Why SQL Is Used Instead of Spreadsheets: Exploring and Filtering Large Datasets

1. Why SQL Is Used Instead of Spreadsheets

Spreadsheets are useful for sorting, filtering, and customizing data, but some datasets are:

• Too large to download
• Too complex to fit into a spreadsheet

In these cases, data analysts use SQL (Structured Query Language) to retrieve only the data they need directly from a database.

SQL allows analysts to:

• Narrow large datasets
• Focus on relevant records
• Avoid unnecessary data processing

2. Databases and Relational Structure

A database is a collection of data stored in a computer system.
Many databases used in analytics are relational databases, which means:

• Data is stored in multiple tables
• Tables are connected through relationships
• Relationships are defined using primary keys and foreign keys

SQL is the language analysts use to communicate with these databases.

3. Exploring Data with a Table Viewer

Before writing queries, analysts often explore datasets using a Table Viewer.

Why preview data?

• Understand what the dataset contains
• Check if the data looks clean and usable
• Confirm that the dataset fits the analysis goal

Some Table Viewers allow previewing a few rows without writing a query.

4. Example Dataset: Solar Potential Data

A sample dataset shows:

• How much sunlight hits rooftops annually
• Data organized by regions, states, and postal codes

This type of dataset is useful for analyses such as:

• Evaluating solar energy potential
• Comparing sunlight exposure across locations

5. Identifying the Dataset Name

To query a table, analysts must use the full dataset name, which includes:

• The database name
• The table name

These are often displayed inside backticks (`) for readability.

Example structure:

database_name.table_name

database_name.table_name

The backticks improve readability but are not always required.

6. Writing a Basic SQL Query

A basic SQL query retrieves data from a table.

Query to view all columns

SELECT *
FROM database_name.table_name;

SELECT *
FROM database_name.table_name;

Explanation:

• SELECT specifies what data to retrieve
• * means all columns
• FROM specifies the source table

Using line breaks and spacing improves readability, but SQL also works as a single line.

7. Query Readability

SQL queries can be written in many formats and still return the same results.

Best practice

• Use line breaks and indentation
• Write queries clearly for yourself and others

Readable queries are easier to debug, review, and maintain.

8. Filtering Data with WHERE

When only part of a dataset is needed, analysts add a WHERE clause.

Example: Filter by state

SELECT *
FROM database_name.table_name
WHERE state_name = 'Pennsylvania';

SELECT *
FROM database_name.table_name
WHERE state_name = 'Pennsylvania';

Explanation:

• WHERE limits the results to records that meet a condition
• Single quotes are used for text (string) values

This query returns only data related to Pennsylvania.

9. Why Filtering Matters

Filtering with SQL:

• Reduces data volume
• Speeds up analysis
• Focuses on relevant information

This approach is similar to filtering in spreadsheets but works directly on large databases.

10. Key Takeaways

• Large datasets are often analyzed using SQL instead of spreadsheets
• SQL is used to query relational databases
• Table Viewers help preview and understand datasets
• SELECT * retrieves all columns
• FROM identifies the source table
• WHERE filters data based on conditions
• Readable SQL queries are easier to manage
• SQL allows analysts to extract only the data they need

One-sentence summary

SQL enables data analysts to efficiently explore, filter, and retrieve specific data from large relational databases by writing clear and targeted queries.