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Big Data Now
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- The value of big data to an organization falls into two categories: analytical use and enabling new products.
- the three Vs of volume, velocity, and variety are commonly used to characterize different aspects of big data.
- mentioned above, it’s not just about input data. The velocity of a system’s outputs can matter too. The tighter the feedback loop, the greater the competitive advantage.
- common use of big data processing is to take unstructured data and extract ordered meaning, for consumption either by humans or as a structured input to an application.
- a principle of big data: when you can, keep everything.
- For instance, documents encoded as XML are most versatile when stored in a dedicated XML store such as MarkLogic. Social network relations are graphs by nature, and graph databases such as Neo4J make operations on them simpler and more efficient.
- majority of big data solutions are now provided in three forms: software-only, as an appliance or cloud-based.
- Many organizations opt for a hybrid solution: using on-demand cloud resources to supplement in-house deployments.
- Big data practitioners consistently report that 80% of the effort involved in dealing with data is cleaning it up in the first place,
- Data marketplaces are a means of obtaining common data, and you are often able to contribute improvements back. Quality
- data scientists as having the following qualities: Technical expertise: the best data scientists typically have deep expertise in some scientific discipline. Curiosity: a desire to go beneath the surface and discover and distill a problem down into a very clear set of hypotheses that can be tested. Storytelling: the ability to use data to tell a story and to be able to communicate it effectively. Cleverness: the ability to look at a problem in different, creative ways.
- ability of MapReduce to distribute computation over multiple servers.