📊 Understanding 6 Common Data Formats in Data Analytics

Rethan Kumar cv — Tue, 07 Oct 2025 04:21:58 +0000

Data formats are the backbone of analytics — they determine how efficiently data is stored, transferred, and processed. Whether you’re working with simple spreadsheets or massive big data systems, understanding data formats helps you pick the right tool for the job.

In this blog, let’s explore six popular data formats used in data analytics:

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

We’ll use a simple dataset throughout this article to represent the same data in all formats.

🧩 Our Sample Dataset

Let’s take a small dataset of student marks:

Name	Register Number	Subject	Marks
Riya Sharma	101	Math	95
Arjun Patel	102	Science	88
Meera Nair	103	English	92

1️⃣ CSV (Comma Separated Values)

CSV is the simplest and most human-readable data format. It stores data as plain text, where each line represents a record and values are separated by commas.

Advantages:

Easy to read and write
Supported by almost every data tool (Excel, Python, etc.)

Disadvantages:

No data types (everything is text)
Doesn’t handle nested or hierarchical data well

Example (CSV):

Name,Register Number,Subject,Marks
Riya Sharma,101,Math,95
Arjun Patel,102,Science,88
Meera Nair,103,English,92

2️⃣ SQL (Relational Table Format)

SQL data is stored in tables with defined columns and data types. It’s used in relational databases like MySQL, PostgreSQL, and SQLite.

Advantages:

Structured and enforceable schema
Supports powerful querying

Disadvantages:

Rigid structure
Not suitable for unstructured data

Example (SQL):

CREATE TABLE Students (
    Name VARCHAR(50),
    RegisterNumber INT,
    Subject VARCHAR(30),
    Marks INT
);

INSERT INTO Students (Name, RegisterNumber, Subject, Marks) VALUES
('Riya Sharma', 101, 'Math', 95),
('Arjun Patel', 102, 'Science', 88),
('Meera Nair', 103, 'English', 92);

3️⃣ JSON (JavaScript Object Notation)

JSON stores data as key-value pairs, commonly used in APIs and web applications. It supports nested structures and is easily parsed by programming languages.

Advantages:

Human-readable
Supports nested and complex data structures
Widely used in web and API data

Disadvantages:

Slightly larger file size
Parsing overhead compared to CSV

Example (JSON):

[
  {
    "Name": "Riya Sharma",
    "RegisterNumber": 101,
    "Subject": "Math",
    "Marks": 95
  },
  {
    "Name": "Arjun Patel",
    "RegisterNumber": 102,
    "Subject": "Science",
    "Marks": 88
  },
  {
    "Name": "Meera Nair",
    "RegisterNumber": 103,
    "Subject": "English",
    "Marks": 92
  }
]

4️⃣ Parquet (Columnar Storage Format)

Parquet is a columnar storage format used in big data frameworks like Apache Spark and Hadoop. It stores data by columns instead of rows — making analytical queries faster and storage smaller.

Advantages:

Highly compressed and efficient
Great for analytical queries (e.g., aggregate functions)
Supports complex data types

Disadvantages:

Not human-readable
Best used with big data tools

Example (Conceptual Representation):

Column 1: Name -> ["Riya Sharma", "Arjun Patel", "Meera Nair"]
Column 2: RegisterNumber -> [101, 102, 103]
Column 3: Subject -> ["Math", "Science", "English"]
Column 4: Marks -> [95, 88, 92]

(Actual Parquet files are binary and not viewable as text.)

5️⃣ XML (Extensible Markup Language)

XML represents data using custom tags, similar to HTML. It’s structured and self-descriptive but more verbose than JSON.

Advantages:

Self-descriptive tags
Good for hierarchical data

Disadvantages:

Verbose syntax
Slower to parse compared to JSON

Example (XML):

<Students>
  <Student>
    <Name>Riya Sharma</Name>
    <RegisterNumber>101</RegisterNumber>
    <Subject>Math</Subject>
    <Marks>95</Marks>
  </Student>
  <Student>
    <Name>Arjun Patel</Name>
    <RegisterNumber>102</RegisterNumber>
    <Subject>Science</Subject>
    <Marks>88</Marks>
  </Student>
  <Student>
    <Name>Meera Nair</Name>
    <RegisterNumber>103</RegisterNumber>
    <Subject>English</Subject>
    <Marks>92</Marks>
  </Student>
</Students>

6️⃣ Avro (Row-based Storage Format)

Avro, developed by Apache, is a row-based binary format often used for data serialization in big data pipelines. It requires a schema definition and stores data efficiently for transmission between systems.

Advantages:

Compact binary format
Schema-based (ensures consistency)
Ideal for streaming and big data

Disadvantages:

Not human-readable
Requires schema evolution handling

Example (Conceptual Representation):

Schema:

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

Data:

[
  {"Name": "Riya Sharma", "RegisterNumber": 101, "Subject": "Math", "Marks": 95},
  {"Name": "Arjun Patel", "RegisterNumber": 102, "Subject": "Science", "Marks": 88},
  {"Name": "Meera Nair", "RegisterNumber": 103, "Subject": "English", "Marks": 92}
]

(Actual Avro data is stored in binary format, not plain text.)

🧠 Summary

Format	Structure	Readability	Common Use Case
CSV	Row-based	✅ Human-readable	Spreadsheets, simple data
SQL	Table	✅ Human-readable	Relational databases
JSON	Key-value	✅ Human-readable	Web APIs, configurations
Parquet	Columnar	❌ Binary	Big data analytics
XML	Tag-based	✅ Human-readable	Legacy web services
Avro	Row-based (binary)	❌ Binary	Data streaming, serialization

🚀 Final Thoughts

Each data format has its own strengths — CSV is great for simplicity, JSON for flexibility, SQL for structure, and Parquet or Avro for performance in big data environments.

Choosing the right one depends on your data size, structure, and use case.

💡 In modern analytics, you’ll often see multiple formats working together — for example, CSV input files transformed into Parquet for efficient querying in Spark.

✍️ Author: Rethan Kumar

UI/UX Designer | Tech Enthusiast | Exploring Data and Design

MongoDB CRUD in the Shell — InsertMany, Update, Regex Count, Delete, and Top N Sort

Rethan Kumar cv — Tue, 26 Aug 2025 16:57:48 +0000

Recently, I had the chance to work hands-on with MongoDB using a sample games dataset. This activity gave me a deeper understanding of how NoSQL databases operate, from storing and managing data to performing meaningful analysis.

Setting Up the Environment

I began by installing and running MongoDB Compass, which served as a user-friendly interface for database interaction. I created a new database called gameDB, where all the records would be stored and managed.

Importing the Dataset

The dataset was provided in JSON format and included details such as:

Game ID

Title

Genre

Rating

Review

Once imported, the data appeared neatly structured inside a collection, making it easy to explore.

Hands-On with CRUD Operations

🔹 Inserting Records
To get comfortable, I manually added 10 new game entries. This exercise helped me understand how MongoDB handles data insertion.

🔹 Querying the Data

Top 5 Games by Rating – I filtered and sorted the dataset to find the highest-rated games.

Keyword Search in Reviews – By searching for the term “good” in reviews, I explored how MongoDB manages text queries.

Fetching Reviews by Game ID – I retrieved reviews for a particular game, showing the power of targeted queries.

🔹 Updating & Deleting
I experimented with updating a review to reflect new feedback and deleting an outdated record. This highlighted MongoDB’s flexibility in handling dynamic data.

Exporting Results

After performing the queries, I exported results into JSON and CSV formats. This step was useful for sharing insights and conducting further analysis outside the database.

Key Learnings

Working with MongoDB on this dataset gave me valuable insights:

How CRUD operations (Create, Read, Update, Delete) work in a NoSQL environment

Ways to query, filter, and analyze unstructured data effectively

The ease of exporting results for external usage

Conclusion

This project showed me how MongoDB can be an excellent choice for handling semi-structured datasets like game reviews. It combines flexibility, scalability, and powerful querying features, making it well-suited for modern data-driven applications.

DEV Community: Rethan Kumar cv

📊 Understanding 6 Common Data Formats in Data Analytics

🧩 Our Sample Dataset

1️⃣ CSV (Comma Separated Values)

2️⃣ SQL (Relational Table Format)

3️⃣ JSON (JavaScript Object Notation)

4️⃣ Parquet (Columnar Storage Format)

5️⃣ XML (Extensible Markup Language)

6️⃣ Avro (Row-based Storage Format)

🧠 Summary

🚀 Final Thoughts

✍️ Author: Rethan Kumar

MongoDB CRUD in the Shell — InsertMany, Update, Regex Count, Delete, and Top N Sort