This document provides an overview of SQL data definition. It discusses SQL and the relational model, and how E/R diagrams can be implemented using SQL tables, columns, and constraints. Key topics covered include the CREATE TABLE statement and how to define columns, primary keys, foreign keys and relationships between tables. Examples are given of creating tables based on entities from an E/R diagram. The document also lists additional resources and announces coursework deadlines.
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
Structured Query Language
SQL Commands:
• The standard SQL commands to interact with relational databases are CREATE, SELECT, INSERT, UPDATE, DELETE and DROP
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
Structured Query Language
SQL Commands:
• The standard SQL commands to interact with relational databases are CREATE, SELECT, INSERT, UPDATE, DELETE and DROP
A short course on "SQL Fundamentals - Oracle 11g". All practicals are performed in the embedded video. Video can be separately found at given link
https://youtu.be/1OxaHBEVubg
A short course on "SQL Fundamentals - Oracle 11g". All practicals are performed in the embedded video. Video can be separately found at given link
https://youtu.be/1OxaHBEVubg
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
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This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. SQL Data Definition
In This Lecture
• SQL
• The SQL language
• SQL, the relational model, and E/R diagrams
• CREATE TABLE
• Columns
• Primary Keys
• Foreign Keys
• For more information
• Connolly and Begg chapter 6
• Ullman and Widom 3.2, 6.6.
3. SQL Data Definition
SQL
• Originally ‘Sequel’ -
Structured English
query Language,
part of an IBM
project in the 70’s
• Sequel was already
taken, so it became
SQL - Structured
Query Language
• ANSI Standards
• SQL-89
• SQL-92 (SQL2)
• SQL-99 (SQL3)
• Most modern DBMS
use a variety of SQL
• Most based on SQL2,
increasingly SQL3
• Few (if any) are true
to the standard
4. SQL Data Definition
SQL
• SQL provides
• A data definition
language (DDL)
• A data manipulation
language (DML)
• A data control
language (DCL)
• In addition SQL
• Can be used from
other languages
• Is often extended to
provide common
programming
constructs (such as if-
then tests, loops,
variables, etc.)
5. SQL Data Definition
Notes
• SQL is (usually) not
case-sensitive, but
we’ll write SQL
keywords in upper
case for emphasis
• SQL statements will
be written in BOLD
COURIER FONT
• Strings in SQL are
surrounded by single
quotes:
'I AM A STRING'
• Single quotes within
a string are doubled:
'I''M A STRING'
• The empty string:''
6. SQL Data Definition
Non-Procedural Programming
• SQL is a declarative
(non-procedural)
language
• Procedural - say
exactly what the
computer has to do
• Non-procedural –
describe the required
result (not the way to
compute it)
• Example: Given a
database with tables
• Student with
attributes ID, Name,
Address
• Module with attributes
Code, Title
• Enrolment with
attributes ID, Code
• Get a list of students
who take the module
‘Database Systems’
7. SQL Data Definition
Procedural Programming
Set M to be the first Module Record /* Find module code for */
Code = ‘’ /* ‘Database Systems’ */
While (M is not null) and (Code = ‘’)
If (M.Title = ‘Database Systems’) Then
Code = M.Code
Set M to be the next Module Record
Set NAMES to be empty /* A list of student names */
Set S to be the first Student Record
While S is not null /* For each student... */
Set E to be the first Enrolment Record
While E is not null /* For each enrolment... */
If (E.ID = S.ID) And /* If this student is */
(E.Code = Code) Then /* enrolled in DB Systems */
NAMES = NAMES + S.NAME /* add them to the list */
Set E to be the next Enrolment Record
Set S to be the next Student Record
Return NAMES
8. SQL Data Definition
Non-Procedural (SQL)
SELECT Name FROM Student, Enrolment
WHERE
(Student.ID = Enrolment.ID)
AND
(Enrolment.Code =
(SELECT Code FROM Module WHERE
Title = ‘Database Systems’))
9. SQL Data Definition
SQL, the Relational Model,
and E/R Design
• SQL is based on the
relational model
• It has many of the
same ideas
• Databases that
support SQL are often
described as relational
databases
• It is not always true
to the model
• E/R designs can be
implemented in SQL
• Entities, attributes,
and relationships can
all be expressed in
terms of SQL
• Many-to-many
relationships are a
problem, so should be
removed
10. SQL Data Definition
Relations, Entities, Tables
Relational model
Relation
Tuple
Attribute
Foreign Key
Primary Key
E/R Diagram
Entity
Instance
Attribute
M:1 Relationship
SQL
Table
Row
Column or Field
Foreign Key
Primary Key
11. SQL Data Definition
Implementing E/R Designs
• Given an E/R design
• The entities become
SQL tables
• Attributes of an entity
become columns in
the corresponding
table
• Relationships may be
represented by
foreign keys
Enrolment
Student
Module
In
Has
ID
Code
Title
Name
Address
Year
Assignment
Exam
Assignment
Exam
Credits
12. SQL Data Definition
Entities and Attributes
• Each entity becomes
a table in the
database
• The name of the table
is often the name of
the entity
• The attributes become
columns of the table
with the same name
• A table called Student
• With columns for ID,
Name, Address, and
Year
Student
ID
Name
Address
Year
13. SQL Data Definition
CREATE TABLE
CREATE TABLE
<name> (
<col-def-1>,
<col-def-2>,
:
<col-def-n>,
<constraint-1>,
:
<constraint-k>)
• You supply
• A name for the table
• A list of column
definitions
• A list of constraints
(such as keys)
14. SQL Data Definition
Column Definitions
<col-name> <type>
[NULL|NOT NULL]
[DEFAULT <val>]
[constraint-1 [,
constraint-2[,
...]]]
• Each column has a
name and a type
• Common types
• INT
• REAL
• CHAR(n)
• VARCHAR(n)
• DATE
15. SQL Data Definition
Column Definitions
• Columns can be
specified as NULL or
NOT NULL
• NOT NULL columns
cannot have missing
values
• If neither is given
then columns are
assumed NULL
• Columns can be
given a default value
• You just use the
keyword DEFAULT
followed by the
value, eg:
num INT DEFAULT 0
16. SQL Data Definition
Example
CREATE TABLE Student (
stuID INT NOT NULL,
stuName VARCHAR(50) NOT NULL,
stuAddress VARCHAR(50),
stuYear INT DEFAULT 1)
Student
ID
Name
Address
Year
17. SQL Data Definition
Constraints
CONSTRAINT
<name>
<type>
<details>
• Common <type>s
• PRIMARY KEY
• UNIQUE
• FOREIGN KEY
• INDEX
• Each constraint is
given a name -
Access requires a
name, but some
others don’t
• Constraints which
refer to single
columns can be
included in their
definition
18. SQL Data Definition
Primary Keys
• Primary Keys are
defined through
constraints
• A PRIMARY KEY
constraint also
includes a UNIQUE
constraint and
makes the columns
involved NOT NULL
• The <details> for a
primary key is a list
of columns which
make up the key
CONSTRAINT <name>
PRIMARY KEY
(col1, col2, …)
19. SQL Data Definition
Unique Constraints
• As well as a single
primary key, any set
of columns can be
specified as UNIQUE
• This has the effect of
making candidate
keys in the table
• The <details> for a
unique constraint are
a list of columns
which make up the
candidate key
CONSTRAINT <name>
UNIQUE
(col1, col2, …)
20. SQL Data Definition
Example
CREATE TABLE Student (
stuID INT NOT NULL,
stuName VARCHAR(50) NOT NULL,
stuAddress VARCHAR(50),
stuYear INT DEFAULT 1,
CONSTRAINT pkStudent
PRIMARY KEY (stuID))
21. SQL Data Definition
Relationships
• Depends on the type
• 1:1 are usually not
used, or can be
treated as a special
case of M:1
• M:1 are represented
as a foreign key from
the M-side to the 1
• M:M are split into two
M:1 relationships
Enrolment
Student
Module
In
Has
ID
Code
Title
Name
Address
Year
Assignment
Exam
Assignment
Exam
Credits
22. SQL Data Definition
Representing Relationships
• The Enrolment table
• Will have columns for
the Exam and
Assignment attributes
• Will have a foreign key
to Student for the ‘has’
relationship
• Will have a foreign key
to Module for the ‘in’
relationship
Enrolment
Student
Module
In
Has
ID
Code
Title
Name
Address
Year
Assignment
Exam
Assignment
Exam
Credits
23. SQL Data Definition
Foreign Keys
• Foreign Keys are also
defined as
constraints
• You need to give
• The columns which
make up the FK
• The referenced table
• The columns which
are referenced by the
FK
CONSTRAINT <name>
FOREIGN KEY
(col1,col2,…)
REFERENCES
<table>
[(ref1,ref2,…)]
• If the FK references
the PK of <table> you
don’t need to list the
columns
24. SQL Data Definition
Example
CREATE TABLE Enrolment (
stuID INT NOT NULL,
modCode CHAR(6) NOT NULL,
enrAssignment INT,
enrExam INT,
CONSTRAINT enrPK
PRIMARY KEY (stuID, modCode),
CONSTRAINT enrStu FOREIGN KEY (stuID)
REFERENCES Student (stuID),
CONSTRAINT enrMod FOREIGN KEY (modCode)
REFERENCES Module (modCode))
25. SQL Data Definition
Next Lecture
• More SQL
• DROP TABLE
• ALTER TABLE
• INSERT, UPDATE, and DELETE
• Data dictionary
• Sequences
• For more information
• Connolly and Begg chapters 5 and 6
• Ullman and Widom 6.5
26. SQL Data Definition
Coursework
• Cw1: Entity-relationship diagram and table definitions –
deadline 29 February at 3, submit to the School Office.
• Cw2: SQL creating tables in Oracle – marked in the labs
on 20/02, latest submission by email to me 22/02.
• Labs start on the 13th of February (next week).
• This week there are still no labs, but you can start on
your own:
• Set up an Oracle account (instructions on
http://www.cs.nott.ac.uk/~nza/G51DBS)
• Start creating tables required in the first SQL
exercise (cw2)