Introduction
In its simplest form, a database management system (DBMS) is a computerized system for storing and managing data. It enables end users to create, collect, store, protect, and analyze a large amount of data in a database. Enhancing security, ensuring data integrity, and concurrency for databases are some of its other tasks. It works as an interface between databases and users or application programs, corroborating the consistent organization of data and easy accessibility. DBMS performs a range of functions, from administrative tasks to data recovery, centralized data view, and several more. As a system software with multiple integrated components, Big Data and data analytics are an integral part of DBMS.
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This article from The CEO Views intends to discuss the functions of Big Data and data analytics by unveiling their integrated approach to database management.
Functions of a Database Management System
DBMS’s functions go beyond data storage. It has much more to offer in this technology and data-driven business world. DBMS collects, stores, analyzes, manipulates, and deletes data from a database. With a smooth-running database management system, users have control over their data in a database. It allows them to update, read, create, and delete data. A DBMS has three foundational data elements- the database engine, database schema, and a logical structure of the database. These three data elements deliver security, concurrency, integrity, and a uniform data administration process. Below are some fundamental functions of a DBMS.
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Data Storage
A DBMS offers efficient data storage and retrieval by storing data in rows, tables, and columns.
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Data Administration
Database management systems perform administrative tasks, such as performance monitoring, security, data recovery, and backup.
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Controlling Concurrency
A DBMS prevents data corruption or inconsistency in an environment where multiple users access and modify the database simultaneously.
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Centralized Data Views
The centralized view of data in a DBMS helps users access data from multiple locations in a controlled way. It can limit the usage of end users to inspect how they view data.
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Data Independence
A DBMS delivers both physical and logical data independence to protect users and applications from having to know the location of stored data or any changes to the physical structure of data.
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Manipulation of data
It allows users to insert, update, modify, and delete data inside a database to ensure data integrity and consistency.
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Backup and Recovery
A DBMS fosters backup and recovery of data by creating backup copies to restore data for further usage.
Big Data and Database Management
Big Data and database management are interconnected concepts. Large and complex datasets that require specialized techniques and tools for storage, processing, and analysis are widely known as Big Data. An efficient database management system offers the structure and tools to manage such a vast amount of data.
Big data management refers to the organization, administration, and governance of large volumes of data, both structured and unstructured. A traditional DBMS generally focuses on structured data. Whereas big data requires unstructured and semi-structured data, demanding specialized DBMS like NoSQL databases. These databases are preferred for big data as they support both data structures. DBMSs are equipped with all the necessary tools and techniques to handle large volumes and complex data, like big data. It relies on a DBMS for storage and management of its prodigious datasets.
What does DBMS do in Data Analytics?
Data management and data analytics share a symbiotic relationship. But, before exploring their interconnected relationship, let us first know what data analytics is. Data analytics is the process of drawing insights from data. We already know that database management is the process of collecting, storing, organizing, manipulating, deleting, and recovering data.
Now, the question is how these two disciplines are connected. DBMS is the foundation of data analytics. Effective database management unlocks an efficient and authentic data analysis procedure. Data analytics requires equipment and techniques to examine raw data, identify patterns, and extract meaningful insights to empower business decision-making. DBMS makes data easily accessible and reliable, ensuring a successful data analysis.
Through data analytics, data management practices can be improved as areas of improvement can be identified in the database management system, such as optimizing data storage, enhancing data security, and more. Conversely, well-managed data makes data analysis smooth and accurate, leading to better insights and informed decision-making.
Conclusion
As database management continues to evolve in this tech-driven business world, the role of big data and data analytics becomes more prominent. Working in a symbiotic relationship, both disciplines contribute to the empowerment of data-driven decision-making.
