As the name suggests, the goal of database management systems is exactly to handle a set of data to convert it into relevant information for the organization, either at an operational or strategic level.
It does so through a series of software routines that allow its use safely, simple and neat. It is about, in summary, of a set of programs that carry out tasks in an interrelated way to help the database construction and manipulation, implementing the interface form between these, the applications and the users themselves.
Its use allows system administrators to have better control and, Besides, at the same time obtain better results when making inquiries that help business administration by generating the much desired competitive advantage.
Functions and functionalities
A DBMS system stands for independence, minimal redundancy, consistency of the information (concurrency control), abstraction of information on its physical storage, as well as secure access and the adoption of the essential measures to guarantee the integrity of the data.
These particularities are some of the defining characteristics of a DBMS, whose essential processes are the manipulation and construction of databases, as well as its definition. These are characteristics that, at the same time, facilitate compliance with a series of functions related to many of the aspects mentioned, among others the definition of the data, its easy handling, quick administration, being able to represent complex relationships between the data and other aspects related to the security and validity of the data.
In front of your great functionality, some of its disadvantages are, Besides: the investment required to implement a hardware DBMS, the software and knowledge required for it, vulnerability to failure due to its centralization and its deficiencies with some types of data (as is the case of graphic or multimedia data, among others.).
The most used languages in a database administrator (DBMS)
Regarding the languages used in a DBMS, Note the Data Manipulation Language (DML) for the consultation and manipulation of data. Especially the SQL (Structured query language), the most used DML for relational data management, just like him Language of definition of data (DDL), used to determine structures and functions in the query.
The Data control language (DCL), in conclusion, at the same time it is a language used in a DBMS by the administrator, this time to control access to the data in the database.
The future of efficient database management is here: it's called AI
Increasingly, institutions are realizing that artificial intelligence (HE) and machine learning applied to managing and optimizing your databases are taking self-healing and self-tuning to the next level. These solutions, from both database providers and third parties, Enable database administrators to spend less time looking for bottlenecks and more time doing more productive and creative work in support of strategic business goals.
To understand how new technologies make it possible, it is necessary to know what artificial intelligence is, machine learning and deep learning:
- Artificial intelligence: is all that a machine achieves by imitating certain human functions “cognitive” like learning and problem solving. There are countless examples, as automated trading systems, autonomous vehicles or smart route delivery systems.
- Machine learning, which at the same time is known as machine learning, is a subset of artificial intelligence that uses statistical techniques to allow computers to model and predict outcomes using data sets. Email filters, fraud detection systems and rating systems to drive online marketing are some examples.
- Deep learning is a specific type of machine learning that uses artificial neural networks, unlike task-oriented machine learning algorithms. This technology enables computer vision, voice accreditation and natural language processing.
Now that we know what these advances are for, we can focus on how they benefit from database administration. Imagine that a database system (DBMS) is able to anticipate operational problems and take prescriptive measures to avoid them, allocating additional resources, adding or removing indexes, or automatically adjusting query execution plans.
This is what is known as autonomous databases powered by machine learning., that can predict when a nuisance may occur and automatically warn the DBA or take action.
This type of system is able to take advantage of the data collected from previous workloads to adjust new ones, using machine learning to build models that capture how the DBMS responds to different settings. It is a very suitable use for new applications, allowing you to recommend settings that serve to increase the probability of achieving a goal, how to reduce latency or improve performance.
The Machine learning and statistical regression techniques can simultaneously be applied to database management to identify bottlenecks. and predict the performance of a given set of resources. One more example that innovation offers us very different ways to increase efficiency, the performance and agility of our business processes.