Data in Cloud

1. CSV (Comma Separated Values)

Explanation:
CSV is a simple text format where each row represents a record, and columns are separated by commas. It’s easy to read and widely supported.

Example:

Name,RegisterNo,Subject,Marks
Alice,101,Math,95
Bob,102,Science,88
Charlie,103,English,92

Use case: Quick data exchange, spreadsheets, basic analytics.

2. SQL (Relational Table Format)

Explanation:
SQL stores data in structured tables with rows and columns. You can query this data using SQL commands.

Example Table Creation and Data Insert:

CREATE TABLE Students (
Name VARCHAR(50),
RegisterNo INT,
Subject VARCHAR(50),
Marks INT
);

INSERT INTO Students (Name, RegisterNo, Subject, Marks) VALUES
('Alice', 101, 'Math', 95),
('Bob', 102, 'Science', 88),
('Charlie', 103, 'English', 92);

Use case: Database storage, complex queries, data integrity.
**

1. JSON (JavaScript Object Notation)**

Explanation:
JSON is a lightweight data format often used for APIs. It stores data as key-value pairs, making it easy to read for humans and machines.

Example:

[
{"Name": "Alice", "RegisterNo": 101, "Subject": "Math", "Marks": 95},
{"Name": "Bob", "RegisterNo": 102, "Subject": "Science", "Marks": 88},
{"Name": "Charlie", "RegisterNo": 103, "Subject": "English", "Marks": 92}
]

Use case: Data exchange between web apps, APIs, and NoSQL databases.

4. Parquet (Columnar Storage Format)

Explanation:
Parquet is a columnar storage format optimized for big data. It stores data by columns instead of rows, improving performance for analytical queries.

Python Example (Creating Parquet):

import pandas as pd

data = {
"Name": ["Alice", "Bob", "Charlie"],
"RegisterNo": [101, 102, 103],
"Subject": ["Math", "Science", "English"],
"Marks": [95, 88, 92]
}

df = pd.DataFrame(data)
df.to_parquet("students.parquet")

Use case: Big data analytics, Spark, Hive, fast columnar queries.

5. XML (Extensible Markup Language)

Explanation:
XML is a markup language that stores data in a hierarchical structure using tags. It’s human-readable and widely used in legacy systems.

Example:

Alice
101
Math
95


Bob
102
Science
88


Charlie
103
English
92

Use case: Config files, legacy systems, document storage.

6. Avro (Row-based Storage Format)

Explanation:
Avro is a row-based storage format often used with Hadoop. It’s compact, efficient, and supports schema evolution (i.e., adding new fields without breaking old data).

Example Schema + Data (JSON representation of Avro):

{
"type": "record",
"name": "Student",
"fields": [
{"name": "Name", "type": "string"},
{"name": "RegisterNo", "type": "int"},
{"name": "Subject", "type": "string"},
{"name": "Marks", "type": "int"}
]
}

Data:

[
{"Name": "Alice", "RegisterNo": 101, "Subject": "Math", "Marks": 95},
{"Name": "Bob", "RegisterNo": 102, "Subject": "Science", "Marks": 88},
{"Name": "Charlie", "RegisterNo": 103, "Subject": "English", "Marks": 92}
]

Use case: Big data storage, efficient serialization, Hadoop ecosystem.