wk-7

Big Data Analytics

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Big data analytics is used to solve such as excess to micro mistakes, managing the vast amount of data in the big data platforms, data storage and quality and data security (Sharda et al. 2020). These are common challenges which are solved through big data solutions. There are potential risks that develop as a result of big data, especially in issues such as privacy and data security. The big data tools are used in analyzing and storage utilizes disparate data sources. Eventually, there is a high risk of data exposure which makes the security of the data vulnerable.

On the other hand, popular data storages such as data warehouses are commonly used in gathering and storage of vast amounts of unrestricted and structured data. Challenges arise when the data lakes attempt to link unstructured data and inconsistent data from diverse data sources which lead to data errors. It is also challenging getting the large sets of data into big data platforms which overwhelms the data engineers and is considered vital to engage data analytics. Big data analytics is used to analyze micro mistakes in the data. It is engaged in helping an organization to make sense of its unused business data which streamlines the data to be accessible and consumable to every-one.

Most of the companies are shifting to cloud as it is quicker and cheaper in comparison to the traditional data storage methods. There will be a transmission from the traditional or on-premise to cloud data solutions. There will be a reduced traditional on-premise data storage strategies. However, some companies may opt to maintain the traditional storage models as they cannot move all the data to the cloud. The companies will employ the two strategies, which are cloud computing strategies and on-premise data storage. There will be no need to purchase the physical hardware's such as the traditional data warehouses in future as a result of cloud data warehouses.

References

Sharda, R., Delen, D., Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support 11E. ISBN: 978-0-13-519201-6.