 90% of the data in the world today has been created in the last two years alone

Big data converted

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