Download Big Data Analytics: From Strategic Planning to Enterprise by David Loshin PDF

By David Loshin

Big information Analytics will help managers in delivering an summary of the drivers for introducing substantial info expertise into the association and for knowing the categories of industrial difficulties most suitable to important information analytics strategies, figuring out the price drivers and advantages, strategic making plans, constructing a pilot, and finally making plans to combine again into creation in the enterprise.

• courses the reader in assessing the possibilities and price proposition
• evaluate of massive information and software program architectures
• provides a number of applied sciences and the way they healthy into the massive info environment

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Extra resources for Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph

Example text

2. Computation-restricted throttling: There are existing algorithms, but they are heuristic and have not been implemented because the expected computational performance has not been met with conventional systems. 3. Large data volumes: The analytical application combines a multitude of existing large datasets and data streams with high rates of data creation and delivery. Business Problems Suited to Big Data Analytics 17 4. Significant data variety: The 1 1data in the different sources vary in structure and content, and some (or much) of the data is unstructured.

A small sandbox may not exhibit the same computation demand as a larger environment, so understand the need for computational performance and how it needs to scale. Applying a standard for scaling the environment means the enterprise needs to adjust its operations and systems maintenance model to presume the existence of massive data volumes. Considerations to address include using high-speed networks; enabling high-performance data integration tools such as data replication, change data capture, compression, and alternate data layouts to rapidly load and access data; and enabling large-scale backup systems.

40 Big Data Analytics • The need for oversight: This realization, which might be considered a follow-on to the first, is that ensuring the usability of data for all purposes requires more comprehensive oversight. Such oversight should include monitored controls incorporated into the system development life cycle and across the application infrastructure. These realizations lead to the discipline called data governance. Data governance describes the processes for defining corporate data policies, describing processes for operationalizing observance of those policies, along with the organizational structures that include data governance councils and data stewards put in place to monitor, and hopefully ensure compliance with those data policies.

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