We extract credit card offers, Qantas and Velocity redemption tables, lounge reviews, and point valuation metrics from Point Hacks. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Credit Card Offers objects from pointhacks.com.au. All fields typed and schema-versioned.
"card_name": "Qantas Premier Platinum", "issuer": "NAB", "bonus_points": 80000, "reward_program": "Qantas Frequent Flyer", "spend_requirement": 3000.0, "spend_days": 90, "annual_fee": 349.0
| # | card_name | issuer | bonus_points | reward_program | spend_requirement | spend_days |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Flight Reviews objects from pointhacks.com.au. All fields typed and schema-versioned.
"airline": "Singapore Airlines", "aircraft_type": "A380", "cabin_class": "Suites", "origin": "SYD", "destination": "SIN", "points_cost": 155000, "taxes_fees": 124.5
| # | airline | flight_number | aircraft_type | cabin_class | origin | destination |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lounge Reviews objects from pointhacks.com.au. All fields typed and schema-versioned.
"lounge_name": "Qantas First Lounge", "airport_code": "SYD", "terminal": "International", "access_methods": "['Oneworld Emerald', 'First Class Ticket']", "food_rating": 4.8, "overall_rating": 4.9
| # | lounge_name | airport_code | terminal | access_methods | amenities | food_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Redemption Guides objects from pointhacks.com.au. All fields typed and schema-versioned.
"loyalty_program": "Velocity Frequent Flyer", "region_from": "Australia", "region_to": "Europe", "business_points": 139000, "partner_airlines": "['Qatar Airways', 'Singapore Airlines']", "routing_rules": "Maximum 2 transits permitted"
| # | loyalty_program | region_from | region_to | economy_points | business_points | first_points |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Articles & News objects from pointhacks.com.au. All fields typed and schema-versioned.
"article_id": "ph-84921", "title": "Ultimate Guide to Qantas Points", "author": "Daniel Sciberras", "publish_date": "2023-10-14T08:00:00Z", "category": "Guides", "comment_count": 42
| # | article_id | title | author | publish_date | modified_date | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Point Hacks publishes dense, unstructured guides and dynamic credit card offers. We normalise this content into queryable datasets for competitive intelligence and travel aggregators.
Extract bonus point values, minimum spend criteria, timeframes, and annual fees from comparison tables.
Parse aircraft types, route details, points paid, and cash taxes from narrative review articles.
Convert HTML zone-based award charts into structured origin-destination point pricing matrices.
Extract eligibility criteria, operating hours, and amenity ratings from specific airport lounge guides.
Resolve redirect chains on credit card application buttons to identify exact banking campaign parameters.
Track stated end dates for elevated sign-up bonuses to maintain accurate historical offer timelines.
Scrape paginated user comments to gauge sentiment on frequent flyer program devaluations or card changes.
Capture article categories, tags, and author metadata to map content strategy and topic velocity.
Monitor guide update timestamps to detect when point valuations or award chart prices are modified.
Brief in. Clean data out.
Select target categories: credit card offers, flight reviews, or redemption guides. We design the schema.
We configure crawlers to parse WordPress DOM structures, handle pagination, and extract table data.
Schema validation, null-rate checks on point values, and data typing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on an agreed daily or weekly cadence.
Extracting data from editorial sites requires handling inconsistent formatting and complex table structures. Here is how we maintain data quality.
Redemption guides use varying table structures for award charts. We use custom heuristics to map row headers (destinations) and column headers (cabins) into a flat, relational schema.
Point values are often written as '120k' or '120,000 pts'. Our parsers strip text and normalise these into integer types for direct database ingestion.
We traverse all historical pages within categories to build a complete catalogue of older reviews and expired credit card offers, ensuring no historical data is missed.
Point Hacks frequently updates existing articles. We hash the article content and metadata, only emitting new records when significant changes to point values or fees occur.
We extract the underlying destination URLs from affiliate tracking links to provide clean mapping to the actual banking or airline product pages.
Financial institutions monitor competitor credit card sign-up bonuses and minimum spend requirements to adjust their own acquisition strategies.
Flight search engines integrate point valuations and redemption costs to show users alternative payment methods for specific routes.
Airlines track how their award availability and pricing compare to competitors in independent editorial reviews.
Publishers analyze article topic velocity, comment engagement, and update frequency to optimise their own travel content production.
Agencies track which banking products are receiving premium placement and promotional focus across major travel sites.
Corporate travel platforms use historical points-to-cash valuations to optimise reward program utilisation for enterprise clients.
"Point Hacks holds the most comprehensive historical record of Australian credit card bonuses and airline award charts. We turn that editorial content into structured intelligence."
Parsing unstructured blog content into strict relational tables requires custom heuristics and continuous maintenance. DataFlirt handles the complex DOM extraction, table normalisation, and data typing so your analysts receive clean, query-ready datasets without writing a single line of parsing logic.
Everything supported by our pointhacks.com.au 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 fast, concurrent crawling of static WordPress content, managing request queues and deduplication.
Bespoke Python extraction logic targets specific WordPress shortcodes and table structures to guarantee accurate data typing.
Airflow schedules regular sweeps of category pages to detect new articles and updated guides automatically.
Data delivered to where your team already works — no new tooling required.
About pointhacks.com.au scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available editorial content and comparison tables is generally permissible. DataFlirt extracts only public, non-authenticated article data and does not access private forum messages or user accounts. Clients should ensure their use of the data complies with copyright laws regarding republication.
Yes. We extract the stated promotion end dates from the article text and comparison tables. We also monitor pages for updates to detect when an offer is withdrawn unannounced.
Our parsers use resilient XPath and CSS selectors. We monitor extraction yields continuously. If a WordPress theme update alters table structures, our alerting system flags the anomaly and our engineers update the selectors.
We can extract publicly readable threads and posts from the community forum. However, we do not scrape private messages or restricted sections requiring user login.
We typically configure pipelines to crawl the homepage and category feeds daily to capture new articles and updated guides. Full historical archive crawls are usually run once during initial setup.
Yes. We clean textual representations (e.g., '100k', '100,000 pts') into standard integer values during the extraction pipeline, ensuring the data is immediately usable for quantitative analysis.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop manually checking blogs for new credit card offers or updated award charts. We build the pipeline to deliver structured travel data directly to your systems.
