We extract B2B supplier directories, machinery specifications, market news, and commodity pricing trends from the Indian Textile Journal. 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 Company Directory objects from indiantextiljournal.com. All fields typed and schema-versioned.
"company_name": "Lakshmi Machine Works Ltd", "category": "Spinning Machinery", "city": "Coimbatore", "state": "Tamil Nadu", "pin_code": "641020", "contact_person": "Sanjay Jayavarthanavelu", "products_offered": "Carding machines, Draw frames, Ring spinning frames"
| # | company_name | category | address | city | state | pin_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Industry News objects from indiantextiljournal.com. All fields typed and schema-versioned.
"article_id": "ITJ-2023-11-402", "headline": "Cotton prices stabilise amid fresh crop arrivals", "publish_date": "2023-11-14", "category": "Market Trends", "tags": "['Cotton', 'Pricing', 'Agriculture']", "related_companies": "['Cotton Corporation of India']"
| # | article_id | headline | author | publish_date | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Machinery & Products objects from indiantextiljournal.com. All fields typed and schema-versioned.
"product_id": "PRD-8821", "product_name": "Airjet Loom ZA209i", "manufacturer": "Tsudakoma", "category": "Weaving", "specifications": "Speed: 1200 RPM, Width: 190cm", "applications": "Apparel fabrics, Industrial textiles"
| # | product_id | product_name | manufacturer | category | sub_category | specifications |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Market Pricing objects from indiantextiljournal.com. All fields typed and schema-versioned.
"commodity_name": "Shankar-6 Cotton", "material_type": "Raw Cotton", "price_inr": 61500.0, "unit": "Candy (356 kg)", "date_recorded": "2023-11-15", "market_location": "Rajkot"
| # | commodity_name | material_type | price_inr | unit | date_recorded | market_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Events & Exhibitions objects from indiantextiljournal.com. All fields typed and schema-versioned.
"event_name": "India ITME 2024", "start_date": "2024-12-08", "end_date": "2024-12-13", "city": "Greater Noida", "organizer": "India ITME Society", "exhibitor_count": 1800
| # | event_name | start_date | end_date | venue | city | country |
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Trade journals hold decades of valuable B2B data, but extracting it requires parsing unstructured text, standardising legacy HTML, and normalising corporate records into strict warehouse schemas.
Parse thousands of manufacturer and supplier profiles. We separate unstructured address blocks into clean street, city, state, and pin code fields.
Scrape the entire back catalogue of industry news, interviews, and market analyses, capturing full text, author metadata, and publication dates.
Extract product capabilities, technical specifications, and application domains for textile machinery, standardising metrics where possible.
Capture historical and current pricing for cotton, yarn, and synthetic fibres reported in market update sections.
Download and parse embedded PDF reports, corporate brochures, and technical data sheets linked within product showcases.
Compile details of upcoming trade shows, exhibitions, and conferences, including venue details, dates, and organiser contact information.
Legacy sites often feature inconsistent formatting. We apply regex and NLP logic to normalise company names, phone numbers, and job titles.
Run scheduled pipelines to capture newly published articles, directory additions, and price updates without re-scraping the entire archive.
Handle broken pagination, malformed DOM elements, and server timeouts common in legacy web infrastructure.
Brief in. Clean data out.
Select the target sections: supplier directories, news archives, or machinery showcases. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and custom parsing logic to handle indiantextiljournal.com's specific DOM structure.
Schema validation, null-rate checks, and address normalisation testing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Scraping B2B trade journals is rarely about bypassing sophisticated anti-bot systems. It is about handling malformed HTML, unstructured text, and inconsistent pagination.
Trade directories often dump company name, contact person, and address into a single paragraph tag. We use custom regex pipelines and NLP models to split these into strict, queryable warehouse fields.
Legacy sites frequently contain unclosed tags, nested tables, and inconsistent CSS classes. Our selector strategy relies on structural XPath fallbacks rather than fragile class names.
When pagination links are generated dynamically or break mid-archive, standard crawlers fail. We build custom URL generators and sitemap parsers to ensure complete catalogue extraction.
Critical machinery specifications are often locked in linked PDF brochures. We download, parse, and OCR these documents, appending the extracted text to the main product record.
Even legacy sites employ basic IP blocking. We use distributed proxy pools and strict concurrency limits to scrape the site reliably without triggering server-side bans or causing performance degradation.
Apparel manufacturers extract machinery and raw material supplier directories to identify new vendors and diversify supply chains.
Commodity traders track historical cotton and yarn pricing reports to model market trends and forecast procurement costs.
Software and logistics companies targeting the textile sector use extracted company profiles to build enriched outbound sales lists.
Equipment manufacturers monitor competitor product showcases and technical specifications to benchmark their own hardware.
Private equity firms aggregate industry news, corporate interviews, and expansion announcements to identify high-growth textile enterprises.
Trade show organisers track historical event logs to identify frequent exhibitors and target them for future sponsorships.
"The Indian Textile Journal holds decades of B2B supplier networks and market pricing trends, locked inside unstructured articles and legacy directories."
Extracting data from legacy trade publications requires handling inconsistent DOM structures, broken pagination, and embedded PDF reports. DataFlirt normalises this unstructured industry data into queryable warehouse records, allowing procurement teams to focus on sourcing rather than parsing HTML.
Everything supported by our indiantextiljournal.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.
Optimised for high-throughput HTML parsing. Scrapy handles crawl orchestration, deduplication, and retry logic, extracting data efficiently from static legacy pages.
We deploy specific parsing modules to handle unstructured addresses, malformed dates, and missing fields, ensuring the final output matches strict database schemas.
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 indiantextiljournal.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory listings, news articles, and product specifications is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data beyond business contact information, nor do we bypass paywalls without authorisation.
Legacy directories often group the entire address, phone number, and contact person into a single text block. We use custom regex pipelines and address parsing libraries to normalise these into strict fields (street, city, state, pin_code, phone).
Yes. Our pipeline can download linked PDF documents, extract the text using pdfplumber, and append the relevant technical specifications directly to the main product record.
For news and commodity pricing, we typically configure daily or weekly incremental runs. For company directories and machinery showcases, monthly refreshes are usually sufficient to capture new additions.
Legacy sites change rarely, but when they do, our monitoring stack detects schema drift and null-rate spikes immediately. We update the selectors and rerun the pipeline to ensure no data is lost.
Absolutely. We provide a sample run of up to 500 company records or news articles during the scoping phase, allowing you to validate field completeness and normalisation quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of the supplier directory or a continuous feed of market news and pricing - we scope, build, and operate the pipeline. Tell us what you need.
