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Big data

The document provides an overview of big data, defining it as large, complex data sets that traditional data processing cannot handle. It discusses the characteristics of big data, including volume, velocity, and variety, as well as the technologies and tools like Hadoop and MapReduce used for storage and processing. The benefits of big data are highlighted, emphasizing improved decision-making and the ability to analyze vast amounts of data efficiently.

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1. Introduction 2. What is Big Data 3. Big Data sources 4. Characteristic of Big Data 5. Storing and processing of Big Data 6. Hadoop 7. Why Big Data 8. Tools used in Big Data 9. Application of Big Data 10. Benefits of Big Data CONTENT
INTRODUCTION  Big Data may well be the Next Big Thing in the IT world.  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 and service offerings.
 Big Data is a term for data sets that are so large or complex that traditional data processing applications are inadequate.  If data reaches beyond the storage capacity and beyond the processing power. That data we are calling as ‘Big Data’.  Big Data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques.  We all live in data world. We need to store data and process the data. WHAT IS BIG DATA
 How we are getting these much data these years.  Walmart handles more than 1 million customer transactions every hour.  Facebook handles 40 billion photos from its user base.  Boeing 737 will generate 240 terabytes of flight data during a single flight across the US. WHAT IS BIG DATA
BIG DATA SOURCES Mobile Devices Readers/Scanners Science facilities Microphones Cameras Social Media Programs/ Software
THREE CHARACTERISTICS OF BIG DATA V3S Volume • Data quantity Velocity • Data Speed Variety • Data Types
Application Of Big Data analytics Homeland Security Smarter Healthcare Multi-channel sales Telecom Manufacturing Traffic Control Trading Analytics Search Quality
1ST CHARACTER OF BIG DATA VOLUME  Data is rapidly increasing (GB, TB, PB). 1024 mb = 1gb, 1024 gb = 1tb, 1024 tb = 1pb  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  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
3RD CHARACTER OF BIG DATA VARIETY  Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio, video and unstructured text, including log files and social media.  Traditional database systems were designed to address smaller volumes of structured data,  Big Data analysis includes different types of data
THE STRUCTURE OF BIG DATA  Structured - Most traditional data sources  Semi-structured - Many sources of big data.  Unstructured - Video data, audio data coming. 12
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 (HDFS) - HBase - Hive
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
 Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage..  Core Concepts in Hadoop  HDFS (Hadoop Distributed File system)  Map Reduce (Technique for processing the HDFS )
HISTORY OF HADOOP  Google in 2003, after 13 years of research came up with  GFS (a technique for storing )  Map Reduce (best processing technique)  But only made in white papers and not implemented.  Later in 2005, Yahoo came up with a conclusion for managing huge data. (But they have taken the reference from google.)  HDFS  Map reduce  Doug Cutting is the Inventor of Hadoop.
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.
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
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 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.  Big Data is already an important part of the $64 billion database and data analytics
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