Data Lake is a storage warehouse that can store huge amount of organized, semi-organized, and unstructured data. It is a storage location for every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration. Data lakes democratizes data and is a cost-effective technique to store all data of an organization for later processing. In contrast to a hierarchal data warehouse in which data is stored in files and folders, data lake has a flat structural design. Each data element in a data lake is given with a unique identifier and labeled with a set of metadata information. The primary objective of building a data lake is to offer an unrefined view of data to data scientists. Data lake offers business agility. Machine Learning and Artificial Intelligence can be used to produce profitable predictions.
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The streamlined access to organizational data from departmental mainframe, silos, and legacy systems and rise in need to extract in-depth insights from growing volumes of data to gain a competitive advantage among organizations across the globe are the major driving factors for growth of the market. However, lack of metadata in data lake leading to data swamps can hamper the data lake market growth. Contrarily, rise in shift toward cloud-based data platforms to manage and mitigate data issues is further expected to offer opportunities for the increased adoption of the market.
All organizations need data lake as it allows them to merge different data silos and deliver a representation of an organizational data asset. In other words, a data lake provides framework for data science that would otherwise be difficult to derive without a database. A data lake ensures that all employees, irrespective of their designation can have access to information. This is known as data democratization. For instance, only top managers in some organizations may have the authority to collect all types of data. However, with data lake, required data is made available to all levels of employees, irrespective of their designation.
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Metadata is a data that characterizes other information. When used properly within a data lake, it acts as a labeling framework that allows individuals to search for different kinds of data. Metadata can also create a hierarchical storage structure that prevents a data lake from changing into a data swamp. Companies can arrange their data with metadata tags signifying the source of data or how it correlates to a company event. It is also worthy to rely on metadata to help describe time frames or age of the data. If an organization made a metadata tag titled ‘2020 User Feedback Form’, that metadata explains both the type and age of the information. Some metadata tags are less specific, such as “Twitter.”
Even in this instance, the individuals working with the data can use more than one metadata tag for a piece of information, thus adding context to it. Data swamps do not have metatags, however, individuals accessing the data run into a problematic scenario where they can know exactly what kind of information they would like to find but have no idea how to find it.
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Key market playyers are Atos SE, Amazon.com Inc., Cloudera Inc., Google LLC, IBM Corporation, Microsoft Corporation, Oracle Corporation, Snowflake Inc., TCS LTD, Teradata Corporation
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