1. What Is a Temporary Table?

A temporary table is a database table that:

  • Exists only during a session
  • Stores intermediate results
  • Is automatically removed when the session ends

Temporary tables are useful for short-term analytical tasks.

They function like “working memory” for SQL queries.


2. Why Use Temporary Tables?

Temporary tables help when:

  • Working with large datasets
  • Reusing filtered subsets repeatedly
  • Breaking complex queries into logical stages
  • Simplifying multi-step joins
  • Improving readability and efficiency

Instead of repeatedly filtering or joining, you store the result once and reuse it.


3. Example Use Cases

3.1 Complex Multi-Table Joins

Instead of:

  • Joining 7–8 tables in one query

You can:

  1. Join smaller tables first.
  2. Store result in a temporary table.
  3. Join that temp table to larger tables.

This reduces query complexity.


3.2 Repeated Subset Analysis

If analyzing:

  • Only trips ≥ 60 minutes

Instead of filtering every time:

WHERE trip_duration >= 60

You create a temp table once and reuse it.


4. Creating a Temporary Table Using WITH (CTE)

In BigQuery and many SQL systems, the WITH clause creates a Common Table Expression (CTE).

A CTE:

  • Acts like a temporary table
  • Exists only within the query
  • Improves readability

5. Example: Bike Trips Over 60 Minutes

Step 1: Define Temporary Table

WITH trips_over_1_hr AS (
    SELECT *
    FROM `bigquery-public-data.new_york.citibike_trips`
    WHERE tripduration >= 60
)

Explanation:

  • WITH → Start temporary definition.
  • trips_over_1_hr → Name of temp table.
  • AS → Assign query.
  • Subquery inside parentheses defines the subset.

This does not permanently create a table.
It exists only for the query session.


6. Using the Temporary Table

After defining it, you can reference it like a regular table.

Example: Count Long Trips

SELECT COUNT(*) AS cnt
FROM trips_over_1_hr;

This returns:

  • Total number of trips ≥ 60 minutes.

7. Commenting in SQL

To add descriptive comments:

-- Count trips longer than 60 minutes

Or in some systems:

## Count trips longer than 60 minutes

Comments improve collaboration and documentation.


8. Benefits of Using WITH (CTE)

8.1 Readability

Instead of deeply nested subqueries:

SELECT ...
FROM (
    SELECT ...
    FROM ...
)

You define logical blocks at the top.


8.2 Reusability

Within the same query:

WITH subset AS (...)
SELECT ...
FROM subset;

You can reference subset multiple times.


8.3 Efficiency

  • Avoid repeated filtering
  • Avoid rewriting complex conditions
  • Reduce duplication

9. Temporary Tables vs Permanent Tables

FeatureTemporary TablePermanent Table
Stored long-termNoYes
Visible to othersUsually NoYes
Exists across sessionsNoYes
Used for intermediate stepsYesNo (typically)

Temporary tables are ideal for exploratory analysis.


10. Multi-Step Example Structure

WITH filtered_data AS (
    SELECT *
    FROM source_table
    WHERE condition
),
aggregated_data AS (
    SELECT column, COUNT(*)
    FROM filtered_data
    GROUP BY column
)
SELECT *
FROM aggregated_data;

This layered approach:

  • Breaks analysis into logical phases.
  • Improves clarity.
  • Makes debugging easier.

11. When to Use Temporary Tables

Use temp tables when:

  • You need repeated access to a filtered dataset.
  • You are performing multiple calculations on the same subset.
  • You want to simplify nested logic.
  • You are testing intermediate results.
  • You are building a report pipeline.

12. Analytical Advantages

Temporary tables:

  • Improve organization
  • Reduce repetitive code
  • Simplify debugging
  • Enhance team collaboration
  • Support modular query design

They act as structured building blocks for complex analysis.


13. Summary

Temporary tables (via WITH clause):

  • Store intermediate query results.
  • Exist only during query execution.
  • Simplify complex SQL logic.
  • Allow repeated reuse of filtered subsets.
  • Improve readability and efficiency.

They are powerful tools for managing multi-step SQL analysis and are foundational for scalable database workflows.