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Navigating the Data Lake with Amazon Redshift

Introduction In this age of big data, organizations have to deal with large volumes of data in different formats from varied sources. Considering the volume of the data, companies need a storage and analytical solution that equips them with the necessary agility to handle such huge volumes. A data lake is considered to be the most viable option to tackle such huge volumes of data. Unlike the traditional data warehouses, a data lake acts as a repository to store data of all sorts in their original format. There is no additional need for converting the data to a specific format before saving. The data lakes are gaining popularity due the immense flexibility that they offer. In this context, this white paper tries to highlight the benefits of building a data lake on Amazon Redshift. What is a data lake and why do we need one? A data lake is a flat architecture storage system that provides massive storage for any kind of data. It provides enormous processing power and the ability to virtually handle limitless concurrent tasks or jobs. The data lake allows organizations to store all of their data, both structured and unstructured, in one centralized repository. Data lakes are transforming the way in which enterprises store and manage data. Data lakes are considered to be most feasible solution as they offer the most economical solution to store, manage and analyse data, when compared to the traditional data warehouse solutions. These technologies also allow organisations to analyse the data and get insights in to their business operations, which were quite difficult to obtain using conventional methods. 

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