Given the massive scale of unstructured data generation, cloud data lakes, Hadoop, and other systems allow enormous storage and management.Ĭonsidering the variety of unstructured data formats, it is not surprising that it accounts for 80% of the total data. Unstructured data is stored in specialized databases like NoSQL, MongoDB, or data lakes. The ambiguity adds to the complexity as there is no pre-defined structure to the data. Handling unstructured data is way more complicated than the structured data. Storing massive volumes of unstructured data is challenging. This variety and ambiguity makes it impossible for unstructured data to be forced to fit into tables. If the review contains a mix of text, audio, and visuals, it cannot be stored in a database. Storing text reviews in a database will not give you any meaningful information. Typical examples are text data, audio, video, social media data, real-time streaming data from IoT smart devices, reviews, and many more, where insights go beyond numbers to feelings, opinions, and ideas. Unstructured data has no definite structure or data model and is stored in its native format.
This is a classic example of structured data and can be efficiently managed through a database. As new products are introduced or existing products modified, the changes must be reflected in the inventory records.
Inventory Management: A retail store needs to keep accurate track of its current inventory in the warehouse for business. Each employee’s data can be efficiently accessed using a unique id. Any changes in these attributes can be easily tracked using SQL queries. Use CasesĮmployee Management: Employee attributes like name, designation, address, salary, the department can be arranged in a structured tabular format. Related tables like customer purchase history, watch history, product information, product inventory, etc., can be grouped in a data warehouse for marketing analysis. Jira Certification Course for Business analyst.BA Training with Investment Banking Domain.
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