How to Compare Excel vs CSV Performance in Power Automate

When building Power Automate flows that process hundreds or thousands of records, the data source can have a significant impact on performance.

A common question is:

Which is faster in Power Automate — Excel or CSV?

In this article, we'll create a practical Power Automate benchmark using the same dataset in both Excel and CSV, measure the processing time, and examine the factors that influence the overall performance of the flow.

The goal is not simply to determine which format is faster, but to understand when to use Excel, when to use CSV, and how to design the flow for better performance

The short answer is:

CSV is generally more lightweight and can be faster for bulk data processing, while Excel is better suited for structured business data and human interaction.

However, the file format is only one part of the performance equation. Connector calls, filtering, pagination, loops, concurrency, and destination systems can have a much greater impact on the overall flow execution time.

In this article, we'll build a practical Power Automate comparison between Excel and CSV and discuss when you should use each format.

1. Excel vs CSV

Before looking at performance, it is important to understand the difference between the two formats.

Excel

Excel is a structured workbook that can contain:

Tables
Worksheets
Formulas
Formatting
Multiple columns and data types
Business calculations

In Power Automate, Excel data is commonly accessed using the Excel Online (Business) connector.

A typical flow looks like:

Excel File
    ↓
Excel Table
    ↓
List rows present in a table
    ↓
Filter
    ↓
Apply to each
    ↓
Destination

CSV

CSV stands for Comma-Separated Values.

It is essentially a text-based representation of tabular data.

For example:

ID,EmployeeName,Department,Amount
1,Employee 001,IT,1000
2,Employee 002,Finance,1500
3,Employee 003,HR,1200

A typical Power Automate flow looks like:

CSV File
    ↓
Get file content
    ↓
Convert to text
    ↓
Parse rows
    ↓
Filter
    ↓
Transform
    ↓
Destination

Because CSV does not contain workbook structure, formulas, formatting, or worksheets, it can be simpler to process as raw data.

2. Why CSV Can Be Faster

When Power Automate reads an Excel workbook, it is interacting with a structured workbook through the Excel connector.

There can be additional overhead related to:

Workbook processing
Excel tables
Connector communication
Pagination
Multiple Excel operations
Concurrent access
Large datasets

CSV is much simpler.

The flow can retrieve the file content and process it as text.

For simple tabular data, this can reduce the amount of processing required before the actual transformation begins.

However, this does not mean CSV will always result in a faster flow.

3. Create the Test Files

To perform a fair comparison, use the same dataset in both formats.

For example, create:

EmployeeData.xlsx
EmployeeData.csv

Use 5,000 records in both files.

Example data:

ID	Employee Name	Department	Amount
1	Employee 001	IT	1,000
2	Employee 002	Finance	1,500
3	Employee 003	HR	1,200
...	...	...	...
5000	Employee 5000	IT	1,800

For Excel, convert the data into an Excel Table.

For example:

Table Name: EmployeeTable

Store both files in a SharePoint document library.

4. Build the Excel Test

Create an Instant cloud flow with a manual trigger.

The first step is to capture the start time.

Excel Start Time

Use a Set variable action:

utcNow()

Then add:

Excel Online (Business) → List rows present in a table

Configure:

Location: SharePoint Site
Document Library: Documents
File: EmployeeData.xlsx
Table: EmployeeTable

If you are testing a large dataset, configure pagination appropriately.

5. Count the Excel Records

After retrieving the rows, use a Compose action:

length(
    body('List_rows_present_in_a_table')?['value']
)

For our example, the result should be:

Now capture the Excel end time:

utcNow()

6. Calculate Excel Processing Time

Use ticks() to calculate the elapsed time.

div(
    sub(
        ticks(variables('ExcelEnd')),
        ticks(variables('ExcelStart'))
    ),
    10000
)

The result is the elapsed time in milliseconds.

For example:

Excel Processing Time: 5,200 ms

The actual result will vary depending on your environment.

7. Build the CSV Test

Now perform the same test using the CSV file.

First capture the start time:

utcNow()

Then use:

SharePoint → Get file content using path

For example:

/Documents/EmployeeData.csv

8. Convert CSV Content to Text

The file content can be converted to text using:

base64ToString(
    body('Get_file_content_using_path')?['$content']
)

Now the flow has the complete CSV content as text.

9. Split the CSV into Rows

For a simple CSV file, you can split the content into rows.

split(
    outputs('CSV_Text'),
    decodeUriComponent('%0A')
)

This creates an array containing each row.

The first item will normally be the header:

ID,EmployeeName,Department,Amount

Remove the header using:

skip(
    outputs('CSV_Rows'),
    1
)

Now you have only the data rows.

10. Count CSV Records

Use:

length(
    outputs('CSV_Data_Rows')
)

The expected result is:

Capture the end time:

utcNow()

Then calculate the duration using the same ticks() calculation used for Excel.

11. Compare the Results

Now the flow can compare both results.

For example:

Records	Excel	CSV
1,000	2.1 sec	1.3 sec
5,000	5.2 sec	3.1 sec
10,000	9.8 sec	5.9 sec
25,000	25.4 sec	14.8 sec

These numbers are examples only.

You should run the benchmark in your own Power Automate environment because actual performance depends on your connectors, files, data volume, network conditions, and flow design.

12. Don't Benchmark Only Once

A single test isn't enough to make a reliable performance conclusion.

Run each test multiple times.

For example:

1,000 records  → 3 runs
5,000 records  → 3 runs
10,000 records → 3 runs
25,000 records → 3 runs

Then calculate the average.

For example:

Average Excel Time
=
Run 1 + Run 2 + Run 3
---------------------
          3

Do the same for CSV.

This reduces the impact of temporary connector or service latency.

13. The Biggest Performance Factor Isn't Always the File

This is the most important point.

Suppose CSV takes only 3 seconds to read 5,000 records.

But your flow does this:

CSV
 ↓
Parse 5,000 rows
 ↓
Apply to each
 ↓
SharePoint Create item
 ↓
5,000 calls

The CSV may be fast, but the overall flow can still be slow.

The destination system and number of connector calls can dominate the execution time.

This is why:

Faster file parsing does not automatically mean a faster Power Automate flow.

14. Filter Data as Early as Possible

Avoid processing records that you don't need.

Less efficient

Read 10,000 records
        ↓
Apply to each
        ↓
Check Status
        ↓
Process only Pending records

Better

Read data
    ↓
Filter Pending records
    ↓
Process required records

If only 500 out of 10,000 records are required, reducing the workload early can significantly improve the flow.

15. Reduce Apply to Each Operations

Loops can become expensive when processing thousands of records.

For example:

Apply to each
    ↓
Get item
    ↓
Update item

If this runs 5,000 times, the flow is making a large number of connector operations.

Where possible:

Filter before the loop
Select only required columns
Avoid unnecessary lookups
Use batch operations where supported
Use controlled concurrency for independent operations

16. Pagination Matters

When processing large Excel datasets, pagination is important.

If your Excel table contains thousands of records, review the pagination settings on:

List rows present in a table

But remember:

Enabling pagination doesn't automatically make the flow faster.

Pagination allows more records to be retrieved, but retrieving more records also means more data must be processed.

The better approach is to retrieve only what you actually need.

17. Concurrency Can Help

If records can be processed independently, controlled concurrency can improve performance.

For example:

Apply to each
       ↓
Concurrency
       ↓
Multiple records processed in parallel

But increasing concurrency without considering the destination can cause:

API throttling
Connector limits
Failed requests
Retry operations
Destination contention

Therefore, concurrency should be tested rather than simply set to the maximum value.

18. Excel vs CSV - When Should You Use Each?

Choose Excel when:

Business users maintain the data
You need formulas
You need formatting
You need Excel tables
Users need to review the workbook
The process depends on workbook functionality

Choose CSV when:

Data is exchanged between systems
You need a lightweight format
You are processing simple tabular data
You are performing bulk imports/exports
Humans don't need to maintain the file
You want to avoid workbook-specific processing

19. Practical Architecture

For a high-volume integration, focus on the complete architecture.

Source
  ↓
Retrieve Required Data
  ↓
Filter Early
  ↓
Select Required Columns
  ↓
Transform
  ↓
Batch / Optimize
  ↓
Destination
  ↓
Log Performance

For example:

CSV
 ↓
Get File Content
 ↓
Parse
 ↓
Filter
 ↓
Transform
 ↓
Batch Processing
 ↓
SharePoint / SQL / API

The same principle can be applied when Excel is the source.

20. Excel vs CSV Decision Matrix

Requirement	Better Choice
Business users maintain data	Excel
Formulas required	Excel
Formatting required	Excel
Structured workbook	Excel
System-to-system integration	CSV
Lightweight data exchange	CSV
Bulk text processing	CSV
Simple import/export	CSV
Human-readable report	Excel
Complex spreadsheet functionality	Excel

21. Important CSV Limitation

Be careful with simple CSV parsing.

This row:

123,"Ketan, Sathavara",IT,1000

cannot safely be processed using:

split(item(), ',')

because the name contains a comma.

Real-world CSV files can contain:

Quoted values
Commas inside values
Line breaks
Double quotes
Different delimiters
Encoding differences

For production solutions, use a proper CSV parsing strategy instead of assuming every comma represents a column boundary.

22. Final Takeaway

So, which reads data faster in Power Automate — Excel or CSV?

For simple bulk data processing, CSV will often have lower processing overhead than Excel.

But that should not be the only factor when designing a Power Automate solution.

The biggest performance improvements usually come from:

Filter early → Reduce connector calls → Process only required records → Control concurrency → Batch operations → Avoid unnecessary loops

Think of it this way:

Excel is great for people.

CSV is great for data exchange.

When performance matters, don't choose the format based only on the file extension.

Choose the format and architecture based on:

Data volume
Business requirements
Processing complexity
Connector behavior
Destination system
Number of operations
Scalability requirements

The best Power Automate solution isn't necessarily the one that reads the file fastest.

It's the one that minimizes the total work the flow has to perform.