The Edexcel ICT A-Level Notes for Unit 3 document is a comprehensive guide covering all the essential topics and concepts students need to know to excel in their ICT A-Level exams. This document has been designed to help students understand the complex concepts of Unit 3, which covers data modellin...
This is good, But my suggestion is the note should be completed with the relevant syllabus and its topic numbers
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Data validation:
● Data validation is the process of checking the integrity, accuracy and structure of data
that is to be used.
● This process of verifying data before it’s allowed into a computer system is to ensure
that the data has not been altered, compromised or corrupted in any way during
transmission.
● It is important before it is able to mitigate any project defects.
Data redundancy:
1. Define and explain Data Redundancy:
Data redundancy is a condition that is made in a database when a piece of data
exists in multiple places. It stops the unnecessary replication of data within a
database.
● Advantages of data redundancy include:
○ Improved data reliability - The data is ensured to be complete and
accurate as the data redundancy double checks data to confirm it is
right and fully completed.
○ Better data security - It prevents unauthorised access and activities
such as data breaches or cyberattacks to take place in the database.
2. What can data redundancy lead to?
● Data inconsistency: Data inconsistency is when there are two or more
tables within a database that hold the same data but it is received from
different inputs. Data redundancy can lead to data inconsistency due to the
idea that the different files could end up containing different data about the
object and therefore it becomes unreliable information.
● Larger storage requirements: Since data inconsistency consists of two or
more tables, if it occurs, it means that the data will have a different
arrangement and it could be written in many different programming
languages. As a result, a larger storage space will be needed.
, ● Increased operational costs: Due to larger storage requirements, there will
be an increase in operational and access costs.
Data normalisation:
Definition:
The process used to organise the data or information in a database into columns.
This is used to minimise data duplication-can cause an increase in database size
and decrease in performance-throughout the database
The processes of restructuring a relation database based on the “normal forms”.
Normalising a database structures it in which can increase data integrity in the
database.
Advantages Disadvantages
Increases the data integrity of the Accurate knowledge of the different
database. typical structures is necessary in order
to normalise a database.
It reduces data redundancy making the Organising data in a specific way
database more reliable and consistent. through normalisation can reduce a
database's flexibility.
It helps to simplify the database so it’s When a database is normalized, it may
easier to process, develop and maintain require more tables and additional join
the database. operations to access the data, resulting
in increased storage requirements.
Non-normalized database
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Types of normalisation forms-
● First normalisation:
- A normalisation form that ensures that there are no 2 same data
entries in a group/table
For a table to be considered a first normalisation form(1NF) it needs to fulfil the
following rules:
➔ Each cell should contain a single(atomic) value
➔ Values stored in a column must be consistent
➔ All columns should have unique names.
➔ Rows and columns are not ordered
● Second normalisation:
For a table to be considered a second normalisation form(2NF) it needs to fulfil the
following rules:
➔ The table must already be in 1NF
➔ Non-key columns(columns that have no key attributes) must depend on the
primary key
(2NF is commonly only a issue when a composite primary key-a primary key made
of 2 or more columns- is used)
● Third normalisation:
For a table to be considered a third normalisation form(3NF) it needs to fulfil the
following rules:
➔ It should be in 2NF
➔ It should not have any transitive functional dependencies
, transitive functional dependency: an indirect relationship between values in the same
table that causes a functional dependency.
Big data:
What is ‘Big Data’?
Big Data is a large collection of data that is gathered from multiple sources and used to
analyse trends and behaviours. It can process big amounts of structured, semi-structured
and unstructured data to extract insightful information which can be used to form and design
a pattern that businesses can use to better their product/service and therefore achieve
business growth.
Big Data is defined by the 5 V’s. These are:
1. Volume:
This relates to the size of data and the name itself suggests that it will be a large amount.
The value of the data is determined by its size. An example of this is data feeds on social
media or web pages and apps. This is why the volume of big data is essential as the large
amount of data will need to be stored.
2. Velocity:
Velocity refers to the speed at which Big Data is processed. Examples of times that Big data
deals with velocity is visible in sources such as application logs, networks, social media,
mobile devices, etc.
3. Variety:
Variety involves the different types of data. There are three types which are structured, semi-
structured and unstructured.
- Structured: Data that is organised and usually refers to data that has a defined
length and format.
- Semi-structured: Data that has not been organised in a special way/does not follow
a specific structure (such as a database) but still has some form of structure to it.
- Unstructured: Data that is unorganised and doesn’t fit into the traditional database
structure.
4. Veracity:
This characteristic involves the inconsistency and uncertainty of data. Some data that is
available can become messy and the quality of it can be hard to control and adjust to the
needs of the user. This is also because Big Data can be in different forms of data types and
come from many different sources.
5. Value:
Data is something that is collected together and turned efficient for processing. That is why
in Big Data, the value of the data that is processed and generated is significant. Large
amounts of data is useless to a company if it contains no value and cannot be turned into
something useful. That is why data has to be converted into something valuable in order to
have value and information can be extracted from it.
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