How to Append Multiple Years of Sales Data in Power BI Power Query

By Ravindra Bagale · Last updated: 16 October 2026

Practice files: blinkit-orders-2023-small.csv and blinkit-orders-2024-small.csv (the rows on this page), and blinkit-orders-2023.csv and blinkit-orders-2024.csv (120 and 140 rows, for the practice task).

Made-up data: all orders, products and stores are fictional practice data imitating a Blinkit-style export from Maharashtra cities. Amounts are in ₹ and dates are DD-MM-YYYY.

1. The problem

Last year's report reads one file, blinkit-orders-2023.csv. The 2024 file has the same kind of orders, but it was exported by a different system:

If you just stack the two files, Power BI does not see revenue (₹) as revenue. It adds it as a separate column, the 2024 revenue sits there with nulls under revenue, and your Total Revenue card ignores 2024 completely.

2. The idea in simple words

Append puts the rows of one table under the rows of another, like stapling this year's register pages behind last year's. It matches columns by name:

• Same name → the values go into the same column.
• A name only in one table → a new column, with null for the rows of the other table.

So before you append, make the new file look exactly like the old one: same column names, same data types, same cleaning rules (blank IDs removed, NA in metrics turned into null).

3. The data

2023 file (already clean):

2024 file (raw; the delivery date and the two empty columns are not shown):

Predict before you build: how many rows will sales have, and what is the total revenue? (Section 5.)

4. Step by step

Load the 2023 file

1. Home › Get data › Text/CSV › blinkit-orders-2023-small.csv › Transform Data.
2. In the Query Settings pane, rename the query to orders_2023.
3. Set the types: order_date and delivery_date › Change Type › Using Locale… › Date, English (India); sales and stock › Whole Number; price and revenue › Decimal Number.

Load and clean the 2024 file

4. Home › New Source › Text/CSV › blinkit-orders-2024-small.csv › OK. Rename the query to orders_2024.
5. Open the filter arrow on order_id. If the list says List may be incomplete, click Load more. Untick (blank) and any NA, OK. Do the same check on product_id.
6. Ctrl+click promo_bin and delivery_date_format1, right-click › Remove Columns.
7. Double-click the header revenue (₹) and type revenue. Rename delivery_date_format2 to delivery_date. The spelling must match the 2023 query exactly: same letters, same underscores, no extra spaces.
8. Right-click price › Replace Values. Value To Find: NA. Replace With: type null. OK. The cell shows null in italics.
9. Set the same types as in step 3.

Append as a new query

10. Home › Append Queries › Append Queries as New.
11. Choose Two tables. First table: orders_2023. Second table: orders_2024. OK.
12. A new query called Append1 appears. Rename it to sales.

Load only the result

13. Right-click orders_2023 in the Queries pane and untick Enable load. Do the same for orders_2024. Their names turn italic: they still feed sales but are not loaded as separate tables.
14. Home › Close & Apply.
15. In Table view select sales and read the row count. In Report view, put revenue and sales on two cards.

5. Your result should look like this

sales has 7 rows, and only sales appears in the Data pane:

6. What happened

• The blank order ID row was removed from 2024 before the append, so only real orders were stacked.
• Because every 2024 column now has the same name as a 2023 column, Append put each value into the matching column. Nothing new was added on the side.
• sales is built from the two source queries. When you later change orders_2024, sales changes with it on the next refresh.
• With Enable load off, the 2023 and 2024 rows are loaded once (inside sales), not three times.

7. Explore further

• Next year's file. When the 2025 file comes, load it as orders_2025, clean it the same way, then select sales › Home › Append Queries (not "as New") › orders_2025. Every visual on the dashboard updates after Close & Apply. The next chapter does exactly this.
• Column order does not matter, names do. If 2024 has revenue before price, Append still matches them by name.
• Data types should match too. If revenue is Decimal in one query and Text in the other, the appended column becomes a mix and sums fail. Set types in both source queries.
• Append Queries vs Append Queries as New. Plain Append adds a step to the selected query; as New makes a separate query. As New keeps each year's cleaning in its own query and the result in one clearly named query.
• Three or more tables at once: choose Three or more tables in the Append dialog and add them to the list.
• Power BI Desktop runs only on Windows. On a Mac, use a Windows PC or a Windows virtual machine for this work.

8. Common mistakes

9. Practise on your own

10. Think like a professional

• Business view. One table per subject (sales), not one per year. Reports then compare years with a simple year filter.
• Performance. Turn off Enable load on helper queries. Each loaded table uses memory and refresh time.
• Maintenance. Name queries clearly: orders_2023, orders_2024, sales. Next year someone else can follow the chain in View › Query Dependencies.
• Many yearly files? Put them in one folder and use Get data › Folder, so a new file is picked up on refresh.

Interview questions

1. What is the difference between Append and Merge? Append adds rows from tables with the same columns; Merge adds columns from another table using a common key.
2. Why does an append sometimes create new columns full of nulls? A column name differs between the tables, so Append treats them as different columns.
3. Why turn off Enable load on the source queries? So the same rows are not loaded twice and the model stays small.
4. How do you add next year's data? Load and clean the new file to match, then append it into the existing sales query.

Recap

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