We extract location details, regional pricing tiers, meat selections, and operating hours from Texas de Brazil. 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 Locations & Hours objects from texasdebrazil.com. All fields typed and schema-versioned.
"location_id": "TX-DAL-01", "store_name": "Dallas", "city": "Dallas", "state": "TX", "zip_code": "75201", "latitude": 32.7915, "longitude": -96.8033, "phone_number": "214-720-1414", "status": "OPEN"
| # | location_id | store_name | address_line_1 | address_line_2 | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dining Pricing objects from texasdebrazil.com. All fields typed and schema-versioned.
"location_id": "TX-DAL-01", "currency": "USD", "regular_dinner_price": 53.99, "salad_area_dinner_price": 31.99, "regular_lunch_price": 34.99, "salad_area_lunch_price": 24.99, "children_pricing_tier_1": "Free under 2", "price_effective_date": "2023-11-01"
| # | location_id | currency | regular_dinner_price | salad_area_dinner_price | regular_lunch_price | salad_area_lunch_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Meat & Salad Menus objects from texasdebrazil.com. All fields typed and schema-versioned.
"item_id": "M-001", "location_id": "TX-DAL-01", "category": "Meats", "item_name": "Picanha", "portuguese_name": "Picanha", "description": "Signature sirloin cut, lightly seasoned with rock salt", "availability_lunch": true, "availability_dinner": true
| # | item_id | location_id | category | item_name | portuguese_name | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Private Dining objects from texasdebrazil.com. All fields typed and schema-versioned.
"location_id": "TX-DAL-01", "has_private_dining": true, "max_capacity": 120, "room_options": "['Wine Cellar Room', 'Main Floor Buyout']", "av_equipment_available": true, "minimum_spend_required": 1500.0, "contact_email": "dallas.events@texasdebrazil.com"
| # | location_id | has_private_dining | max_capacity | room_options | av_equipment_available | minimum_spend_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Catering & Takeout objects from texasdebrazil.com. All fields typed and schema-versioned.
"location_id": "TX-DAL-01", "package_name": "Churrasco Package 1", "package_price": 165.0, "serves_count": 5, "included_meats": "['Picanha', 'Garlic Picanha', 'Chicken Breast wrapped in Bacon']", "delivery_available": true, "delivery_radius_miles": 15.0
| # | location_id | package_name | package_price | serves_count | included_meats | included_sides |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Texas de Brazil operates heavily regionalised pricing and menus. Our scraper maps every location, captures distinct pricing tiers, and tracks operating hours via automated pipelines.
Extract addresses, coordinates, phone numbers, and manager details for all domestic and international locations.
Capture the exact dinner, lunch, and salad-area-only prices per location, including children's pricing tiers.
Scrape the full list of meats, salad area items, desserts, and signature cocktails available at each restaurant.
Monitor changes to lunch, dinner, and happy hour schedules, including holiday closures and special event hours.
Extract capacity limits, room names, AV availability, and contact details for corporate events and buyouts.
Track takeout and catering package pricing, included items, serving sizes, and delivery radii per location.
Monitor public-facing VIP Dine Club promotions, seasonal discounts, and gift card bonus structures.
Extract data from UAE, Mexico, and other international franchise locations with localised currency normalisation.
Run pipelines weekly or monthly to capture menu rotations, price hikes, and new location openings.
Brief in. Clean data out.
Specify target locations, required data points (pricing, menus, hours), and extraction frequency.
We configure Scrapy crawlers to handle store locators, dynamic pricing loads, and regional variations.
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.
Restaurant chain websites rely heavily on JavaScript for store locators and regional pricing. Here is how we ensure reliable extraction.
Texas de Brazil loads specific pricing and hours based on the selected location via JavaScript. We run full Playwright sessions to select each location programmatically and wait for the DOM to hydrate before extracting the regional rates.
To ensure 100% location coverage, we intercept the underlying API calls powering the store locator map, extracting precise coordinates and internal location IDs rather than relying solely on HTML scraping.
Menu layouts change frequently during seasonal updates. We employ fallback chains using XPath, CSS, and structural pattern matching to maintain data integrity when formatting shifts.
We route requests through US residential proxies to avoid rate-limiting from standard web application firewalls and ensure we receive accurate, unblocked responses.
International locations display prices in local currencies. We capture the raw string, isolate the numeric value, and assign the correct ISO currency code for clean ingestion into your warehouse.
National restaurant groups track Texas de Brazil's regional pricing tiers to optimise their own fine-dining and buffet price points.
Retail developers analyse location coordinates and expansion patterns to identify viable markets for premium dining tenants.
Dining directories and reservation aggregators ingest hours, menus, and location metadata to keep their platforms accurate.
Foodservice suppliers monitor meat rotation and seasonal salad bar additions to forecast ingredient demand.
Corporate event concierges build databases of private dining capacities, AV specs, and minimum spend requirements.
Analysts map location density against demographic data to evaluate franchise saturation and market penetration.
"Restaurant pricing is highly regionalised. You cannot benchmark a national chain by looking at a single location's menu."
Extracting accurate data from chains like Texas de Brazil requires iterating through every specific location to capture local pricing variations, distinct operating hours, and specific menu availability. DataFlirt automates this iteration so you receive a unified, accurate dataset without managing the crawling logic.
Everything supported by our texasdebrazil.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 manages the crawl orchestration and queuing, while Playwright handles the dynamic location-selection logic required to load regional pricing.
Requests are routed through US residential IPs to prevent rate-limiting and ensure geographic accuracy when accessing the store locator APIs.
Pipelines execute on AWS Lambda and ECS, orchestrated by Apache Airflow for reliable scheduling and automated delivery.
Data delivered to where your team already works — no new tooling required.
About texasdebrazil.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline iterates through the entire location directory, simulating a user selecting each restaurant to capture the specific lunch, dinner, and salad-area pricing applicable to that market.
We configure scheduled runs (typically weekly or monthly) that compare current extraction results against previous states. Our diffing engine highlights new menu items, removed meats, and adjusted pricing.
No. Texas de Brazil typically handles reservations via third-party widgets (like SevenRooms or OpenTable). Scraping live availability requires interacting with those specific platforms, which falls outside standard menu and location extraction.
Yes. The scraper captures data for all locations listed on the primary website, including international franchises. Currencies and formatting are normalised during the extraction process.
We typically deliver a relational structure: a master locations file linked to secondary files for pricing, menus, and private dining specs via a unique location_id. We can also provide nested JSON if preferred.
For restaurant chain data, clients usually opt for monthly or quarterly refreshes. However, we can configure weekly pipelines if you are monitoring rapid pricing experiments.
Yes. We can run a sample extraction covering 3-5 locations to demonstrate schema structure, field completeness, and price variation before you commit to a full pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of Texas de Brazil locations or continuous monitoring of regional pricing tiers across the industry — we scope, build, and operate the pipeline. Tell us what you need.
