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Compsci #10

From 12 [big data]

TermDefinition
Big data Ex volume of data at Google, NASA, etc
The Vs of Big Data Main ones - Volume, Variety, Velocity. Other - Veracity, Variability, Value
Big data challenges Difficult to effectively and efficiently capture, store, and analyze big data. Also new breeds of tech are needed
Big data considerations Platform limitations, data arriving too fast for the platform to handle, etc
Success factors for big data analytics A clear business need, strong sponsorship, alignment between the business and IT strategy
In memory analytics Storing and processing the complete data set in RAM
In database analytics Placing analytic procedures close to where data is stored
Grid computing & MPP Use of many machines and processors in parallel (MPP - massively parallel processing)
Appliances Combining hardware, software, and storage in a single unit for performance and scalability
Data integration The ability to combine data quickly and at reasonable costs
Processing capabilities The ability to process the data quicky, as it is captured (ex stream analytics)
MapReduce MapReduce processes large, complex data files by distributing tasks across many simple computers to achieve high performance
Hadoop Open-source framework for storing and analyzing massive unstructured data using low-cost hardware for easy scaling
How Hadoop works Uses HDFS(file system) to split data into parts across nodes on commodity hardware, with one node as Facilitator and another as Job Tracker
Hadoop and data warehousing Hadoop handles unstructured, large-scale data, while data warehouses work with structured, processed data. Together, Hadoop can store raw data, and the warehouse can analyze it after processing
MapReduce vs Hadoop Related but not the same. MapReduce provides control for analytics, but it is not an analytic. Hadoop is about data diversity, not just data volume
Created by: jolly_n4
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