We extract store-level grocery pricing, BOGO deals, stock availability, digital coupons, and nutritional data from Publix. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Details objects from publix.com. All fields typed and schema-versioned.
"sku": "482910", "upc": "0002840004380", "title": "Lay's Classic Potato Chips", "brand": "Lay's", "category": "Pantry", "sub_category": "Snacks & Chips", "weight": "8 oz", "allergens": "['None']"
| # | sku | upc | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Store Pricing objects from publix.com. All fields typed and schema-versioned.
"store_id": "1294", "sku": "482910", "regular_price": 4.79, "sale_price": 2.39, "bogo_eligible": true, "price_per_unit": 0.59, "unit_measure": "per oz", "price_timestamp": "2026-05-12T09:14:00Z"
| # | store_id | sku | regular_price | sale_price | bogo_eligible | price_per_unit |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Promotions & Coupons objects from publix.com. All fields typed and schema-versioned.
"coupon_id": "CP-94821", "title": "Save $1.00 on Lay's", "discount_value": 1.0, "min_purchase": 2, "valid_from": "2026-05-10", "valid_until": "2026-05-24", "digital_only": true, "club_publix_exclusive": true
| # | coupon_id | title | description | discount_value | min_purchase | valid_from |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Inventory & Locations objects from publix.com. All fields typed and schema-versioned.
"store_id": "1294", "store_name": "Publix Super Market at Brickell Village", "sku": "482910", "in_stock": true, "aisle_number": "7", "shelf_location": "Middle", "delivery_eligible": true, "pickup_eligible": true
| # | store_id | store_name | sku | in_stock | stock_level | aisle_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Store Metadata objects from publix.com. All fields typed and schema-versioned.
"store_id": "1294", "store_name": "Publix Super Market at Brickell Village", "city": "Miami", "state": "FL", "zip_code": "33130", "has_deli": true, "has_bakery": true, "latitude": 25.7645
| # | store_id | store_name | address | city | state | zip_code |
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Our Publix scraper handles every layer of the platform: hyper-local store pricing, weekly BOGO cycles, nutritional profiles, and digital coupons — with JavaScript rendering and session management built in.
Capture hyper-local pricing across 1,300+ Publix locations. We inject ZIP codes and store IDs to extract exact shelf prices.
Track weekly Buy-One-Get-One-Free cycles, temporary price reductions, and percentage discounts across all grocery categories.
Extract macro profiles, full ingredient lists, dietary flags, and allergen warnings directly from product detail pages.
Monitor Club Publix digital coupons, clip-to-card offers, and manufacturer rebates visible on the platform.
Capture exact store routing data, aisle numbers, and shelf placements to understand product visibility and merchandising.
Track out-of-stock indicators and stock availability statuses per SKU at the individual store level.
Cross-reference Publix internal SKUs with global UPC/GTIN codes for precise matching against competitor catalogues.
Scrape custom order options, platter configurations, and sub sandwich pricing from the Publix Deli interface.
Identify markups applied to Instacart-powered delivery orders versus standard in-store pickup pricing.
Brief in. Clean data out.
Provide ZIP codes, store IDs, or category lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle ZIP code session injection, and manage proxy rotation.
Schema validation, null-rate checks, price-outlier detection, and sample payloads before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Grocery platforms invest heavily in scraping detection and rely on complex session states for local pricing. Here is how we stay resilient.
Publix utilizes strict rate limits and bot detection on its frontend. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass blocks.
Store selection, price hydration, and digital coupons are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution to capture data that standard HTTP clients miss.
Pricing and BOGO eligibility vary drastically by store. We maintain isolated session states and inject specific store IDs to ensure the pricing data accurately reflects the local shelf price.
Grocery layouts shift during seasonal changes. Our selector strategy uses multiple fallback chains per field so a frontend update does not break your data pipeline overnight.
For large grocery catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Grocery chains benchmark against Publix BOGOs and base pricing to adjust their own regional promotional strategies.
FMCG brands verify shelf placement, promotional compliance, and out-of-stock rates across specific store clusters.
Economic analysts track basket cost changes across Florida and the Southeast to measure regional food inflation.
Agencies monitor digital coupon saturation and discount depths to optimize trade spend for their brand clients.
Retail strategists identify regional SKU variations and stock gaps to inform category management decisions.
Analysts compare in-store pricing against delivery markups to measure the true cost of convenience platforms.
"Grocery pricing is hyper-local. A Publix in Miami prices differently than one in Atlanta, and tracking those deltas requires executing thousands of concurrent store sessions."
Extracting supermarket data at scale means fighting heavy bot protection, managing complex ZIP-code session states, and rendering heavy JavaScript frontends. DataFlirt handles the proxy rotation and session isolation so your analysts can focus on price elasticity and promotional cycles — not the extraction infrastructure.
Everything supported by our publix.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering, store ID injection, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of US-based residential ISP proxies. Rotation happens per-request with sticky sessions required for maintaining local store contexts.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About publix.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Publix is generally permissible under applicable law. DataFlirt targets only public, non-authenticated grocery, pricing, and promotional data. We do not extract personal data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We manage isolated browser sessions and inject specific ZIP codes or store IDs during the crawl. This ensures the pricing and availability data accurately reflects the physical shelf state at that specific location.
Yes. We can extract and differentiate between standard in-store pricing and the marked-up pricing displayed for delivery orders fulfilled via the Instacart integration.
Publix weekly ads and BOGO cycles typically reset on Wednesdays or Thursdays depending on the region. We schedule pipeline runs to align with these reset windows to capture the latest promotions immediately.
Yes. We extract full macro nutritional profiles, ingredient lists, dietary flags, and allergen warnings directly from the product detail pages.
Our smallest packages start at a defined SKU list or category set across a specific cluster of store locations. For larger national catalogues, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 SKUs across a handful of store locations as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off category dump or a continuous price-monitoring feed across 1,300+ stores — we scope, build, and operate the pipeline. Tell us what you need.
