Data analytics can influence business processes, enhance decision-making and drive business growth.
The data stored can be structured, semi-structured, or unstructured, which means it can be stored in a more flexible format for future use. When data is stored, it is associated with identifiers and metadata tags for faster retrieval.
Data lakes are usually configured on a cluster-like container of inexpensive and scalable commodity hardware. It stores every type of data in its native format with no fixed limits on account size or file size and offers a flexible format for future use. The Data Lake offers high data quantity to increase analytic performance and native integration.
Data is dumped and there is a need to worry about storage capacity. A research analyst can focus on finding meaningful patterns in data and not the data itself.
Unlike a hierarchal data warehouse where data is stored in files and folders, a data lake has a flat architecture. Every data element stored is given a unique identifier and tagged with a set of metadata information.