data science unit 2 bigdata introduction .pptx

data science unit 2 bigdata introduction .pptx

• 1.
  BIG DATA
• 2.
  What is bigData? • Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations • It refers to a massive amount of data that keeps on growing exponentially with time. • It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques. • It includes data mining, data storage, data analysis, data sharing, and data visualization. • The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data
• 3.
  Characteristics of bigdata 1) Variety • Variety of Big Data - structured, unstructured, and semistructured data gathered from multiple sources. While in the past, data could only be collected from spreadsheets and databases, 2) Velocity • Velocity essentially refers to the speed at which data is being created in real-time.. 3) Volume Big Data indicates huge ‘volumes’ of data that is being generated on a daily basis from various sources like social media platforms, business processes, machines, networks, human interactions, etc
• 4.
  Big Data Techniques •Association rule learning • Classification tree analysis • Genetic algorithms • Machine learning • Regression analysis • Sentiment analysis • Social network analysis
• 5.
  Big data storage •Big data storage needs to be able to handle capacity and provide low latency for analytics work. • The largest big data practitioners – Google, Facebook, Apple, etc – run what are known as hyperscale computing environments. • These comprise vast amounts of commodity servers with direct-attached storage (DAS) • Such environments run the likes of Hadoop, NoSQL and Cassandra as analytics engines,
• 6.
  Storage tools 1. BeyondHadoop 2. Edge Computing 3. Storage Remains Essential 4. Multi-Cloud 5. Embedded Intelligence 6. IoT and Machine Learning 7. Zero Tolerance 8. Hybrid Storage 9. Vertical Focus 10. Storage Intelligence
• 7.
  Applications 1) Healthcare 2) Academia 3)Banking 4) Manufacturing 5) IT 6. Retail 7. Transportation
• 8.
  Big Data Casestudies • 1. Walmart • 2. American Express • 3. General Electric • 4. Uber • 5. Netflix • 6. Procter & Gamble • 7. IRS