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Database Management Systems and SQL – Tutorial for Beginners

Database Management Systems and SQL are two of the most important and widely used tools on the internet today.

You use a Database Management System (DBMS) to store the data you collect from various sources, and SQL to manipulate and access the particular data you want in an efficient way.

Many different businesses use these tools to increase their sales and improve their products. Other institutions like schools and hospitals also use them to improve their administrative services.

In this article, you will learn about:

• The basics of DBMS and SQL
• The most important features of DBMS and SQL
• The reasons you should learn DBMS and SQL.

What Does a DBMS Do?

DBMS stands for Database Management System, as we mentioned above. SQL stands for Structured Query Language.

If you have lots of data that you need to store, you don't just want to keep it anywhere – then there would be no sense of what that huge amount of data means or can tell you. That's why we use a DBMS.

A database is basically where we store data that are related to one-another – that is, inter-related data. This inter-related data is easy to work with.

A DBMS is software that manages the database. Some of the commonly used DBMS (software) are MS ACCESS, MySQL, Oracle, and others.

Suppose you have some data like different names, grades, and ID numbers of students. You'd probably prefer to have that data in a nice table where a particular row consists of students’ names, grades, and ID numbers. And to help you organize and read that data efficiently, you'll want to use a DBMS.

Using a DBMS goes hand in hand with SQL. This is because when you store data and want to access and alter it, you'll use SQL.

A database stores data in various forms like schemas, views, tables, reports, and more.

Types of DBMS

There are two types of DBMS.

First, you have Relational Databases (RDBMS). In these types of databases, data is stored in the format of tables by the software. In an RDBMS, each row consists of data from a particular entity only.

Some of the RDBMS commonly used are MySQL, MSSQL, Oracle, and others.

Then you have Non-Relational Databases. In these databases, data is stored in the form of key and value pairs.

Some of the Non-Relational DBMSs commonly used are MongoDB, Amazon, Redis, and others.

Components of a DBMS

There are mainly four components of a DBMS which you can understand by checking out the image below:

You have your Users. There can be multiple users, like someone who manages the database (the database administrator), system developers, and also those who are just regular users like the customer.

You also have the Database Application. The application of a database can be either departmental or personal or may be for internal use in an organization.

Then you have the DBMS, which we've been discussing. This is software that helps the users create the database and access the data inside it in an efficient manner.

Finally, you have the Database, which is a collection of data stored in the form of a single unit.

One important feature of a DBMS is that it helps reduce the redundancy in the data stored. Having the same data stored at multiple locations in a database is called redundancy.

To eliminate and reduce the redundancy in the database, normalization is used.

Normalization is the process of structuring the data in an RDBMS by removing anomalies. It is important to enable easy retrieval of data from the database as well as to add or delete data without losing consistency. This might be implemented with the help of “Normal Forms” in DBMS. These normal forms help in establishing relations in a relational database instead of having to redefine existing fields again and again. In this way, normalization reduces redundancy.

What is SQL?

SQL is a database language. SQL is used widely and almost all Relational Database Management Systems can recognize it.

SQL contains a set of commands that enable you to create a database. You can also use it to execute commands in your Relational Database Management System.

SQL has certain advantages which have helped it thrive from the 1970s until now. It is widely accepted by both people and platforms, in part because of the following features:

• SQL is fast
• SQL is a very high-level language
• SQL is a platform-independent language
• SQL is a standardized language
• SQL is a portable language

Along with all the features mentioned above, you need almost no coding skills to work with SQL.

SQL performs a variety of tasks like creating, altering, maintaining and retrieving data, setting properties, and so on. All the tasks are done based on the commands you write, and these commands are grouped into various categories like DDL commands, DML commands, DCL commands, and so on.

Let's discuss some of the frequently used commands and their types.

DDL commands

DDL stands for Data Definition Language. It includes the set of commands that you use to perform various tasks related to data definition. You use these commands to specify the structure of the storage and methods through which you can access the database system.

You use DDL commands to perform the following functions:

• To create, drop, and alter.
• To grant and revoke various roles and privileges.
• Maintenance commands

Example DDL commands include CREATE , ALTER , DROP , and TRUNCATE .

DML commands

DML stands for Data Manipulation Language. As the name suggests, it consists of commands which you use to manipulate the data.

You use these commands for the following actions:

• Modification

Example DML commands are SELECT , INSERT , UPDATE , and DELETE .

TCL commands

TCL stands for Transaction Control Language. As the name says, you use these commands to control and manage transactions.

One complete unit of work that involves various steps is called a transaction.

You use these commands for the following purposes:

• To create savepoints
• To set properties of the transaction going on
• To undo the changes to the database (permanent)
• To make changes in the database (permanent)

Example TCL commands include COMMIT , ROLLBACK , and SAVE TRANSACTION .

How to Write Basic Queries in SQL

There are various keywords you use in SQL like SELECT, FROM, WHERE, and others. These SQL keywords are not case-sensitive.

To create a table called Student that has a name, roll numbers, and marks in it, you can write:

Here CREATE, TABLE, and NOT NULL are keywords. You use CREATE and TABLE to create a table and NOT NULL to specify that the column cannot be left blank while making a record.

To make a query from a table, you'll write:  

You use the ‘select’ keyword to pull the information from a table. The ‘From’ keyword selects the table from which the information is to be pulled. The ‘where’ keyword specifies the condition to be specified.

For example, say we want to retrieve the marks from the student table that has data for marks, roll numbers, and names. The command would be as follows:

If you want to learn more about SQL for beginners, you can check out this cheatsheet that'll teach you the basics pretty quickly.

You can also go through this Relational Database Course for Beginners to get a more solid understanding of the query language.

Why Are DBMS and SQL Important?

Being able to work with DBMS and SQL are some of the most critical skills in today’s world. After all, you know what they say - "Data is the new oil." So you should know how to work with it effectively.

Here are a few reasons why you should learn how to use at least one DBMS and SQL.

Reasons to Learn How to Use a DBMS

If you're storing an extremely large amount of data.

If your organization needs to store a huge amount of data, you'll want to use a DBMS to keep them organized and be able to access them easily.

DBMS store the data in a very logical manner making it very easy to work with a humongous amount of Data. You can read more about database management systems in this tutorial by freeCodeCamp , in this Wiki , and on Scaler for a better understanding of data storage in DBMS.

If you're doing data mining

Data mining is the process of extracting usable data that includes only relevant information from a very large dataset. Using a DBMS, you can perform data mining very efficiently. For managing the data, you use CRUD operations which stands for Create, Read, Update, and Delete. You can perform these operations with a DBMS easily and efficiently.

Integrity constraint and scalability

The data you store in your database satisfies integrity constraints. Integrity constraints are the set of rules that are already defined and which are responsible for maintaining the quality and consistency of data in that database. The DBMS makes sure that the data is consistent. Scalability is another important feature of a DBMS. You can insert a lot of data into a database very easily and it will be accessible to the user quickly and with some basic queries. You do not need to write new code and spend lots of time and money on expanding the same database.

When you have multiple user interfaces

When you're using a DBMS, you can have multiple users access the system at the same time. Just like in a UNIX operating system two users can log into a single account at the same time.

DBMS makes storing data simple. You can also add security permissions on data access to make sure access is restricted and the privacy of the data remains intact. DBMS protects the confidentiality, availability, integrity, and consistency of the data stored in it. Along with making the data secure it reduces the time taken to develop an application and makes the process efficient.

Learning a DBMS is an in-demand skill:

Most companies out there – big or small – have lots of data to work with. And so they'll need people to analyze it.

If you know how to use a DBMS, you can use those skills in almost all data-oriented technologies. So once you learn DBMS, it will be easy to work on any data-driven technology.

Reasons to Learn SQL

Since SQL is a language that is used for database management, some of the above points also apply to learning it (such as data storage, data mining, and so on).

Here are some of the additional reasons you should learn SQL.

SQL is relatively easy to learn

SQL is quite easy to learn in the context of database management. SQL queries resemble the simple English we use in our day-to-day life. For example, if we want to make a table named Topics, we just have to use the command:

Understanding how a computer works helps you learn other skills related to computers like any programming language, spreadsheet software like MS Excel, and word processing software like MS word.

You also use SQL to manage data on various platforms like SQLite .

SQL is standardized

SQL was developed in the 1970s and has been extensively used for more than 50 years without many significant changes made to it. This makes it a standard skill for working with data, so typically when you apply for a job, they will be using SQL for data storage and management purposes. This general standardization also makes it easier to learn because you don't need to constantly update your knowledge, again and again, to be adept at it.

SQL is easy to troubleshoot

Any error you get while using SQL will show a clear message about what's going on in very simple English.

For example, if you are trying to use a table or any database that does not exist, it will show the error that the table or the database you are trying to access does not exist.

There is the concept of exception handling in SQL also just like any other           programming language.

Exception handling is used for handling query runtime errors with the TRY CATCH construct. The TRY block is used to specify the set of statements that need to be checked for an error, while CATCH block executes certain statements in case an error has occurred. Exception handling is crucial for writing bug-free code.

Easy to manipulate data

Data manipulation refers to Adding (or inserting), deleting (removing), and modifying (updating) the data in a database. The data you store in the SQL is dynamic in nature which makes it easy for you to manipulate the data at any point in time.

You can also retrieve data easily using a single-line SQL command.And if you want to present the data in the form of charts or graphs, then SQL plays a key role in that and makes data visualization easy for you.

Client and server data sharing

Whenever an application is used, the data stored in the database management system is retrieved based on the option selected by the user. To create and manage the servers, SQL is used. SQL is used to navigate through the large amount of data stored in the database management system.

Easy to sync data from multiple sources

You'll come across many cases when you have to get data from multiple sources and combine them to get the desired output. This means you'll be dealing with outputs from multiple sources at one time, which can be time-consuming and a tedious job.

But when you use SQL, it is much easier to handle data from multiple sources at the same time and combine them to get the desired output.

In SQL you can use the UNION operation to combine data, like this:

Using this combines the columns “name” and “order_id” from the “customers” and “orders” tables, respectively, and renders the combined table.

Flexibility, versatility, and data analysis

SQL is a programming language, but the scope of this language is not only limited to programming tasks. You can use it for various purposes like in the finance sector and in sales and marketing, as well. By executing a few queries you can get the data you need and analyze it for your purposes.

There are various roles that are specific to SQL like SQL developer, SQL database Administrator, Database Tester, SQL Data analyst DBA, Data Modeler, and more. You can learn more about salary insights here .

Another important role is that of a data analyst. The process of cleansing, modeling, and transforming data to draw conclusions from it based on certain information is called Data analysis.

The role of a data analyst is important in any organization as it helps in analyzing trends and making fast and flexible decisions on the basis of the available data.

SQL and DBMS are two of the most in-demand skills for Data Analysis.

How DBMS and SQL Work Together

DBMS and SQL are interdependent and cooperate to make the data organized and accessible. Now, let's understand how SQL works in synchronization with a Database Management System.

SQL is the way you interact with the database management system. You use it to retrieve, insert, update, or delete data (CRUD operations), among other things.

When you execute a SQL command, the DBMS figures out the most efficient way to execute that command. The interpretation of the task to be performed is determined by the SQL engine.

The classic query engine is used to handle all the non-SQL queries, but it will not handle any logical files.

The query processor interprets the queries of the user and translates them into a database-understandable format.

The parser is used for translation purposes (in query processing). It also checks the syntax of the query and looks for errors, if present.

The optimisation engine, as the name suggests, optimises the performance of the database with the help of useful insights.

The DBMS engine is the underlying software component for performing CRUD operations on the database.

The file manager is used for managing the files in the database, one at a time.

And the transaction manager is used for managing the transactions to maintain concurrency while accessing data.

In this article, we have discussed the basics of DBMS and SQL and why you should learn these skills.

We have discussed the purpose and importance of DBMS and SQL, what they're used for, and what professionals who work with databases and SQL do.

After reading this article you have a good understanding of where knowledge of DBMS and SQL can take you. Happy Learning!

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• DBMS Tutorial - Database Management System

Basic of DBMS

• Introduction of DBMS (Database Management System) - Set 1
• History of DBMS
• Advantages of Database Management System
• Disadvantages of DBMS
• Application of DBMS
• Need for DBMS
• DBMS Architecture 1-level, 2-Level, 3-Level
• Difference between File System and DBMS
• Entity Relationship Model
• Introduction of ER Model
• Structural Constraints of Relationships in ER Model
• Difference between entity, entity set and entity type
• Difference between Strong and Weak Entity
• Generalization, Specialization and Aggregation in ER Model
• Recursive Relationships in ER diagrams
• Relational Model
• Introduction of Relational Model and Codd Rules in DBMS
• Types of Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign)
• Anomalies in Relational Model
• Mapping from ER Model to Relational Model
• Strategies for Schema design in DBMS
• Relational Algebra
• Introduction of Relational Algebra in DBMS
• Basic Operators in Relational Algebra
• Extended Operators in Relational Algebra
• SQL Joins (Inner, Left, Right and Full Join)
• Join operation Vs Nested query in DBMS
• Tuple Relational Calculus (TRC) in DBMS
• Domain Relational Calculus in DBMS
• Functional Dependencies
• Functional Dependency and Attribute Closure
• Armstrong's Axioms in Functional Dependency in DBMS
• Equivalence of Functional Dependencies
• Canonical Cover of Functional Dependencies in DBMS
• Normalisation
• Introduction of Database Normalization
• Normal Forms in DBMS
• First Normal Form (1NF)
• Second Normal Form (2NF)
• Boyce-Codd Normal Form (BCNF)
• Introduction of 4th and 5th Normal Form in DBMS
• The Problem of Redundancy in Database
• Database Management System | Dependency Preserving Decomposition
• Lossless Decomposition in DBMS
• Lossless Join and Dependency Preserving Decomposition
• Denormalization in Databases
• Transactions and Concurrency Control
• Concurrency Control in DBMS
• ACID Properties in DBMS
• Implementation of Locking in DBMS
• Lock Based Concurrency Control Protocol in DBMS
• Graph Based Concurrency Control Protocol in DBMS
• Two Phase Locking Protocol
• Multiple Granularity Locking in DBMS
• Polygraph to check View Serializability in DBMS
• Log based Recovery in DBMS
• Timestamp based Concurrency Control
• Dirty Read in SQL
• Types of Schedules in DBMS
• Conflict Serializability in DBMS
• Condition of schedules to View-equivalent
• Recoverability in DBMS
• Precedence Graph for Testing Conflict Serializability in DBMS
• Database Recovery Techniques in DBMS
• Starvation in DBMS
• Deadlock in DBMS
• Types of Schedules based Recoverability in DBMS
• Why recovery is needed in DBMS
• Indexing, B and B+ trees
• Indexing in Databases - Set 1
• Introduction of B-Tree
• Insert Operation in B-Tree
• Delete Operation in B-Tree
• Introduction of B+ Tree
• Bitmap Indexing in DBMS
• Inverted Index
• Difference between Inverted Index and Forward Index
• SQL Queries on Clustered and Non-Clustered Indexes

File organization

• File Organization in DBMS - Set 1
• File Organization in DBMS | Set 2
• File Organization in DBMS | Set 3

DBMS Interview questions and Last minute notes

• Last Minute Notes - DBMS
• Commonly asked DBMS interview questions
• Commonly asked DBMS interview questions | Set 2

DBMS GATE Previous Year Questions

• Database Management System - GATE CSE Previous Year Questions
• Database Management Systems | Set 2
• Database Management Systems | Set 3
• Database Management Systems | Set 4
• Database Management Systems | Set 5
• Database Management Systems | Set 6
• Database Management Systems | Set 7
• Database Management Systems | Set 8

DBMS Tutorial – Database Management System

Database Management System is a software or technology used to manage data from a database. Some popular databases are MySQL, Oracle, MongoDB, etc. DBMS provides many operations e.g. creating a database, Storing in the database, updating an existing database, delete from the database. DBMS is a system that enables you to store, modify and retrieve data in an organized way. It also provides security to the database.

In this Database Management System tutorial you’ll learn basic to advanced topics like ER model, Relational Model, Relation Algebra, Normalization, File Organization, etc.

‘Recent Articles’ on DBMS !

• Introduction
• File Organization
• Advanced Topics
• Quick Links

Introduction :

• DBMS Introduction | Set 1
• DBMS Introduction | Set 2 (3-Tier Architecture)
• DBMS Architecture 2-level 3-level
• Need For DBMS
• Data Abstraction and Data Independence
• Database Objects
• Multimedia Database
• Use of DBMS in System Software
• Choice of DBMS | Economic factors

Entity Relationship Model :

• Enhanced ER Model
• Minimization of ER Diagram
• ER Model: Generalization, Specialization and Aggregation
• Recursive Relationships

Relational Model :

• Relational Model and CODD Rules
• Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign)
• Number of possible Superkeys

>> Quiz on ER and Relational Model

Relational Algebra :

• Basic Operators
• Extended Operators
• Inner Join vs Outer Join
• How to solve Relational Algebra Problems for GATE
• How to Solve Relational Algebra Problems for GATE

Functional Dependencies :

• Finding Attribute Closure and Candidate Keys using Functional Dependencies
• Armstrong’s Axioms in Functional Dependency
• Canonical Cover

Normalisation :

• Normal Forms
• Dependency Preserving Decomposition
• Lossless Join Decomposition
• LossLess Join and Dependency Preserving Decomposition
• How to find the Highest Normal Form of a Relation
• DBMS | Data Replication

>> Quiz on Normal Forms

Transactions and Concurrency Control :

• ACID Properties
• Concurrency Control -Introduction
• Concurrency Control Protocol | Graph Based Protocol
• Concurrency Control Protocol | Two Phase Locking (2-PL)-I
• Concurrency Control Protocol | Two Phase Locking (2-PL)-II
• Concurrency Control Protocol | Two Phase Locking (2-PL)-III
• Concurrency Control Protocol | Multiple Granularity Locking
• Concurrency Control Protocol | Thomas Write Rule
• Concurrency Control | Polygraph to check View Serializabilty
• DBMS | Log based recovery
• Timestamp Ordering Protocols
• Introduction to TimeStamp and Deadlock Prevention Schemes
• Conflict Serializability
• View Serializability
• How to test if two schedules are View Equal or not ?
• Recoverability of Schedules
• Precedence Graph for testing Conflict Serializabilty
• Transaction Isolation Levels in DBMS
• Database Recovery Techniques

>> Quiz on Transactions and concurrency control

Indexing, B and B+ trees :

• Indexing and its Types
• B-Tree | Set 1 (Introduction)
• B-Tree | Set 2 (Insert)
• B-Tree | Set 3 (Delete)
• B+ Tree (Introduction)
• Bitmap Indexing

>> Practice questions on B and B+ Trees >> Quizzes on Indexing, B and B+ Trees

File Organization:

• File Organization – Set 1
• File Organization – Set 2 (Hashing in DBMS)
• File Organization – Set 3
• File Organization – Set 4

>> Quiz on File structures

Advanced Topics :

• Query Optimization
• How to store a password in database?
• Storage Area Networks
• Network attached storage
• Data Warehousing
• Data Warehouse Architecture
• Characteristics and Functions of Data warehouse
• Difficulties of Implementing Data Warehouses
• Data Mining
• Data Mining | KDD process
• Data Mining | Sources of Data that can be mined
• ODBMS – Definition and overview
• Architecture of HBase
• Apache HBase
• Architecture and Working of Hive
• Apache Hive
• Difference between Hive and HBase

SQL Tutorial

• SQL | Tutorials
• Quiz on SQL

DBMS practices questions :

• Database Management Systems | Set 1
• Database Management Systems | Set 9
• Database Management Systems | Set 10
• Database Management Systems | Set 11

Advantages of DBMS

There are some following reasons to learn DBMS:

• Organizing and management of data: DBMS helps in managing large amounts of data in an organized manner. It provides features like create, edit, delete, and read.
• Data Security: DBMS provides Security to the data from the unauthorized person.
• Improved decision-making: From stored data in the database we can generate graphs, reports, and many visualizations which helps in decision-making.
• Consistency: In a traditional database model all things are manual or inconsistent, but DBMS enables to automation of the operations by queries.

FAQs on Database Management System(DBMS)

Q.1 what is database.

A database is a collection of organized data which can easily be created, updated, accessed, and managed. Records are kept maintained in tables or objects. A tuple (row) represents a single entry in a table. DBMS manipulates data from the database in the form of queries given by the user.

Q.2 What are different languages present in DBMS?

DDL (Data Definition Language) : These are the collection of commands which are required to define the database. E.g., CREATE, ALTER, RENAME, TRUNCATE, DROP, etc. DML (Data Manipulation Language) : These are the collection of commands which are required to manipulate the data stored in a database. E.g., SELECT, UPDATE, INSERT, DELETE, etc. DCL (Data Control Language) : These are the collection of commands which are dealt with the user permissions and controls of the database system. E.g, GRANT, and REVOKE. TCL (Transaction Control Language) : These are the collection of commands which are required to deal with the transaction of the database. E.g., COMMIT, ROLLBACK, and SAVEPOINT.

Q.3 What are the ACID properties in DBMS?

The full form of ACID is Atomicity, Consistency, Isolation, and Durability these are the properties of DBMS that ensure a safe and secure way of sharing data among multiple users. A – Atomic: All changes to the data must be performed successfully or not at all. C – Consistent: Data must be in a consistent state before and after the transaction. I – Isolated: No other process can change the data while the transaction is going on. D – Durable: The changes made by a transaction must persist.

Q.4 What are the Advantages of DBMS?

The followings are the few advantages of DBMS : Data Sharing: Data from the same database can be shared by multiple users at the same time. Integrity: It allows the data stored in an organized and refined manner. Data Independence: It allows changing the data structure without changing the composition of executing programs. Data Security: DBMS comes with the tools to make the storage and transfer of databases secure and reliable. Authentication and encryption are the tools used in DBMS for data security.

Quick Links :

• Last Minutes Notes(LMNs) on DBMS
• Quizzes on DBMS !
• ‘Practice Problems’ on DBMS !
• DBMS interview questions | Set 1
• DBMS interview questions | Set 2

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CS44800: Introduction to Database Systems, Spring 2019

Lecture Schedule

Chapter 1 - Introduction ( ppt / pdf )

Chapter 2 -Database System Concepts and Architecture ( ppt / pdf )

Chapter 13 - Disk Storage, Basic File Structures and Hashing ( ppt )

Chapter 14 - Indexing Structures for Files ( ppt )

Chapter 3 - Data Modeling Using the Entity Relationship (ER) Model ( ppt / pdf )

• Hierarchical model-slides
• Network Model-slides
• Handout on Natural and Outer Joins & Relational Calculus

Chapter 6 - The Relational Algebra and Calculus ( ppt / pdf )

Chapter 7 - Relational Database Design by ER-and EERR-to-Relational Mapping ( ppt / pdf )

Chapter 15 - Algorithms for Query Processing and Optimization ( ppt )

Chapter 8 - SQL-99: Schema Definition, Constraints, and Queries and Views ( ppt )

• JDBC Tutorial
• PL/SQL Tutorial

Chapter 10 - Functional Dependencies and Normalization for Relational Databases ( ppt )

• Example of proving loss less join
• Normalization and lossless joins and FD preservations

Chapter 12 - Practical Database Design Methodology and Use of UML Diagrams ( ppt )

Chapter 16 - Practical Database Design and Tuning ( ppt )

• Concurrency Control in Database Systems , Bharat Bhargava, IEEE Trans on Knowledge and Data Engineering,11(1), Jan.-Feb. 1999
• The Transaction Concept: Virtues and Limitations , Jim Gray, VLDB, 1981
• 2PL and Conflict Graph. ( pdf )
• Locking-Serializability. ( pdf )
• Optimistic-timestamps-Failure-Commitment. ( pdf )
• It will help you learn about CC ideas on 2PL, optimistic, time stamps and about transaction failures to help you do the homework. You may read the papers on CC under handouts and check sections 2 up to 2.3.2. ( pdf )
• You may read slides on chapter 21 (7th edition) up to slides 21-22 and then 27-29 for answers to homework. ( pdf )
• Logs, failures, serializability is discussed in slides for chapter 20. ( pdf )
• Concurrency Control in Database Systems, Bharat Bhargava, IEEE Trans on Knowledge and Data Engineering,11(1), Jan.-Feb. 1999. ( pdf )
• The Transaction Concept: Virtues and Limitations , Jim Gray, VLDB, 1981. ( pdf )

Chapter 19 - Database Recovery Techniques ( ppt )

• Feature analysis of selected database recovery techniques, Bharat Bhargava and Leszek Lilien, Proceedings of the May 4-7, 1981, National Computer Conference . ACM, 1981.( pdf )
• Secure Data Warehouse - Privacy

Chapter 24 - Enhanced Data Models for Advanced Applications ( ppt )

• Introduction to Cloud Computing
• Privacy and Identity Management in Cloud Computing
• Complete Tutorial

Chapter 26 - Web Database Programming using PHP ( ppt )

Chapter 27 - XML-Extensible Markup Language ( ppt )

Chapter 29 - Overview of Data Warehousing and OLAP ( ppt )

Chapter 30 - Emerging Database Technologies and Applications ( ppt )

CS44800, Spring 2019 Purdue University, Computer Science Department

Browse Course Material

Course info, instructors.

• Prof. Samuel Madden
• Prof. Robert Morris
• Prof. Michael Stonebraker
• Dr. Carlo Curino

Departments

• Electrical Engineering and Computer Science

As Taught In

• Information Technology
• Algorithms and Data Structures
• Data Mining
• Software Design and Engineering

Learning Resource Types

Database systems, final project assignment and ideas.

A large portion (20%) of your grade in 6.830 consists of a final project. This project is meant to be a substantial independent research or engineering effort related to material we have studied in class. Your project may involve a comparison of systems we have read about, an application of database techniques to a system you are familiar with, or be a database-related project in your research area.

This document describes what is expected of a final project and proposes some possible project ideas.

What Is Expected

Good class projects can vary dramatically in complexity, scope, and topic. The only requirement is that they be related to something we have studied in this class and that they contain some element of research — e.g., that you do more than simply engineer a piece of software that someone else has described or architected. To help you determine if your idea is of reasonable scope, we will arrange to meet with each group several times throughout the semester.

What to Hand In

There are two written deliverables, a project proposal and a final report.

Project Proposal : The proposal should consist of 1-2 pages describing the problem you plan to solve, outlining how you plan to solve it, and describing what you will “deliver” for the final project. We will arrange short meetings with every group before the project proposal to help you refine your topic and would be happy to provide feedback on a draft of your proposal before it is due.

Final Report : You should prepare a conference-style report on your project with maximum length of 15 pages (10 pt font or larger, one or two columns, 1 inch margins, single or double spaced — more is not better). Your report should introduce and motivate the problem your project addresses, describe related work in the area, discuss the elements of your solution, and present results that measure the behavior, performance, or functionality of your system (with comparisons to other related systems as appropriate.)

Because this report is the primary deliverable upon which you will be graded, do not treat it as an afterthought . Plan to leave at least a week to do the writing, and make sure you proofread and edit carefully!

Please submit a paper copy of your report. You will also be expected to give a presentation on your project in class that will provide an opportunity for you to present a short demo of your work and show what you have done to other students in the class. Details about the format of the presentation will be posted as the date gets closer.

Project Ideas

The following is a list of possible project ideas; you are not required to choose from this list — in fact, we encourage you to try to solve a problem of your own choosing! If you are interested in working on one of these projects, contact the instructors and we can put you in touch with students and others around MIT working on these ideas. Note that these are not meant to be complete project proposals, but just suggestions for areas to explore — you will need to flesh them out into complete projects by talking with your group members, the course staff, and graduate students working on these projects.

Being able to compare performance of different DBMSs and different storage and access techniques is vital for the database community. To this purpose several synthetic benchmark has been designed and adopted over time (see TPC-C, TPC-H etc…). Wikipedia open source application, and publicly available data (several TB!!), provide a great starting point to develop a benchmark based on real-world data. Moreover, we obtained from the Wikimedia foundation 10% of 4 months of Wikipedia accesses (roughly 20 billion HTTP requests!). The project will consists in using this real-world data, queries and access patterns to design one of the first benchmarks based on real-world data.

Amazon RDS is a database service provided within the EC2 cloud. An interesting project consists in investigating performance and scalability characteristics of Amazon RDS. Also since RDS services run in a virtualized environment, studying the “stability” and “isolation” of the performance offered is interesting.

Hosted database services such as Amazon RDS, Microso SQL Azure are starting to become popular. It is still unclear what is the performance impact of running applications on a local (non-hosted) platform, such as a local enterprise datacenter, while having the data hosted “in the cloud”. An interesting project aim at investigating the performance impact for different classes of applications e.g., OLAP, OLTP, Web.

Performance monitoring is an important portion of data-center and database management. An interesting project consists in developing a monitoring interface for MySQL, capable of monitoring multiple nodes, reporting both DBMS internal statistics, and OS-level statistics (CPU, RAM, DIsk), potentially automating the detection of saturation of resources.

Being able to predict cpu/mem/disk load of database machines can enable “consolidation”, i.e., the co-location of multiple DB within a smaller set of physical servers. We have an interesting set of data from real-world data-centers, the project would consist in investigating machine-learning and other predictive techniques on such real-world data.

Flash memories are very promising technologies, providing lower latency for random operations. However, they have a series of unusual restrictions and performance. An interesting project investigates the performance impact of using flash memories for DB applications.

Often database assume data to be stored on a local disk, however data stored on network file systems can allow for easier administration, and is rather common in enterprises using SAN or NAS storage systems. The project will investigate the impact of local-vs-networked storage on query performance.

Partition-aware object-relational mapping. Many programmers seem to prefer object-relational mapping (ORM) layers such as like Ruby on Rails or Hibernate to a traditional ODBC/JDBC interface to a database. In the H-store Project we have been studying performance benefits that can be obtained in a “partitonable” database, where the tables can be cleanly partitioned according to some key attribute (for example, customer-id), and queries are generally run over just one partition. The goal of this project would be to study how to exploit partitioning to improve the performance of a distributed ORM layer.

Twitter provides a fire hose of data. Automatically filtering, aggregating, analyzing such data can allow a way to harness the full value of the data, extracting valuable information. The idea of this project is investigating stream processing technology to operate on social streams.

Client-side database. Build a Javascript library that client-side Web applications can use to access a database; the idea is to avoid the painful way in which current client-side application have to use the XMLHttpRequest interface to access server-side objects asynchronously. This layer should cache objects on the client side whenever possible, but be backed by a shared, server-side database system.

As a related project, HTML5 browsers (including WebKit, used by Safari and Chrome), include a client-side SQL API in JavaScript. This project would involve investigating how to user such a database to improve client performance, offload work from the server, etc.

Preventing denial-of-service attacks on database systems. Databases are a vulnerable point in many Web sites, because it is often possible for attackers to make some simple request that causes the Web site to issue queries asking the database to do a lot of work. By issuing a large number of such requests, and attacker can effectively issue a denial of service attack against the Web site by disabling the database. The goal of this project would be to develop a set of techniques to counter this problem — for example, one approach might be to modify the database scheduler so that it doesn’t run the same expensive queries over and over.

Auto-admin tools to recommend indices, etc. Design a tool that recommends a set of indices to build given a particular workload and a set of statistics in a database. Alternatively investigate the question of which materialized views to create in a data-warehousing system, such as

Scientific community data management requirements significantly differ from regular web/enterprise ones. To this purpose a specialized DB is currently being developed named: SciDB. Studying performance of SciDB on dedicated servers vs. on virtualized environment such as EC2 is an intriguing topic. Another interesting investigation would cover the impact on SciDB performance of storing the data over the network (e.g., network file system). A third interesting project would explore the performance of clustering algorithms on SciDB vs. MapReduce.

Asynchronous Database Access. Client software interacts with standard SQL databases via a blocking interface like ODBC or JDBC; the client sends SQL, waits for the database to process the query, and receives an answer. A non-blocking interface would allow a single client thread to issue many parallel queries from the same thread, with potential for some impressive performance gains. This project would investigate how this would work (do the queries have to be in different transactions? what kind of modification would need to be made to the database) and would look at the possible performance gains in some typical database benchmarks or applications.

Extend SimpleDB. SimpleDB is very simple. There are a number of ways you might extend it to explore some of the research ideas we have studied in this class. For example, you could add support for optimistic concurrency control and compare its performance to the basic concurrency control scheme you will implement in Problem Set 3. There are a number of other possible projects of this type; we would be happy to discuss these in more detail.

CarTel. In the CarTel project, we are building a system for collecting and managing data from automobiles. There are several possible CarTel related projects: * One of the features of CarTel is a GUI for browsing geo-spatial data collected from cars. We currently have a primitive interface for retrieving parts of the data that are of interest, but developing a more sophisticated interface or query language for browsing and exploring this data would make a great project. * One of the dangers with building a system like CarTel is that it collects relatively sensitive personal information about users location and driving habits. Protecting this information from casual browsers, insurance companies, or other undesired users is important. However, it is also important to be able to combine different users data together to do things like intelligent route planning or vehicle anomaly detection. The goal of this project would be to find a way to securely perform certain types of aggregate queries over CarTel data without exposing personally identifiable information. * We have speed and position data from the last year for 30 taxi cabs on the Boston streets. Think of something exciting you could do with this.

Rollback of long-running or committed transactions. Database systems typically only support UNDO of committed transactions, but there are cases where it might be important to rollback already committed transactions. One approach is to use user-supplied compensating actions, but there may be other models that are possible, or it may be possible to automatically derive such compensating action for certain classes of transactions.

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Lean into being a facilitator — not an expert.

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I was five years into my executive coaching practice when I was invited to lead a workshop on “Coaching Skills for Human Resource Leaders” at a global conference. As the room filled up with participants, I identified a few colleagues who had already been coaching professionally for more than a decade. I felt self-doubt start to kick in: Why were they even here? What did they come to learn? Why do they want to hear from me?

• Deborah Grayson Riegel is a professional speaker and facilitator, as well as a communication and presentation skills coach. She teaches leadership communication at Duke University’s Fuqua School of Business and has taught for Wharton Business School, Columbia Business School’s Women in Leadership Program, and Peking University’s International MBA Program. She is the author of Overcoming Overthinking: 36 Ways to Tame Anxiety for Work, School, and Life and the best-selling Go To Help: 31 Strategies to Offer, Ask for, and Accept Help .

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Same AI + Different Deployment Plans = Different Ethics

KENNESAW, Ga. | May 14, 2024

Why Autonomous Cars Aren’t Yet Ethical For Wide Deployment

This month I will address an aspect of the ethics of artificial intelligence (AI) and analytics that I think many people don’t fully appreciate. Namely, the ethics of a given algorithm can vary based on the specific scope and context of the deployment being proposed. What is considered unethical within one scope and context might be perfectly fine in another. I’ll illustrate with an example and then provide steps you can take to make sure your AI deployments stay ethical.

There are limited tests of fully autonomous, driverless cars happening around the world today. However, the cars are largely restricted to low-speed city streets where they can stop quickly if something unusual occurs. Of course, even these low-speed cars aren’t without issues. For example, there are reports of autonomous cars being confused and stopping when they don’t need to and then causing a traffic jam because they won’t start moving again.

We don’t yet see cars running in full autonomous mode on higher speed roads and in complex traffic, however. This is in large part because so many more things can go wrong when a car is moving fast and isn’t on a well-defined grid of streets. If an autonomous car encounters something it doesn’t know how to handle going 15 miles per hour, it can safely slam on the brakes. If in heavy traffic traveling at 65 miles per hour, however, slamming on the breaks can cause a massive accident. Thus, until we are confident that autonomous cars will handle virtually every scenario safely, including novel ones, it just won’t be ethical to unleash them at scale on the roadways.

Some Massive Vehicles Are Already Fully Autonomous – And Ethical!

If cars can’t ethically be fully autonomous today, certainly huge farm equipment with spinning blades and massive size can’t, right? Wrong! Manufacturers such as John Deere have fully autonomous farm equipment working in fields today. You can see one example in the picture below. This massive machine rolls through fields on its own and yet it is ethical. Why is that?

In this case, while the equipment is massive and dangerous, it is in a field all by itself and moving at a relatively low speed. There are no other vehicles to avoid and few obstacles. If the tractor sees something it isn’t sure how to handle, it simply stops and alerts the farmer who owns it via an app. The farmer looks at the image and makes a decision -- if what is in the picture is just a puddle reflecting clouds in an odd way, the equipment can be told to proceed. If the picture shows an injured cow, the equipment can be told to stop until the cow is attended to.

This autonomous vehicle is ethical to deploy since the equipment is in a contained environment, can safely stop quickly when confused, and has a human partner as backup to help handle unusual situations. The scope and context of the autonomous farm equipment is different enough from regular cars that the ethics calculations lead to a different conclusion.

Putting The Scope And Context Concept Into Practice

There are a few key points to take away from this example. First, you can’t simply label a specific type of AI algorithm or application as “ethical” or “unethical”. You also must also consider the specific scope and context of each deployment proposed and make a fresh assessment for every individual case.

Second, it is necessary to revisit past decisions regularly. As autonomous vehicle technology advances, for example, more types of autonomous vehicle deployments will move into the ethical zone. Similarly, in a corporate environment, it could be that updated governance and legal constraints move something from being unethical to ethical - or the other way around. A decision based on ethics is accurate for a point in time, not for all time.

Finally, it is necessary to research and consider all the risks and mitigations at play because a situation might not be what a first glance would suggest. For example, most people would assume autonomous heavy machinery to be a big risk if they haven’t thought through the detailed realities as outlined in the prior example.

All of this goes to reinforce that ensuring ethical deployments of AI and other analytical processes is a continuous and ongoing endeavor. You must consider each proposed deployment, at a moment in time, while accounting for all identifiable risks and benefits. This means that, as I’ve written before , you must be intentional and diligent about considering ethics every step of the way as you plan, build, and deploy any AI process.

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Getting an Overview of the Core Terms in Margin Analysis

After completing this lesson, you will be able to:

• Get an Overview of the Core Terms in Margin Analysis

Overview of the Core Terms in Margin Analysis

https://learning.sap.com/learning-journeys/outline-cost-management-and-profitability-analysis-in-sap-s-4hana/outlining-profitability-analysis_b5b7efbb-55ea-4ff5-bc70-15d39d8a14eb

Introduction to Margin Analysis

The following video provides an overview of Margin Analysis.

Master Data

Master data in margin analysis include profitability characteristics and functional areas. Functional areas break down corporate expenditure into different functions, in line with the requirements of cost of sales accounting.

These functions can include:

• Production.
• Administration.
• Sales and Distribution.
• Research and Development.

For primary postings, the functional area is derived according to fixed rules and included in the journal entries. For secondary postings, the functional area and partner functional area are derived from the sender and receiver account assignments to reflect the flow of costs from sender to receiver.

Profitability Characteristics

Profitability characteristics represent the criteria used to analyze operating results and the sales and profit plan. Multiple profitability characteristics are combined to form profitability segments. The combination of characteristic values determines the profitability segment for which the gross margin structure can be displayed. A profitability segment corresponds to a market segment.

For example, the combination of the characteristic values North (Sales region), Electronics (Product group) and Wholesale (Customer group) determine a profitability segment for which the gross margin structure can be displayed.

True vs Attributed Account Assignments

Each activity relevant to Margin Analysis in the SAP system, such as billing, creates line items. G/L line items can carry true or attributed account assignments to profitability segments.

• Goods issue item or billing document item in a sell-from-stock scenario.
• Manual FI posting to profitability segment.
• Primary Costs or Revenue.
• Secondary Costs.
• Balance Sheet Accounts with a statistical cost element assigned.

The derivation of attributed profitability segments is based on the true account assignment object of the G/L line item. This object can be of the following types:

• Cost Center.
• Sales Order.
• Production Order (only for Engineer-to-Order process.)
• Maintenance Order.
• Service Document (service order or service contract.)

After the profitability characteristics are derived, the resulting data is mapped to the G/L line item according to specific mapping rules. An attributed profitability segment is derived to fulfill the requirement of filling as many characteristics in the item as possible to enable the maximum drilldown analysis capability.

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Learning Systems Announcements

Put some tech in your t&l, canvas feature updates – april 2024, assignments – submission_type_selection update.

Production Environment Availability: 4/20/2024

In Assignments, external tools using the submission_type_selection placement have an updated interface in the Assignment Submission Type field. This update displays the configured icon, name of the tool and a description. The update improves cohesion and enhances the overall visual aesthetics of the Canvas user interface. In Assignments, an external tool using the submission_type_ selection placement displays an icon [1], the name of the tool [2] and the tool description [3].

Mobile – Mobile Offline Support

The Mobile offline mode feature option is available to admin allowing students to sync course content to view as read-only when offline. Note: As instructors develop course content, it is essential to carefully consider the origin of visual elements or links associated with the material. This feature provides users increased flexibility, productivity, and improved experiences in various scenarios where internet connectivity may be unreliable or unavailable.

For more information about the student mobile view, see the  Canvas Student Release Notes (iOS 7.0)  and the  Canvas Student Release Notes (Android 7.0) .

New Quizzes – Item Analysis Report Additional Data

Production Environment Availability: 4/20/204

The following data is added to the Item Analysis report:

• Essay and File Upload- Score distribution chart, CSV support
• Numeric-Answer Frequency Summary, Aggregation and CSV support

This update provides additional information on specific question types for instructors to evaluate student responses and provide appropriate intervention.

Raw Points and Percentages on the Moderate Page

On the New Quizzes Moderate page, a configure icon is added allowing instructors to switch between percentages and raw points in the score display column. This feature improves reporting flexibility and grading system alignment. To switch between percentages and raw points in the score column, click the  Configure  icon.

View Log Question Position

In New Quizzes the Moderation page View Log includes the question position of the answered question. This update allows instructors to easily verify the question position as students complete quizzes. In the View Log of a New Quiz, the question position or question number displays.

Gradebook – Message Students Who Total Columns

Production Environment Availability: 4/24/2024

In the Gradebook, the Message Students Who option is available in the Total column. Instructors can message students with a total grade higher than a specific grade or message students with a total lower than a specific grade. This update provides additional messaging opportunities not specific to an individual assignment. In the Total column, select the  Message Students Who  link.

In the For Students Who drop down menu, select the  Total grade higher than  or  Total grade lower than  option [1]. Then enter the desired grade [2] and compose the message [3].

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