Data in the Cloud: Understanding 6 Common Data Formats in Analytics

AISHWARYA M 23IT007 — Sat, 11 Oct 2025 03:45:23 +0000

When working with data in the cloud or in analytics pipelines, the format of your data matters. Choosing the right format can impact performance, storage cost, and ease of use.

In this article, we’ll explore six widely used data formats:

CSV (Comma-Separated Values)
SQL (Relational Tables)
JSON (JavaScript Object Notation)
Parquet (Columnar Storage)
XML (Extensible Markup Language)
Avro (Row-based Storage)

*SAMPLE DATASET: *

Name Register_No Subject Marks
Aisha 101 Math 85
Rahul 102 Science 90
Meera 103 English 78

1️⃣ CSV (Comma-Separated Values)

What is CSV?
CSV is the simplest data format. Each row represents a record, and values are separated by commas. It’s human-readable and widely supported by Excel, Python, R, and databases.

Dataset in CSV:

Name,Register_No,Subject,Marks
Aisha,101,Math,85
Rahul,102,Science,90
Meera,103,English,78

2️⃣ SQL (Relational Table Format)

What is SQL format?
SQL databases store data in tables with rows and columns. Data is structured, and queries are run using SQL commands.

Dataset in SQL:

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

INSERT INTO Students (Name, Register_No, Subject, Marks) VALUES
('Aisha', 101, 'Math', 85),
('Rahul', 102, 'Science', 90),
('Meera', 103, 'English', 78);

3️⃣ JSON (JavaScript Object Notation)

What is JSON?
JSON is a lightweight, human-readable data format often used in APIs. It stores data as key-value pairs, arrays, and nested objects.

Dataset in JSON:

[
{
"Name": "Aisha",
"Register_No": 101,
"Subject": "Math",
"Marks": 85
},
{
"Name": "Rahul",
"Register_No": 102,
"Subject": "Science",
"Marks": 90
},
{
"Name": "Meera",
"Register_No": 103,
"Subject": "English",
"Marks": 78
}
]

4️⃣ Parquet (Columnar Storage Format)

What is Parquet?
Parquet is a binary columnar storage format optimized for big data analytics. Instead of storing row by row, it stores column by column, which speeds up aggregations and reduces storage.

Dataset in Parquet (conceptual view):

Columns:
Name → ["Aisha", "Rahul", "Meera"]
Register_No → [101, 102, 103]
Subject → ["Math", "Science", "English"]
Marks → [85, 90, 78]

5️⃣ XML (Extensible Markup Language)

What is XML?
XML is a markup language that uses tags to structure data. It’s verbose but still used in enterprise systems and web services.

Dataset in XML:

<Students>
  <Student>
    <Name>Aisha</Name>
    <Register_No>101</Register_No>
    <Subject>Math</Subject>
    <Marks>85</Marks>
  </Student>
  <Student>
    <Name>Rahul</Name>
    <Register_No>102</Register_No>
    <Subject>Science</Subject>
    <Marks>90</Marks>
  </Student>
  <Student>
    <Name>Meera</Name>
    <Register_No>103</Register_No>
    <Subject>English</Subject>
    <Marks>78</Marks>
  </Student>
</Students>

6️⃣ Avro (Row-based Storage Format)

What is Avro?
Avro is a row-based binary format developed by Apache. It’s schema-driven and commonly used in Kafka and Hadoop ecosystems. Unlike Parquet, it stores data row by row.

Dataset in Avro (conceptual view):

Schema (JSON-based):

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

{"Name": "Aisha", "Register_No": 101, "Subject": "Math", "Marks": 85}
{"Name": "Rahul", "Register_No": 102, "Subject": "Science", "Marks": 90}
{"Name": "Meera", "Register_No": 103, "Subject": "English", "Marks": 78}

Conclusion

Each data format serves a different purpose:

CSV → Simple, universal

SQL → Structured, relational

JSON → Flexible, great for APIs

Parquet → Columnar, optimized for analytics

XML → Structured but verbose

Avro → Row-based, schema-driven

In modern cloud data platforms (like AWS, GCP, Azure), Parquet and Avro are heavily used for large-scale analytics, while CSV, JSON, and SQL remain popular for data interchange.

Managing Yelp Reviews in MongoDB Compass

AISHWARYA M 23IT007 — Wed, 10 Sep 2025 06:27:20 +0000

Working with databases can sometimes feel intimidating, but tools like MongoDB Compass make it much easier by providing a visual interface for managing and analyzing collections. In this blog, we’ll walk through some practical steps using screenshots from a sample yelpDB.reviews collection.

1. Viewing and Aggregating Documents

The first step is exploring the documents stored inside the reviews collection. MongoDB Compass provides an Aggregations tab that allows us to run aggregation pipelines visually.

2. Searching with Filters

Often, we need to filter reviews to find specific insights. Compass makes this easy with its filter bar.

3. Finding Documents by ID

If you want to look up a specific review or restaurant entry, you can filter using its business_id

4. Deleting Documents

Sometimes, data cleanup is necessary. In Compass, you can flag documents for deletion:
Select the document.
Click Delete.
Confirm deletion.
The document will be permanently removed from the collection.

5. Updating Documents

Compass also allows editing documents directly:
Click the pencil icon beside a field.
Modify the value (e.g., updating a business_id or correcting a review text).
Click Update to save the change.

CONCLUSION

Using MongoDB Compass, we performed key database operations visually:
Aggregating data (average ratings).
Filtering with regex.
Querying by IDs.
Deleting and updating documents.

DEV Community: AISHWARYA M 23IT007

Data in the Cloud: Understanding 6 Common Data Formats in Analytics

Managing Yelp Reviews in MongoDB Compass