Choosing the Right Data Format for your Dataset

In the past 6 months, I have come across many different ways of storing data and I have found that selecting the proper data storage format can streamline workflows and significantly reduce processing times. I thought I would write this article to help consolidate my knowledge and pass on what I have learned. Have you ever seen Excel files saved as a .xlsx or .csv and wondered why there is a difference... then this article is for you.

Below is a quick guide to four popular formats for storing data I have come across —.xlsx, .csv, .json, and .parquet— with examples of datasets they excel with, those they may struggle with, and practical insights for use.

.xlsx – Excel Workbooks

When to Use: For everyday business reports and interactive data analysis.

Good For:

• Financial Statements & Small-Scale Reports: Excel shines with datasets that are relatively small, allowing users to apply formulas, create charts, and generate pivot tables easily. Its intuitive interface supports ad-hoc analysis and interactive reporting.

Bad For:

• Massive Transactional Logs: Large datasets quickly become unwieldy in Excel. As the volume grows, performance issues arise and collaboration becomes challenging due to file size limits and version compatibility issues.

.csv – Comma-Separated Values

When to Use: For transferring data between systems where simplicity and speed are key.

Key Difference from .xlsx: While Excel (.xlsx) supports rich formatting and interactive features, CSV files are plain text. This makes CSVs more lightweight and universally compatible, though they lack the advanced functionalities of Excel.

Good For:

• Exporting Data for Analytics: CSV files work well with datasets such as sales records or customer lists where the data is flat (non-hierarchical). They are easy to parse, load quickly, and integrate with many analytical tools.

Bad For:

• Complex, Hierarchical Data: Datasets that require multiple levels of nesting (like multi-tier product catalogues) lose structure in a CSV, leading to a need for additional documentation or complex parsing logic.

.json – JavaScript Object Notation

When to Use: For web applications and dynamic data exchanges.

Good For:

• Configuration Files & API Responses: JSON is ideal when the data is structured and may contain nested objects, such as user profiles with multiple addresses or transaction details with various attributes. Its self-describing nature facilitates data sharing across web services.

Bad For:

• Large, Flat Datasets: For massive datasets like extensive transaction records, JSON’s verbosity can lead to increased file sizes and slower processing speeds, which might hinder performance in data-intensive applications.

.parquet – Columnar Storage

When to Use: For big data analytics and large-scale data processing tasks.

Good For:

• Enterprise-Level Data Warehousing: Parquet is optimal for datasets such as large-scale user logs or sensor data where queries often involve reading specific columns. Its columnar design allows for efficient compression and faster query performance, reducing computational costs.

Bad For:

• Small, Ad Hoc Reports: When dealing with smaller datasets or one-off reports, the complexity and tooling required to manage Parquet files can be overkill compared to simpler formats like CSV or Excel.

By aligning your data storage strategy with the specific characteristics of your datasets, you can ensure optimal performance and smoother workflows.