Introduction to Big Data
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Big data is high volume, high velocity, and high variety information that requires innovative processing for business insights. It can include structured and unstructured data like text, images, videos and sensor data. Handling big data requires new technologies for capturing, storing, integrating, analyzing and presenting insights. Key benefits include understanding customers and processes, fraud detection, and customizing websites in real time. Characteristics of big data include large volumes, many data types, rapid data production, and inconsistent data loads.
Introduction to Big Data
- 1. Introduction to Big Data Analytics 1 Dr. Amitabh Mishra
- 2. Meaning of Big Data “Big data is a high volume, high velocity, high variety information asset that demands cost-effective and innovative forms of information processing for enhanced business insight and decision making”. Dr. Amitabh Mishra 2
- 3. • Big data involves homogeneous voluminous data that could be: • Structured (as in RDBMS) or • Unstructured (as in blogs, tweets, Facebook comments, emails) • The content may be in different varieties as- – Audio – Picture – Large text Dr. Amitabh Mishra 3
- 4. • Handling Big data need newer and innovative technologies for -capturing, storing, searching, integrating, analysing and presenting newly found insights. Dr. Amitabh Mishra 4
- 5. Benefits Big Data Analytics • Here is a list of advantages that can be achieved by using Big Data analytics: – Understanding and Targeting Customers – Understanding and Optimizing Business Processes – Re-develop your products – Personal Quantification and Performance Optimization – Helps in Fraud Detection & improving Security – Perform Risk Analysis – Customize your website in real time – Optimizing Machine and Device Performance Dr. Amitabh Mishra 5
- 6. Characteristics of Big Data Dr. Amitabh Mishra 6 Characteristics Volume Variety Velocity Variability
- 7. Characteristics: Volume • Volume is obviously the most common trait of Big Data. • Many factors contributed to the exponential increase in data volume, such as: – Transaction-based data stored through the years, – Text data constantly streaming in from social media, – Increasing amounts of sensor data being collected, – Automatically generated GPS data, and so on. • With the staggering increase in data volume, even the naming of the next Big Data echelon has been a challenge. The highest mass of data that used to be called peta bytes (PB) has left its place to zeta bytes (ZB), which is a terabytes (TB). (1 Terabyte can hold 200,000 songs or 17,000 hours of music / 500 hours of movies) Dr. Amitabh Mishra 7
- 8. Characteristics: Variety • Data today comes in all types of formats formats ranging from traditional databases to: – To hierarchical data stores created by the end users and OLAP systems (Online Analytical Processing) – To text documents, e-mail, XML, meter-collected, and sensor-captured data – To video, audio, and stock ticker data • By some estimates, 80 to 85 percent of all organizations’ data is in some sort of unstructured or semi - structured format (a format that is not suitable for traditional databases schemas). Dr. Amitabh Mishra 8
- 9. Characteristics: Velocity • Velocity means the speed of something in a given direction. • According to Gartner, velocity means both – How fast data is being produced and – How fast the data must be processed (i.e., captured, stored, and analysed) to meet the need or demand. • Velocity is perhaps the most overlooked characteristic of Big Data. Reacting quickly enough to deal with velocity is a challenge to most organizations. Dr. Amitabh Mishra 9
- 10. Characteristics: Variability • In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. • Daily, seasonal, and event triggered peak data loads can be challenging to manage— especially with social media involved. Dr. Amitabh Mishra 10