We extract business listings, ABNs, operating hours, local reviews, and contact metadata from Dlook. 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 Business Profiles objects from dlook.com.au. All fields typed and schema-versioned.
"dlook_id": "dlk-892144", "business_name": "Sydney Plumbing Specialists", "abn": "12 345 678 901", "primary_category": "Plumbers", "claimed_status": true, "year_established": 2008, "profile_url": "https://www.dlook.com.au/plumbers/sydney-nsw/sydney-plumbing-specialists"
| # | dlook_id | business_name | abn | acn | primary_category | sub_categories |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Contact Details objects from dlook.com.au. All fields typed and schema-versioned.
"business_id": "dlk-892144", "phone_primary": "(02) 9876 5432", "phone_mobile": "0412 345 678", "website_url": "https://www.sydneyplumbingspecialists.com.au", "email_address": "info@sydneyplumbingspecialists.com.au", "facebook_url": "https://facebook.com/sydplumbing", "last_verified": "2023-10-14T08:22:00Z"
| # | business_id | phone_primary | phone_mobile | fax | email_address | website_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location Data objects from dlook.com.au. All fields typed and schema-versioned.
"business_id": "dlk-892144", "street_address": "124 George Street", "suburb": "Sydney", "state": "NSW", "postcode": "2000", "latitude": -33.8688, "longitude": 151.2093
| # | business_id | street_address | suburb | state | postcode | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from dlook.com.au. All fields typed and schema-versioned.
"review_id": "rev-44921", "business_id": "dlk-892144", "reviewer_name": "John D.", "rating": 5.0, "review_title": "Fast and reliable", "review_text": "Arrived within an hour of calling. Fixed the leak quickly.", "review_date": "2023-09-12"
| # | review_id | business_id | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Operating Hours objects from dlook.com.au. All fields typed and schema-versioned.
"business_id": "dlk-892144", "monday_open": "07:00", "monday_close": "17:00", "weekend_hours": "Closed", "public_holidays": "By appointment only", "thursday_open": "07:00", "thursday_close": "17:00"
| # | business_id | monday_open | monday_close | tuesday_open | tuesday_close | wednesday_open |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Dlook scraper navigates state and suburb taxonomies, bypasses rate limits, and structures raw directory HTML into clean, queryable datasets for B2B lead generation and market analysis.
Capture business name, ABN, ACN, descriptions, and operational metadata directly from listing pages.
Extract phone numbers, mobile contacts, email addresses, and external website URLs for direct outreach.
Normalised extraction of street, suburb, state, and postcode fields, alongside embedded latitude/longitude coordinates.
Scrape customer feedback, star ratings, and owner responses to gauge local business reputation.
Systematically crawl Dlook's category trees and state/suburb directories to ensure 100% coverage of target verticals.
Convert unstructured text-based opening hours into strict, machine-readable daily timeframes.
Identify and extract linked Facebook, Twitter, and LinkedIn profiles associated with the business.
Cross-reference extracted ABNs against standard regex patterns to ensure data hygiene before delivery.
Run incremental crawls to detect new businesses, closed locations, or updated contact details since the last run.
Brief in. Clean data out.
Provide target categories (e.g., Plumbers), states, or specific Dlook URLs. We design the extraction schema.
We configure Scrapy crawlers, proxy rotation for AU IPs, and pagination logic to traverse the directory.
Schema validation, null-rate checks on phone numbers, and location normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Business directories employ rate limiting and structural inconsistencies. Here is how our infrastructure handles Dlook.
Dlook paginates heavily across state and suburb intersections. Our crawlers map the entire taxonomy tree, ensuring no listings are orphaned or missed during deep pagination runs.
To prevent IP bans and rate limiting, we route requests through Australian residential proxies, mimicking local user traffic and adhering to safe concurrency limits.
Directory inputs are notoriously messy. We apply post-processing to standardise AU phone formats (e.g., stripping spaces) and separate addresses into strict street/suburb/state/postcode fields.
Claimed profiles feature different DOM structures than free listings. Our selector chains account for both templates, ensuring consistent data extraction regardless of the listing tier.
For ongoing monitoring, we hash business records. Subsequent crawls only deliver new listings or businesses that have updated their contact details or hours.
Sales teams extract target verticals by state and suburb to build highly segmented outreach lists for outbound campaigns.
Agencies monitor Dlook listings to ensure NAP (Name, Address, Phone) consistency across the web for their clients.
Analysts map business density by category and postcode to identify underserved regions or saturated markets in Australia.
Franchises monitor local competitors' reviews, ratings, and operating hours to benchmark performance.
CRM administrators enrich existing incomplete leads with ABNs, verified phone numbers, and social media links.
Logistics and mapping platforms ingest latitude/longitude coordinates to plot commercial nodes across Australian suburbs.
"Dlook contains a wealth of Australian SME data, but extracting it accurately requires navigating complex taxonomy trees and standardising messy user-generated inputs."
Scraping business directories seems trivial until you hit rate limits, inconsistent address formats, and deep pagination loops. DataFlirt manages the proxy rotation, DOM parsing, and data normalisation, delivering clean, structured AU business records directly to your warehouse.
Everything supported by our dlook.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 crawl orchestration and deep taxonomy traversal. Playwright manages JavaScript execution for dynamically loaded contact details or map widgets.
We utilise Australian residential proxies to mimic local traffic, avoiding geo-blocks and aggressive rate limiting common on regional directories.
Pipelines run on AWS ECS. Airflow manages scheduling and retry logic. Data normalisation occurs in-flight before delivery to your warehouse.
Data delivered to where your team already works — no new tooling required.
About dlook.com.au scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available business listings is generally permissible. However, users must comply with the Australian Privacy Principles (APP) and the Spam Act 2003 when utilising extracted email addresses or phone numbers for marketing. DataFlirt extracts only public data; we do not provide legal advice on your downstream usage.
Yes, where email addresses are publicly visible on the Dlook business profile or embedded in mailto links, our scraper will extract them.
Our schema accepts null values for fields that a business has not provided (e.g., missing fax numbers or operating hours). We implement null-rate monitoring to ensure structural changes haven't broken the selectors.
Yes. We can scope the pipeline to crawl only specific category URLs (e.g., Electricians) or restrict the extraction to specific states like NSW or VIC.
For directory data, we typically recommend weekly or monthly refresh cycles to capture new listings and contact updates without incurring unnecessary compute costs.
Yes. We parse the raw address strings into structured street, suburb, state, and postcode fields to ensure compatibility with standard CRM and GIS systems.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of a specific industry or a continuous feed of new Australian business listings — we scope, build, and operate the pipeline. Tell us what you need.
