Data in Cloud

Jenisha E — Thu, 09 Oct 2025 15:26:56 +0000

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.
**

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:

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

NoSQL - MongoDB Hands-On

Jenisha E — Mon, 25 Aug 2025 14:21:25 +0000

I recently started exploring NoSQL databases, and MongoDB caught my attention because it stores data in a simple, JSON-like format. Instead of using rows and columns like SQL, MongoDB feels more natural — almost like handling real-world objects.

For practice, I picked the Yelp dataset. Imagine having access to thousands of businesses, ratings, and reviews — it’s perfect for testing queries.

I uploaded the dataset into MongoDB

1.Insert at least 10 records manually
To start working with MongoDB, I first inserted 10 sample business records manually into my businesses collection. This step helps in understanding how MongoDB stores data in a JSON-like structure.

Each record represents a business with details like name, city, rating, and category.

2.Query to find top 5 businesses with highest average rating
Next, I wanted to see the top 5 businesses based on their ratings. I used MongoDB’s sort() function to arrange businesses in descending order of ratings and limited the results to 5.

This query is useful when we want to highlight the best-rated businesses.

3.Query to count how many reviews contain the word "good"
To analyze customer feedback, I searched for all reviews that contained the word “good”. I used a regular expression in MongoDB to match the word inside the review text.

The query then counts the total number of matching reviews. This kind of query helps us understand customer sentiment.

4.Query to get all reviews for a specific business ID
In real-world applications, we often need to check all reviews for a particular business. By using the business_id as a filter, I retrieved all the reviews linked to that business.

This helps in analyzing customer opinions for a single location or brand.

5.Update to get all review and delete a record

    Finally, I explored how to update and delete records in MongoDB.

With updateOne(), I modified an existing review to test how updates work.

With deleteOne(), I removed a record from the collection.

DEV Community: Jenisha E

Data in Cloud

NoSQL - MongoDB Hands-On