We extract grocery listings, store-specific pricing, stock depth, and promotional data from H-E-B. Delivered as clean JSON, CSV, or Parquet to your infrastructure.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from heb.com. All fields typed and schema-versioned.
"sku": "1234567", "title": "H-E-B Meal Simple Brisket Queso", "brand": "H-E-B", "price": 8.48, "category": "Deli & Prepared Food", "uom": "16 oz", "heb_own_brand": true
| # | sku | upc | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from heb.com. All fields typed and schema-versioned.
"sku": "1234567", "store_id": "142", "regular_price": 8.48, "combo_loco_eligible": true, "digital_coupon_available": false, "curbside_markup": 0.25, "price_timestamp": "2023-10-24T14:32:00Z"
| # | sku | store_id | regular_price | sale_price | curbside_markup | combo_loco_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from heb.com. All fields typed and schema-versioned.
"store_id": "142", "store_name": "Austin 26", "city": "Austin", "has_curbside": true, "inventory_status": "IN_STOCK", "aisle_location": "Aisle 4"
| # | store_id | store_name | address | city | zip_code | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Nutritional Data objects from heb.com. All fields typed and schema-versioned.
"sku": "1234567", "calories": 240, "total_fat": "18g", "protein": "12g", "dietary_tags": "['Gluten Free', 'Keto Friendly']", "allergens": "['Milk', 'Soy']"
| # | sku | calories | total_fat | sodium | total_carbohydrate | protein |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Weekly Ads objects from heb.com. All fields typed and schema-versioned.
"ad_id": "WKA-492", "store_id": "142", "promotion_type": "Meal Deal", "offer_text": "Buy 1 H-E-B Brisket, Get 4 Items Free", "start_date": "2023-10-25", "end_date": "2023-10-31"
| # | ad_id | store_id | start_date | end_date | promotion_type | offer_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our H-E-B scraper handles the complexities of regional grocery data: store-specific sessions, GraphQL interception, Combo Loco parsing, and nutritional fact extraction.
Title, brand, UPC, pricing, images, and category taxonomy across all H-E-B departments.
Capture hyper-local pricing variations across 400+ locations, including curbside vs in-store differentials.
Track complex promotional mechanics, including Buy-One-Get-X and bundled meal deals specific to Texas regions.
Extract clip-to-card digital coupons, discount values, and expiration dates mapped to specific SKUs.
Parse structured nutrition facts panels, ingredient lists, and dietary flags like Gluten-Free or Keto-Friendly.
Monitor out-of-stock flags and inventory availability at the individual store level.
Isolate and track H-E-B private label products against national brand equivalents.
Digitise weekly circulars into structured promotional datasets mapped to active store IDs.
Extract exact in-store aisle and shelf locations for planogram analysis and instacart competitor tracking.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide target categories, store IDs, or UPC lists. We design the extraction schema together.
We configure Scrapy crawlers, intercept GraphQL endpoints, and handle zip-code session management.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Grocery extraction requires strict session management. Here is how we maintain stable pipelines across hundreds of regional store IDs.
H-E-B pricing and inventory are strictly tied to local store sessions. Our infrastructure maintains persistent cookie jars mapped to specific store IDs and Texas zip codes, ensuring we extract the exact local price rather than generic regional fallbacks.
Modern grocery frontends rely heavily on GraphQL and undocumented internal APIs. We bypass the heavy React DOM entirely where possible, intercepting raw JSON payloads for faster, more reliable data extraction with lower compute overhead.
Grocery sites aggressively rate-limit high-volume catalog scraping. We distribute requests across targeted US proxy pools, managing request concurrency and header rotation to avoid WAF blocks and 403 responses.
H-E-B's promotional logic is notoriously complex, involving multi-item dependencies. Our parsers flatten these into structured, queryable relational records, mapping every free item back to the parent purchase requirement.
Grocery categories shift constantly. We maintain rigid schema validation, alerting on structural changes to the department tree or missing nutritional panels, ensuring downstream ingestion never fails.
Regional competitors monitor H-E-B's localized pricing strategies, tracking base price changes and curbside markups across Texas.
FMCG brands audit digital shelf placement, share of search, and out-of-stock rates for their products against H-E-B private labels.
Retail analysts track the frequency, depth, and mechanics of Combo Locos and digital coupons to model promotional spend.
Health and wellness applications ingest structured ingredient and macronutrient data to power dietary recommendation engines.
Third-party delivery aggregators synchronise local store inventory and pricing to maintain accurate catalogues for their own users.
Economic researchers build hyper-local CPI models by tracking basket price variance across different Texas zip codes over time.
"H-E-B's hyper-local pricing and complex promotional mechanics make it a goldmine for regional retail intelligence - provided you can untangle the store-level session routing."
Extracting grocery data requires more than simple HTTP GET requests. You must manage persistent sessions tied to specific store IDs, parse undocumented GraphQL endpoints, and normalise complex promotional rules like Combo Locos. DataFlirt handles this session state and proxy routing, delivering clean, structured grocery data directly to your warehouse.
Everything supported by our heb.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.
We maintain persistent cookie jars mapped to specific H-E-B store IDs and zip codes, ensuring accurate local pricing and inventory extraction without session leakage.
Our crawlers bypass the heavy React frontend by targeting undocumented GraphQL endpoints directly, reducing latency and compute costs while extracting cleaner JSON structures.
Pipelines run on AWS Lambda and ECS. 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 heb.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure concurrent pipeline runs using store-specific session cookies and zip code routing to extract hyper-local pricing and inventory across hundreds of locations.
Our schema normalises these promotions into relational structures. We extract the primary qualifying item, the list of free or discounted items, and the exact promotional logic required to trigger the deal.
Yes. We parse the structured nutritional facts, ingredient text, and dietary flags (such as Gluten-Free or Vegan) for every SKU that provides this data.
Absolutely. We can extract the brand taxonomy and flag H-E-B Own Brand items, allowing you to run price gap analysis against equivalent CPG products.
We can schedule pipelines to run at daily or intra-day cadences depending on your requirements. Note that inventory flags are based on the digital storefront's state at the time of the crawl.
Our packages start at a defined category or store list with weekly delivery. For full-catalogue daily tracking across multiple locations, we price based on compute volume and proxy bandwidth.
No. DataFlirt only extracts publicly available data from unauthenticated sessions. We do not support scraping personal account data, past orders, or login-gated personalised coupons.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price tracking across 400 store locations - we scope, build, and operate the infrastructure. Tell us what you need.
