Akshay Sharma

1666074696

What is Data Abstraction in DBMS and what are its three levels?

Out of hundreds of the things in the code, is it accurate to showcase every detail to the user? 

Do you think hiding unnecessary data from a slot of thousands of elements embedded in an array is easy for a developer? There must be a way out of it! 

You must have heard about data abstraction in DBMS quite a few times! 

This is your ultimate solution for this issue. 

Data abstraction allows you to show or abstract only limited data from the whole database according to your choices. 

It makes analysis, evaluation, and observing the data easier and more convenient for the user. 

 Can you imagine if you show all complicated codes and relations to the user? They will get confused! 

To save the users from such situations, data abstraction is used by the developer. 

Learn everything you need to know about the steps and levels of data abstraction in one article with us!

Stay till the end of this article to know all the important concepts of data abstraction in DBMS

What do you mean by data abstraction in DBMS  

You can understand data abstraction as your data filter. 

Data abstraction is simply a process to hide the data you don’t want to display in front of the users. It allows the users to simply stick to the data that is necessary for them to know. 

You can abstract particular data out of a set with the help of this process. You can easily save yourself from errors due to recursion in data structures with this process given that every element can be called out easily. 

 Why do we use data abstraction?

Data abstraction is performed by the developers to carry forward two major tasks. 

The primary task of using data abstraction is to eliminate the data which is inappropriate to be displayed in front of the user. Or simply the kind of data that the user does not require. This way, it becomes easier for the user to use the website and get their work done. 

The second task of data abstraction is to ensure that the data is secured and only a few people can access it. In case you present the unfiltered form of data to the users, your confidential data can reach the wrong hands. 

How does data abstraction work? 

It’s important to filter a huge database 

Data abstraction is done by separating the data from the backend and the one shown to the user. 

Simply put, during data abstraction a new representation of data is formed that is displayed to the user. 

This way, the user only gets access to the useful components and not the internal mechanism of the commands. 

But do you think that all of the data can be scanned and filtered in one go? This is not how it works with data abstraction! 

Data abstraction of a particular set of data is done on four different levels. 

Let’s take a walkthrough of all the levels of data abstraction and understand how they work!. 

What are the levels of Data abstraction?

Data abstraction is a detailed process! 

There are different levels in which the data abstraction is done based on the type and specifications of data you wish to extract. 

Beginning with the first i.e. the physical level, let’s understand all the levels individually. 

Physical level

Also called as internal level, it is the lowest level of data abstraction! 

At this level, you will get insights into how exactly the data is stored internally. 

It varies from developer to developer as they decide in which way, the data will be stored by the data structures. 

Say that in a database where you have stored the list and details of the students, you will have to hide certain things. This level of data abstraction will help you to hide the storage blocks of data along with the amount of memory each data item is consuming. 

Logical data abstraction Level

This level of data abstraction is also called the conceptual level. It lies above the physical data abstraction level on the complexity chart. 

In this level of data abstraction, the relations between data elements and the details of the database are hidden. 

Simply put, the data abstraction at this level is done to ensure that the user does not get access to the particulars of the database to which certain data belongs. 

Though the complexity of this level is less than the physical level, it is still more complex than the highest level of data abstraction. 

At this level, you can get to know about tables of the whole data and how these tables are linked to one another. 

Since it deals with only tables and relations between them, it is widely used by database administrators. Also, this level of data abstraction is well adopted by the developers.

For example, if you have stored roll numbers and names of students in a class, this level will create two different tables for this information. Afterward, it will link the roll numbers with student names to fetch all the details of a student through the roll number. 

View data abstraction Level

This is the highest level of data abstraction. 

Also known as the external level of data abstraction, this level comprises several further levels of abstraction. 

In this level, the same database is depicted or shown to the user through different views. You can get single or multiple views of the same data as a user. 

This data abstraction level is the simplest form of data abstraction. It makes the interface simpler and more engaging for the user. 

Let’s take an example! 

Websites, where you can interact with the platform by filling out forms or fields, are much easier to use. You do not need to know how or what data is stored in the memory. 

Winding up

Data abstraction in DBMS is an essential part for the ease of assessment of data. Select the type and levels of data that you need to showcase or restrict from the sight of the user. Use the data abstraction levels carefully, to avoid recursion in data structures and attain efficiency. 


 

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What is Data Abstraction in DBMS and what are its three levels?
Ruth  Nabimanya

Ruth Nabimanya

1623170220

Database Management System | DBMS Tutorial

The collection of similar data in one place refers to a database. Let’s not confuse it with data. Data is a collection of information in the form of facts and figures. The database allows users to manipulate data according to their comfort.

This includes retrieval, insertions, and removal of data. It organizes data in tabular, graphical, and many more forms. The database management system is software to help users in managing data.

It provides an interface for the users to handle data in various forms. This may be during database creating or during the time of update. It also ensures that the data is safe all time while maintaining consistency.

Data Definition Language is the scheme that the system follows to see what data will look like in the database. Some famous database management softwares are – MySQL, Oracle, etc. The task of the database management system includes –

1. Data Updation – It is where all the changes in data are made. This may be the addition, removal, or modification of data.

2. Data Retrieval – It allows retrieval of the data from the database for application usage.

**3. User Administration **– It registers, monitors, maintains and enforces data all the time. This includes securing it, controlling it, managing its performance, and recovering information in case of failure.

History of Database Management System

  • Charles Bachman came up with the first DBMS system in 1960.
  • Codd by IBM’S Information Management System enters in 1970.
  • Peter Chen introduces the Entity-relationship model in 1976.
  • The Relational Model became a database component in 1980.
  • Object-oriented DBMS develops in 1985
  • The incorporation of object orientation in DBMS takes place in the 1990s.
  • A personal DBMS by Microsoft – MS access came out in 1991.
  • The first Internet database applications came out in 1995.
  • XML became relevant to database processing in 1997.

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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

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4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

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How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

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