"Understanding 6 Common Data Formats in Cloud-Based Data Analytics"

In the world of data analytics, data can come in many shapes and formats. Choosing the right one depends on how you plan to store, share, or process it.
In this blog, we’ll look at six popular data formats used in data analytics — with simple explanations and examples for each.

1. CSV (Comma Separated Values)
📘 What it is:

CSV is one of the simplest and most common formats. Each line represents a row, and values are separated by commas. It’s lightweight and easy to read but doesn’t store data types or structure.

🧩 Example (CSV)

*2. SQL (Relational Table Format)
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📘 What it is:

SQL (Structured Query Language) stores data in tables (rows and columns) inside relational databases like MySQL or PostgreSQL. It provides a powerful way to query and manage structured data.

🧩 Example (SQL)

3. JSON (JavaScript Object Notation)
📘 What it is:

JSON is a lightweight, text-based format widely used for APIs and data exchange. It represents data as key-value pairs and is easy for both humans and machines to read.

🧩 Example (JSON)

4. Parquet (Columnar Storage Format)
📘 What it is:

Parquet is a binary, columnar storage format optimized for big data processing (like Hadoop or Spark). It stores data column-wise, making analytical queries faster.

(Note: Parquet is binary, so here we’ll show how it’s represented conceptually.)

🧩 Example (Conceptual)

5. XML (Extensible Markup Language)
📘 What it is:

XML is a markup language that uses tags to define data. It’s structured and flexible but can be verbose.

🧩 Example (XML)

6. Avro (Row-based Storage Format)
📘 What it is:

Avro is a binary row-based format developed by Apache. It’s often used with Kafka and Hadoop for data serialization. It stores both data and schema, making it efficient for long-term storage.

(Conceptual example in JSON-like representation of Avro schema and data)

🧩 Example (Avro)