How to scrape food and grocery data from ALDI using the Minexa.ai extension
- Minexa.ai
- 13 hours ago
- 4 min read
ALDI Australia runs a rotating Special Buys catalogue that changes weekly, and the Pantry Staples theme alone can list dozens of branded grocery products in a single session. If you want that product data in a spreadsheet without copying it row by row, this guide shows you exactly how to do it using the Minexa.ai Chrome extension.
Watch the full walkthrough first, then follow the steps below.
What page are we scraping?
The starting URL is the ALDI Special Buys page filtered to the Pantry Staples theme. It lists branded grocery products with prices, weights, availability labels, and product images all rendered in a structured product grid. There is no public API for this data, and the page uses JavaScript to load its content, which rules out simple copy-paste approaches.
Minexa.ai handles JavaScript-rendered pages automatically, so no extra configuration is needed before you begin.
How do I tell Minexa.ai I am on the right page?
After installing the extension and navigating to the ALDI Special Buys URL, open the Minexa.ai popup. You will see a button confirming you are on the correct page. Clicking it signals to the extension that this is the page you want to extract data from.
This is the only manual confirmation step before Minexa.ai takes over detection.
Does the ALDI page require pagination?
Minexa.ai automatically checks for pagination after you confirm the page. For the ALDI Special Buys Pantry Staples page, the extension identifies whether a next page button, infinite scroll, or load more mechanism is present and shows you what it found before continuing.
You do not need to configure anything here. Review what was detected and click Continue to move forward.
Should I scrape just the list or also the detail pages?
After pagination is confirmed, Minexa.ai asks whether you want to extract data only from the product grid (the list), or also follow each product link and extract additional detail from the individual product pages. For most grocery price tracking and catalogue monitoring use cases, the list data alone is sufficient since it already includes price, weight, brand, and product description.
If you need nutritional information or extended product copy that only appears on the individual product pages, select the list-and-detail option instead.
Simple or advanced scraping mode?
Once you have chosen list-only, Minexa.ai presents two modes: simple and advanced. Simple mode covers the standard extraction flow and works well for the ALDI product grid. Advanced mode is available if you need custom click sequences or more complex interaction workflows before data is captured.
Select simple mode and Minexa.ai will automatically highlight the data container on the page.
How does Minexa.ai identify what to extract?
After selecting simple mode, Minexa.ai automatically highlights the full product grid container on the ALDI page. You do not click on individual fields. The extension recognises the repeating product tile pattern and identifies all data points within it on its own.
Confirm the selection and the scraper is created. All extracted columns become visible immediately, with navigation to browse through them.
What data does the scraper actually extract?
Here is a sample of what the extracted JSON looks like for three products from the ALDI Pantry Staples page. Fields are shown with their clean names, with internal prefixes removed for readability.
[
{
"product_brand": "SIRENA",
"product_name": "Tuna in Oil 285g",
"product_price": "$6.49",
"price_per_unit": "($22.77 per 1 kg)",
"product_weight": "0.29 kg",
"availability_message": "While Stocks Last",
"product_href": "/product/sirena-tuna-in-oil-285g-000000000000370696",
"product_id": "product-tile-000000000000370696"
},
{
"product_brand": "BUNDABERG",
"product_name": "Ginger Beer 10 x 375ml",
"product_price": "$13.79",
"price_per_unit": "($3.68 per 1 L)",
"product_weight": "3.75 L",
"availability_message": "While Stocks Last",
"product_href": "/product/bundaberg-ginger-beer-10-x-375ml-000000000000524641",
"product_id": "product-tile-000000000000524641"
},
{
"product_brand": "CAPILANO",
"product_name": "Australian Honey 1kg",
"product_price": "$13.99",
"price_per_unit": "($1,399.00 per 100 kg)",
"product_weight": "1 kg",
"availability_message": "While Stocks Last",
"product_href": "/product/capilano-australian-honey-1kg-000000000000755274",
"product_id": "product-tile-000000000000755274"
}
]The product_href field encodes a relative path per product that embeds both the brand slug and a stable numeric product identifier. This means you can construct a direct deep-link to any individual product page without visiting it first. The price_per_unit field returns a normalised unit cost string per product, which allows cross-product price comparison without any additional computation. The product_id field surfaces a stable DOM-level identifier per tile, making it reliable for deduplication across repeated scraping runs on the same page. The image_srcset field, not shown above, returns multiple resolution-specific image URLs in a single string so downstream pipelines can select the correct asset depending on display context.
Can I schedule this to run automatically?
Yes. After the scraper is created, Minexa.ai shows a job summary screen where you can connect Google Sheets or set up a recurring schedule. Since ALDI Special Buys change weekly, scheduling the scraper to run each week means you always have the latest catalogue data without triggering anything manually.
How do I run the job and get my data?
From the scraping jobs list, your newly created ALDI scraper appears at the top with a Run button. Click it to start extraction. The data populates in a table as the job runs, and once complete you can export to Excel or JSON directly from the results view.
The scraper you trained on this ALDI page structure can be reused on any future Special Buys URL with the same layout without repeating setup. The structure is remembered, so subsequent runs start almost immediately.
For a related guide on tracking ecommerce and price data at scale, see Competitor price tracking: why most setups break and what actually holds up.
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