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Big data
This document discusses big data concepts including the 5 V's of big data (volume, velocity, variety, veracity, and value). It covers big data storage sizes from megabytes to zettabytes and the processes of collecting, discovering, processing, and managing data. Key challenges discussed are handling different data types at large scales, timeliness, privacy and security. The document also outlines developing analytics tools and models, business intelligence, and storage considerations. It describes analyzing data to find patterns and derive meaning to make decisions. Popular big data technologies like Hadoop, MapReduce, R and Cassandra are listed. The document emphasizes extracting information from data using analytics to predict outcomes. It recommends combining Hadoop and R using Rhadoop
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Big data
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- 4. •MEGA BYTE •GIGA BYTE •TERABYTE •PETA BYTE •EXTRA BYTE •ZETTA BYTE
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- 6. •How to handleHeterogeneity •Scalability (volume) •Structured / Semi structured/ Unstructured data handling •Timeliness response •Handle privacy and security issues
- 7. •Development of analytics •Design& develop appropriate data models •Developments of tools •Creating Data warehouse & Mining techniques •Business intelligence •Storage consideration
- 8. •Variety of Formats •Ambiguityof Data •Abstracted layer of Hierarchy •Lack of Meta Data
- 9. •Finding patterns • Derivingmeaning • Making decisions • Responding to the world with intelligence
- 10. •Lexical Analysis •Syntactic Analysis •SemanticAnalysis •Sentimental Analysis
- 11. •Big Table •Business Intelligence •Cassandra •Hadoopand MapReduce •R
- 12. •Deals with extractinginformation from data •Use this information to predict outcomes •Find patterns that predict similar outcome in future
- 13. •Hadoop and Mapreduce •IntegratingR and Hadoop •Tools like RHIPE and RHadoop
- 14. By combining capabilityof Hadoop and R by using Rhadoop tool the performance and accuracy of data analytics can be increased
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