7 Common Data File Types Every Data Analyst Should Know (and How to Open Them)

If you’re just starting in data analysis, you’ll quickly realize that not all data looks the same. Whether you’re downloading public datasets from sources like the World Bank or Statistics Canada, exporting reports, or querying databases, data can come in many different file types — each with its own structure, purpose, and tools for working with it.

Understanding these formats is one of the first practical skills every data analyst needs. In this guide, we’ll walk through seven of the most common data file types, what kind of information they hold, and how to open them.

1. CSV (.csv) — The Most Common Format You’ll See

What it is:
A CSV (Comma-Separated Values) file stores data in plain text, organized into rows and columns. Each line represents one record, and commas separate the values.

What’s inside:

• Tables of data (like spreadsheets)

• Column headers in the first row

• Raw values without formatting or formulas

How to open it:

• Excel or Google Sheets: Double-click or import it.

• Python (Pandas): Use the Pandas read_csv function.

  import pandas as pd
  df = pd.read_csv("data.csv")
  

Why it matters:
CSV files are simple, lightweight, and universal — making them a go-to choice for data sharing and analysis. Most of the datasets you find online will either be in this format or have an option to download the data in this format. If you are familiar with Excel spreadsheets (see below), you’ll notice that the key difference between a CSV file and an Excel file is that CSV files only contain a single sheet, whereas Excel files can contain multiple sheets.

2. Excel (.xlsx, .xls) — Familiar but Powerful

What it is:
Excel files are spreadsheet documents that can include multiple sheets, formulas, and formatting. Many organizations still rely on Excel for collecting and sharing data.

What’s inside:

• Tables of data across multiple sheets

• Formulas, charts, and filters

• Pivot tables or summary tabs

How to open it:

• Excel or Google Sheets: For quick viewing and editing.

• Python (Pandas):

  import pandas as pd
  df = pd.read_excel("data.xlsx", sheet_name="Sheet1")
  

Why it matters:
Excel is still one of the most widely used tools in data analytics — ideal for exploring, cleaning, and visualizing small datasets. You’ll find that many large organizations share data internally using Excel files. These are familar to most users and since most companies still are Microsoft Office-based it makes sense that these files are so prevalent. Excel also has a vast library of powerful functions and formulas that enable users to process large quantities of data and perform complex analysis quickly.

3. JSON (.json) — Data from the Web

What it is:
JSON (JavaScript Object Notation) is a structured format commonly used for web data, APIs, and configuration files. It’s great for representing complex or nested data.

What’s inside:

• Key-value pairs and lists

• Nested data structures

• Metadata-rich records

How to open it:

• Text editor or VS Code: For quick inspection.

• Online JSON viewer: To visualize nested data.

• Python:

  import json
  with open("data.json") as f:
      data = json.load(f)
  

Why it matters:
If you plan to pull data from APIs or web sources, you’ll work with JSON often. It’s structured, flexible, and widely supported.

4. Parquet (.parquet) — The Big Data Format

What it is:
Parquet is a high-performance file format used for big data and cloud analytics. It stores data in a columnar layout, making it much faster to read and compress.

What’s inside:

• Compressed tabular data

• Column metadata and schema information

How to open it:

• Python (Pandas):

  import pandas as pd
  df = pd.read_parquet("data.parquet")
  

• DuckDB or DBeaver: Great for quick inspection.

Why it matters:
As datasets grow, CSVs become inefficient. Parquet makes querying and analysis much faster — especially in tools like Spark, Snowflake, and BigQuery.

5. SQL (.sql) — Databases in Text Form

What it is:
An SQL file contains commands for creating or managing databases. You’ll often encounter them as exports or scripts for loading and querying data.

What’s inside:

• SQL commands like CREATE TABLE, INSERT INTO, or SELECT

• Database schema and structure

• Optional data insert statements

How to open it:

• Database tools (pgAdmin, DBeaver, MySQL Workbench): To run or import.

• Text editor: For reading or editing scripts.

Why it matters:
SQL is a core skill for analysts. Understanding how to read and use .sql files lets you pull data directly from databases — where most real-world data lives.

6. XML (.xml) — Older but Still Around

What it is:
XML (Extensible Markup Language) stores data in nested tags. Although it’s been largely replaced by JSON, XML is still common in industries that rely on structured data exchange, like finance and publishing.

What’s inside:

• Data enclosed in tags (similar to HTML)

• Hierarchical relationships

• Schema definitions

How to open it:

• Browser or text editor: For basic viewing.

• Python:

  import xml.etree.ElementTree as ET
  tree = ET.parse("data.xml")
  root = tree.getroot()
  

Why it matters:
You might still encounter XML files when pulling data from older systems or government sources — so it’s worth recognizing.

7. Feather (.feather) — Fast for DataFrames

What it is:
Feather is a binary file format designed for quick reading and writing of DataFrames, especially in Python and R. It’s optimized for speed, not storage or sharing.

What’s inside:

• Tabular data (like a CSV)

• Metadata for column names and types

How to open it:

• Python:

  import pandas as pd
  df = pd.read_feather("data.feather")
  

• R:

  library(arrow)
  df <- read_feather("data.feather")
  

Why it matters:
Feather files make it easy to save cleaned data or intermediate results between analysis steps without losing precision or data types.

Final Thoughts

Every data analyst needs to understand how data is stored, shared, and structured. From simple CSVs to high-performance formats like Parquet, each file type plays a role in your analytical workflow.

Once you’re familiar with these seven formats and how to open them, you’ll be well-equipped to handle data from nearly any source — and spend more time analyzing instead of troubleshooting.