What is data storage and why is it important in Computer Science?

Data storage refers to the recording of information in a storage medium. It is crucial in Computer Science because it allows for the retention and retrieval of data, enabling computers to perform tasks, store user information, and maintain system operations. Effective data storage solutions are essential for managing large volumes of data efficiently, which is a key aspect of the Cambridge IGCSE Computer Science curriculum.

How does file compression work and why is it used?

File compression reduces the size of a file by encoding its data more efficiently. This is achieved through algorithms that eliminate redundancies and unnecessary information. Compression is used to save storage space and reduce the time required to transfer files over the internet. Understanding file compression is part of the Cambridge IGCSE Computer Science syllabus, as it helps students learn about efficient data management and transmission.

What are the differences between lossless and lossy compression?

Lossless compression reduces file size without losing any data, allowing the original file to be perfectly reconstructed. Examples include ZIP and PNG formats. Lossy compression, on the other hand, removes some data permanently to achieve higher compression rates, which may result in a loss of quality. JPEG and MP3 are common lossy formats. Both methods are important in data representation and are covered in the Cambridge IGCSE Computer Science curriculum.

Why is understanding data representation important for data storage?

Data representation involves converting information into a format that can be easily processed by computers. Understanding this is crucial for data storage because it affects how data is encoded, stored, and retrieved. Efficient data representation ensures that storage space is used optimally and that data can be accessed quickly. This concept is a fundamental part of the Cambridge IGCSE Computer Science course.

What are some common file formats and their uses in data storage?

Common file formats include text files (TXT), which store plain text; image files (JPEG, PNG), which store visual data; audio files (MP3, WAV), which store sound; and video files (MP4, AVI), which store moving images. Each format is designed to store specific types of data efficiently, balancing quality and storage space. Understanding these formats is essential for students studying data storage in the Cambridge IGCSE Computer Science curriculum.

Why is "Treating lossy compression as 'bad'" a common mistake in Data Storage and File Compression?

Why it happens: Data loss sounds negative. How to avoid it: Lossy is APPROPRIATE for media where some quality loss is imperceptible (audio at MP3, web images at JPEG). It enables streaming and small file sizes that lossless can't match.

Why is "Saying ZIP is lossy" a common mistake in Data Storage and File Compression?

Why it happens: Confusing 'compressed' with 'lossy'. How to avoid it: ZIP is LOSSLESS. The original file can be perfectly recovered after extraction. Otherwise it would be useless for documents, code, etc.

Why is "Stating compression types without when-to-use" a common mistake in Data Storage and File Compression?

Why it happens: Treating the question as definitional. How to avoid it: Mark schemes for 6+ mark compression questions ALWAYS expect a use-case for each type. Memorise: lossy for streaming/web; lossless for documents/code/archives.

Data RepresentationCambridge IGCSE Computer Science (0478)

Data Storage and File Compression

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Data Storage and File Compression Study Notes — Cambridge IGCSE Computer Science 0478 (2026-2028 syllabus)

Storage units (KB to TB) and the two big families of compression — lossy and lossless. Match the compression to the use case for top marks.

What you’ll learn

Mapped to the Cambridge IGCSE 0478 syllabus (2026-2028).

1.1.10 — Identify common storage units (B, KB, MB, GB, TB).
1.1.11 — Distinguish between lossy and lossless compression.
1.1.12 — Identify use cases for each compression type.
1.1.13 — Apply run-length encoding (RLE) as an example of lossless compression.

Storage units

Each step ×1024 (base-2). KB → MB → GB → TB.

Computer-science contexts use base-2 units (powers of 1024) for storage:

Unit	Base-2	Base-10 (SI)	Approx. real-world
Byte (B)	8 bits	8 bits	One ASCII character
Kilobyte (KB)	2¹⁰ = 1,024 B	1,000 B	A short email
Megabyte (MB)	2²⁰ ≈ 1.05 M B	1,000,000 B	A 3-min MP3
Gigabyte (GB)	2³⁰ ≈ 1.07 B B	1,000,000,000 B	A 1-hour HD video
Terabyte (TB)	2⁴⁰ ≈ 1.10 T B	10¹² B	An average laptop SSD
Petabyte (PB)	2⁵⁰	10¹⁵ B	Large data centres

Why two systems? Computer hardware naturally addresses memory in powers of 2 (because addressing is binary). Storage manufacturers historically used base-10 (1 GB = 10⁹ B) to make capacity sound bigger. The discrepancy is why a "1 TB" hard drive shows up as ~931 GB in your operating system.

Cambridge tip. 0478 uses base-2 (1024). Either accepted by mark scheme but show your method.

Each step is ×1024 (base-2) or ×1000 (base-10).
0478 prefers base-2.
Always show conversion working.

Lossy vs lossless compression

Two families. Match to use case.

Compression reduces file size. There are two distinct families.

Lossy compression.

Permanently REMOVES data to shrink the file. Decompression cannot recover the original — only an approximation.

Examples: JPEG (images), MP3 (audio), MPEG / H.264 (video).
How it works: removes data the human senses are insensitive to. JPEG drops fine colour detail; MP3 drops audio frequencies most people can't hear; video codecs drop frames or motion vectors.
Trade-off: massive size reduction (often 10×-100×) at the cost of some quality loss.

Lossless compression.

Reduces size WITHOUT removing data. Decompression perfectly reconstructs the original.

Examples: ZIP, PNG, FLAC, GIF, RLE.
How it works: spots redundancies (repeated patterns, common substrings) and replaces them with shorter codes.
Trade-off: smaller size reduction (often 2×-5×) but no quality loss.

Choosing between them.

Use case	Choose	Why
Web image	Lossy (JPEG)	Bandwidth matters; small quality loss imperceptible
Logo / diagram	Lossless (PNG)	Sharp edges + transparency; needs precision
Music streaming	Lossy (MP3, AAC)	Bandwidth, mobile data
Audio production	Lossless (FLAC, WAV)	Editing requires the original
Document / source code	Lossless (ZIP)	Even one byte change breaks it
Video streaming	Lossy (H.264, AV1)	File sizes otherwise enormous
Medical imaging	Lossless	Diagnostic accuracy depends on every pixel

Cambridge tip. "When would you use X?" questions are routine. Memorise the use-case table.

Lossy: data permanently lost; smaller files; for media.
Lossless: data fully recovered; larger files; for code/docs.
Lossy ≠ bad — appropriate for streaming.
Lossless ≠ overkill — essential for precision content.

Run-length encoding (RLE) — the simplest lossless algorithm

Replace runs of identical data with count + value.

Run-length encoding is the simplest lossless compression method.

Algorithm: scan the data; whenever you find consecutive identical bytes, replace them with a (count, value) pair.

Worked example. Input: AAAABBBCCDAA.

AAAA → 4A
BBB → 3B
CC → 2C
D → 1D
AA → 2A

Output: 4A3B2C1D2A. Original: 12 characters. Compressed: 10 characters (5 pairs).

When RLE works well:

Data with LONG runs of identical bytes.
e.g., monochrome images with large solid areas, simple icon graphics.
Animation frames where most pixels don't change between frames.

When RLE works poorly:

Data with no repetition.
e.g., text in natural language, or encrypted data — where each byte is essentially random.
In the worst case, RLE can make a file LARGER (every byte becomes 2 bytes: count of 1 + value).

Why teach RLE? It's the easiest compression to understand, and it's still used in some image formats (BMP, TIFF) and printer protocols. More sophisticated compression (LZ77 in ZIP/PNG, Huffman coding) builds on the same redundancy idea.

Cambridge tip. RLE questions ask you to (a) compress a string and (b) explain how it works and when it's effective. Top-band candidates note BOTH the use cases and the limitations.

Run + count + character.
Effective for data with long runs.
Ineffective for varied data.
Foundation for understanding more advanced compression.

See the full worked example for data storage and file compression →

How it’s examined

Storage-unit conversions and lossy-vs-lossless distinctions appear most series. RLE worked examples (compressing a given string) appear about half the time. Examiner reports flag candidates who don't include WHEN each compression type is appropriate.

Sources: Cambridge IGCSE Computer Science 0478 syllabus (2026-2028); 0478 Examiner Reports 2022-2024; 0478/12 May/Jun 2024 question paper and mark scheme. Last reviewed 2026-05-09.

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Step-by-step worked examples — Data Storage and File Compression

Step-by-step solutions to past-paper-style questions on data storage and file compression, written exactly the way a tutor would explain them at the board.

1Storage unit conversions (4 marks)
Core• units
Question
Convert 5 GB into bytes. (4 marks)
Step-by-step solution
1. Step 1
  Use the chain. 1 KB = 1024 bytes. 1 MB = 1024 KB. 1 GB = 1024 MB. (Cambridge uses base-2 / 1024 powers; some real-world systems use base-10 / 1000 powers.)
2. Step 2
  Multiply. 5 GB × 1024 MB × 1024 KB × 1024 bytes = 5 × 1024³ bytes.
3. Step 3
  Calculate. 1024³ = 1,073,741,824. 5 × 1,073,741,824 = 5,368,709,120 bytes.
Answer
5 GB = 5,368,709,120 bytes (≈ 5 × 10⁹).
Examiner tip
Cambridge mark schemes accept BOTH base-2 (1024) and base-10 (1000) conversions, but prefer 1024. Show your method.
2Lossy vs lossless compression (6 marks)
Extended• Adapted from 0478/12 May/Jun 2024 Q5• compression
Question
Distinguish between lossy and lossless compression. Give one example of each and one situation where each is appropriate. (6 marks)
Step-by-step solution
1. Step 1
  Lossy (2 marks). Removes data permanently to reduce file size. The original cannot be recovered. Reduces quality. Examples: JPEG (images), MP3 (audio), MPEG (video).
2. Step 2
  Lossless (2 marks). Reduces file size WITHOUT removing data. The original can be perfectly reconstructed. Examples: ZIP, PNG, FLAC.
3. Step 3
  When lossy is appropriate (1 mark). When some quality loss is acceptable for much smaller files — e.g., music streaming, web images, video calls.
4. Step 4
  When lossless is appropriate (1 mark). When perfect reproduction is needed — e.g., text documents, source code, financial records, archival photos.
Answer
Lossy: data permanently removed (JPEG, MP3); good for streaming/web. Lossless: data fully recoverable (ZIP, PNG); essential for documents, code, archives.
3Run-length encoding (RLE) (5 marks)
Extended• compression, RLE
Question
Apply RLE to compress: AAAABBBCCDAA. Show the result and explain how RLE works. (5 marks)
Step-by-step solution
1. Step 1
  RLE works by replacing consecutive identical characters with a count + the character. (1 mark)
2. Step 2
  Identify runs. AAAA = 4 As. BBB = 3 Bs. CC = 2 Cs. D = 1 D. AA = 2 As.
3. Step 3
  Encode. 4A 3B 2C 1D 2A.
4. Step 4
  Compare sizes. Original: 12 characters. Encoded: 10 characters (or 5 pairs). RLE works best when there are LONG runs of identical data.
Answer
Compressed: 4A3B2C1D2A. RLE replaces runs of identical characters with count+character pairs. Effective for data with long repeats; ineffective for varied data.
Examiner tip
RLE is one of the simplest lossless compression algorithms. Mark scheme typically wants the output PLUS an explanation of how it works.

Key Formulae — Data Storage and File Compression

The formulae you need to memorise for data storage and file compression on the Cambridge IGCSE 0478 paper, with every variable defined in plain English and a note on when to use it.

Storage unit conversions (base-2)
$1\ \text{KB} = 2^{10}\ \text{B},\ 1\ \text{MB} = 2^{20}\ \text{B},\ 1\ \text{GB} = 2^{30}\ \text{B},\ 1\ \text{TB} = 2^{40}\ \text{B}$
When to use
Converting between storage units in computer-science contexts.
Example
5 GB = 5 × 2³⁰ ≈ 5.37 × 10⁹ bytes.

Key Definitions and Keywords — Data Storage and File Compression

Definitions to memorise and the exact keywords mark schemes credit for data storage and file compression answers — sharpened from recent examiner reports for the 2026 0478 sitting.

Byte
8 bits. The fundamental unit of digital storage.
Kilobyte (KB)
1,024 bytes (base-2) or 1,000 bytes (base-10 / SI).
Megabyte (MB)
1,024 KB ≈ 1 million bytes.
Gigabyte (GB)
1,024 MB ≈ 1 billion bytes.
Terabyte (TB)
1,024 GB ≈ 1 trillion bytes.
Compression
Examiner keyword
Reducing file size to save storage space and transmission time.
Lossy compression
Examiner keyword
Compression that PERMANENTLY removes data to reduce size. Original cannot be reconstructed. e.g., JPEG, MP3.
Lossless compression
Examiner keyword
Compression that reduces size WITHOUT data loss. Original is fully recoverable. e.g., ZIP, PNG, FLAC.
Run-length encoding (RLE)
Examiner keyword
Lossless compression replacing runs of identical data with a count + value. Effective for repetitive data.

Common Mistakes and Misconceptions — Data Storage and File Compression

The traps other students keep falling into on data storage and file compression questions — taken from recent Cambridge IGCSE 0478 examiner reports and mark schemes — and how to avoid them.

✕Treating lossy compression as 'bad'
Why it happens
Data loss sounds negative.
How to avoid it
Lossy is APPROPRIATE for media where some quality loss is imperceptible (audio at MP3, web images at JPEG). It enables streaming and small file sizes that lossless can't match.
✕Saying ZIP is lossy
Why it happens
Confusing 'compressed' with 'lossy'.
How to avoid it
ZIP is LOSSLESS. The original file can be perfectly recovered after extraction. Otherwise it would be useless for documents, code, etc.
✕Stating compression types without when-to-use
Why it happens
Treating the question as definitional.
How to avoid it
Mark schemes for 6+ mark compression questions ALWAYS expect a use-case for each type. Memorise: lossy for streaming/web; lossless for documents/code/archives.

Practice questions

Exam-style questions with step-by-step worked solutions. Try one before checking the method.

Past paper style quiz

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Data Storage and File Compression — frequently asked questions

The things students keep getting wrong in this sub-topic, answered.

Data Storage and File Compression

Short Study Notes in the form of Slides

Detailed Study Notes

What you’ll learn

How it’s examined

1Storage unit conversions (4 marks)

2Lossy vs lossless compression (6 marks)

3Run-length encoding (RLE) (5 marks)

Storage unit conversions (base-2)

Byte

Kilobyte (KB)

Megabyte (MB)

Gigabyte (GB)

Terabyte (TB)

Compression

Lossy compression

Lossless compression

Run-length encoding (RLE)

✕Treating lossy compression as 'bad'

✕Saying ZIP is lossy

✕Stating compression types without when-to-use

Practice questions

Past paper style quiz

Assess your understanding

Data Storage and File Compression — frequently asked questions