DATA FLOATING IN THE CLOUD

Abijith Raja B — Wed, 08 Oct 2025 04:27:00 +0000

Introduction

In today’s digital world, data moves faster than ever before. From online classes to global business systems, one invisible force connects it all — the cloud.
But when we say data in the cloud, it doesn’t mean our information is literally floating in the sky. Instead, it’s stored safely in large, distributed data centers managed by powerful servers. These servers allow us to access files, photos, and applications anytime, anywhere.
Let’s explore how data is represented in six different formats used widely in data analytics and cloud platforms.

Data Formats in Cloud Analytics

Every time you store, share, or query data in the cloud, you’re likely dealing with one of these six formats:

CSV – Simple text-based, comma-separated data

SQL – Relational, structured data tables

JSON – Lightweight, flexible key-value data

Parquet – Efficient, columnar storage for big data

XML – Markup-based hierarchical data

Avro – Binary, schema-driven data for streaming

To make it easy to understand, let’s take a small dataset and represent it in all six formats.

Sample Dataset

Name	Roll_No	Course	Grade
Aadhira	201	Data Science	A
Niveth	202	AI	B+
Rahul	203	Cloud Computing	A+

1️⃣ CSV (Comma Separated Values)

CSV is one of the simplest and most human-readable formats. Each record is written in one line, and each field is separated by commas.

Example:

Name,Roll_No,Course,Grade
Aadhira,201,Data Science,A
Niveth,202,AI,B+
Rahul,203,Cloud Computing,A+

✅ Pros

Easy to read and edit
Works with almost every tool like Excel, Python, and Google Sheets

⚠️ Cons

No data types or schema
Not suitable for very large datasets

2️⃣ SQL (Structured Query Language)

SQL is the language of relational databases. It stores data in tables with defined columns and allows complex queries.

Example:

CREATE TABLE Students (
  Name VARCHAR(50),
  Roll_No INT,
  Course VARCHAR(50),
  Grade CHAR(2)
);

INSERT INTO Students VALUES
('Aadhira', 201, 'Data Science', 'A'),
('Niveth', 202, 'AI', 'B+'),
('Rahul', 203, 'Cloud Computing', 'A+');

✅ Pros

Structured and organized
Perfect for queries, filters, and joins

⚠️ Cons

Rigid schema
Not suitable for nested data

3️⃣ JSON (JavaScript Object Notation)

JSON is the go-to format for APIs and NoSQL databases. It’s lightweight and great for representing hierarchical data.

Example:

[
  {"Name": "Aadhira", "Roll_No": 201, "Course": "Data Science", "Grade": "A"},
  {"Name": "Niveth", "Roll_No": 202, "Course": "AI", "Grade": "B+"},
  {"Name": "Rahul", "Roll_No": 203, "Course": "Cloud Computing", "Grade": "A+"}
]

✅ Pros

Easy to parse in web apps
Supports nested structures

⚠️ Cons

No strict schema
Becomes bulky for large datasets

4️⃣ Parquet (Columnar Storage Format)

Parquet is built for big data analytics. It stores data column-wise, improving compression and query performance — ideal for tools like AWS Athena or Spark.

Example:

Name: ["Aadhira", "Niveth", "Rahul"]
Roll_No: [201, 202, 203]
Course: ["Data Science", "AI", "Cloud Computing"]
Grade: ["A", "B+", "A+"]

✅ Pros

High compression
Fast analytical queries

⚠️ Cons

Not human-readable
Needs specialized tools (e.g., PyArrow, Spark)

5️⃣ XML (Extensible Markup Language)

XML represents data using tags. It’s structured and self-descriptive — often used in web services or configurations.

Example:

<Students>
  <Student>
    <Name>Aadhira</Name>
    <Roll_No>201</Roll_No>
    <Course>Data Science</Course>
    <Grade>A</Grade>
  </Student>
  <Student>
    <Name>Niveth</Name>
    <Roll_No>202</Roll_No>
    <Course>AI</Course>
    <Grade>B+</Grade>
  </Student>
  <Student>
    <Name>Rahul</Name>
    <Roll_No>203</Roll_No>
    <Course>Cloud Computing</Course>
    <Grade>A+</Grade>
  </Student>
</Students>

✅ Pros

Self-descriptive and structured
Great for hierarchical data

⚠️ Cons

Verbose and heavy
Slower to parse

6️⃣ Avro (Row-Based Storage Format)

Avro is used for data streaming and serialization. It stores data in binary along with a schema — ensuring compactness and compatibility over time.

Schema Example:

{
  "type": "record",
  "name": "Student",
  "fields": [
    {"name": "Name", "type": "string"},
    {"name": "Roll_No", "type": "int"},
    {"name": "Course", "type": "string"},
    {"name": "Grade", "type": "string"}
  ]
}

✅ Pros

Compact binary format
Schema evolution supported

⚠️ Cons

Not human-readable
Requires Avro libraries

Conclusion

Each data format serves a unique purpose in the cloud ecosystem:

Use Case

Simple exports/logs -> CSV
Relational databases -> SQL
APIs or nested data -> JSON
Big data analytics -> Parquet
Hierarchical data -> XML
Real-time streaming -> Avro

In essence, data in the sky isn’t just about storage — it’s about choosing the right format for the right purpose.

Hands-on Exploration of MongoDB with Practical Queries

Abijith Raja B — Tue, 26 Aug 2025 09:03:11 +0000

Hello everyone,

I’ve recently started my journey into the world of databases and chose MongoDB as my starting point. As one of the most popular NoSQL databases, MongoDB offers flexibility by storing data in JSON-like documents instead of rigid relational tables. This makes it particularly effective for handling unstructured or evolving datasets while ensuring scalability and adaptability—qualities that are vital for modern applications.

1.Getting Started
I began by installing MongoDB and setting up a database named Business with a collection called Review. This served as the foundation for my experiments with data storage and manipulation.

2.Importing the Dataset
To work with real-world data, I imported a dataset named yelp.json containing about 1000 records. This dataset provided the context for applying queries and understanding how MongoDB processes data.

3.Inserting Multiple Documents with insertMany()
After loading the dataset, I practiced adding additional records by inserting 10 documents into the Review collection using the insertMany() method.

After inserting the datasets

4. Query to Find Top 5 Businesses with Highest Average Rating
I utilized the aggregate function to identify the top five businesses based on their average ratings.

db.reviews.aggregate([
{ $group: { _id: "$business_id", avg_rating: { $avg: "$stars" } } },
{ $sort: { avg_rating: -1 } },
{ $limit: 5 }
])

5. Query to Count Reviews Containing the Word “good”
To measure customer sentiment, I applied the countDocuments method to find the number of reviews containing the keyword “good.”

db.reviews.countDocuments({ text: /good/i })

6.Query to Retrieve Reviews for a Specific Business ID

I retrieved all reviews linked to a particular business ID using the find() method.

db.reviews.find({ business_id: "B101" })

7.Update a review and delete a record
7.1: Updating a Review
I updated a single document in the collection by using the updateOne() method.

db.reviews.updateOne(
{ review_id: "R107" },
{
$set: {
stars: 4,
text: "The food was good and the service was friendly. I enjoyed the atmosphere, but I felt it was a bit crowded."
}
}
);

7.2: Deleting a Record
I removed one document from the collection by applying the deleteOne() method.

db.reviews.deleteOne(
{ review_id: "R104" }
);

Conclusion

This mini-project provided me with valuable hands-on experience in MongoDB. I explored essential operations such as inserting documents, retrieving data, applying filters, performing aggregations, and executing update/delete actions.

Through this, I gained an appreciation of how MongoDB handles dynamic, unstructured data compared to traditional relational databases. Moving forward, I am excited to explore advanced MongoDB features like replication, sharding, and performance optimization, which are crucial for developing highly scalable, real-world systems.

DEV Community: Abijith Raja B

DATA FLOATING IN THE CLOUD

Hands-on Exploration of MongoDB with Practical Queries