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Large-Scale Data Gathering From Any Online Source Organised & Ready

We handle end-to-end data collection from multiple online sources simultaneously. Whether you need millions of records from a single domain or aggregated datasets from 50+ websites, our infrastructure and expertise deliver clean, deduped data at scale.

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48hr
Avg. Turnaround
500+
Projects Done
10+
Years Experience
Free
Sample First

In plain english

In plain English: Think of this as the foundation — we figure out the best way to gather all the raw data you need, from whichever sources it lives across the internet, and bring it into one clean, consistent place for you.

Built For These Teams & Businesses

Click your role to see what we build for you.

🤖 AI & Machine Learning Teams

Build large, diverse training datasets from curated web sources — clean and labelled

Everything You Need — Nothing You Don't

Every data collection services engagement includes our full quality guarantee: free sample, unlimited revision rounds, and proactive monitoring for ongoing projects.

Multi-source data collection

Multi-source data collection from 50+ websites simultaneously with a unified, consistent schema

Massive scale: collect

Massive scale: collect millions of records per day with our distributed crawler infrastructure

Intelligent deduplication using

Intelligent deduplication using fuzzy matching, URL normalisation, and record fingerprinting

Comprehensive QA: missing

Comprehensive QA: missing field detection, outlier flagging, and validation against expected patterns

Domain expertise across

Domain expertise across e-commerce, real estate, finance, healthcare, travel, and HR data

One-time collection projects

One-time collection projects or ongoing automated pipelines — whichever fits your use case

GDPR and privacy-law-aware

GDPR and privacy-law-aware collection: we only collect publicly available, non-personal data

Structured taxonomy: consistent

Structured taxonomy: consistent field naming, value standardisation, and category mapping across sources

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How Clients Actually Use Our Data Collection Services

Real projects — different industries, different goals, same quality of outcome.

🤖Artificial Intelligence

THE CHALLENGE

847 million Hindi tokens for LLM training — 21 days

A Bangalore AI startup needed a massive, diverse Hindi corpus for training a domain-adapted LLM. We collected from 120 portals including news sites, government releases, and forums — delivering 847M clean, deduplicated tokens in Hugging Face Datasets format.

🏦Financial Data

THE CHALLENGE

15,000 company profiles from 8 business registries worldwide

A B2B data vendor needed a comprehensive global company database. We collected from Dun & Bradstreet public listings, Companies House (UK), MCA21 (India), SEC EDGAR, and 4 more registries — delivering 15,000 structured profiles with 40 fields each.

🌍Academic Research

THE CHALLENGE

Social media dataset: 2M posts across 3 platforms for misinformation study

A university research team needed a large dataset of public social media posts about health misinformation. We collected 2M anonymized public posts across Reddit, Twitter (public API), and news comment sections — delivered with metadata for NLP analysis.

🛒Market Intelligence

THE CHALLENGE

500,000 product listings from 12 Indian e-commerce platforms — monthly

A market research firm needed a complete snapshot of Indian e-commerce product listings every month. We collect from 12 platforms including Amazon.in, Flipkart, Myntra, and Meesho — delivering a 500K-record unified catalogue monthly.

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How We Deliver — Step by Step

A transparent process with clear handoffs. You always know what is happening and what is next.

01
01

Define Data Scope & Schema

Scoping: 1–2 business days
Your side

Tell us: which websites/sources, which data fields, what volume, any geographic or date filters, and your target output schema.

Our side

We audit all sources for feasibility, advise on what is realistically collectible vs what requires special handling, and design the unified output schema that maps fields consistently across all sources.

02
02

Multi-Source Crawler Deployment

Deployment: 2–5 days
Your side

Nothing from you at this stage.

Our side

We deploy specialised crawlers for each data source on distributed infrastructure — optimising crawl depth, rate limiting, and proxy rotation per source. All crawlers run in parallel for maximum speed.

03
03

Aggregation, Deduplication & QA

QA: 1–3 days
Your side

Review the QA report and approve the data before final delivery.

Our side

Raw data streams from all sources are merged into a unified dataset. We run deduplication (exact and fuzzy match), field normalisation, completeness checks, and anomaly detection. Flagged records are manually reviewed.

04
04

Delivery & Documentation

Delivery as agreed
Your side

Receive clean, structured data with full documentation and start using it.

Our side

We deliver with complete documentation: field definitions, source attribution, collection timestamps, coverage notes, and a data quality report. Ongoing pipelines include weekly QA summaries.

What You Actually Receive

No vague promises. Here is the exact list of what lands in your inbox (or database) when we deliver your project.

Supported Output Formats
📄 CSV📊 Excel{ } JSON🗄️ SQL DB📋 Google Sheets🔌 REST API☁️ AWS S3 / Drive🔺 Parquet
🗄️Unified dataset in CSV/JSON/SQL/Parquet — all sources merged into one schema
📋Data quality report: field completeness, source coverage, duplicate rate
📄Field dictionary / data dictionary: definition of every column
🔗Source attribution: which record came from which source and when
🔄Recurring collection pipeline with weekly delivery — auto-scheduled
☁️Cloud storage delivery: AWS S3, Google Drive, or SFTP — your choice

Build It Yourself vs Hire DataScraper.in

Building and maintaining scraping infrastructure is harder than it looks. Here is an honest comparison.

FactorBuild It YourselfDataScraper.in ✓
Setup timeWeeks of development24–48 hours
Anti-bot bypassComplex — easily breaksIncluded, maintained
Maintenance when site changesYour dev team's problemWe fix it proactively
Starting cost$500+ in developer hoursFrom $20
Free sample before payingNoAlways
ScalabilityRebuild for each new sourceAdd sources on demand

Tools & Technologies We Use

We select the right tool for every job — not a one-size-fits-all approach.

🕷️
Scrapy + Redis
Distributed crawling with shared request queues at scale
📨
Apache Kafka
Real-time data streaming from multiple collection agents
🗄️
PostgreSQL / MongoDB
Scalable storage for structured and semi-structured data
🔍
FuzzyWuzzy / rapidfuzz
Intelligent record linkage and duplicate detection
☁️
AWS S3 / GCS
Cloud data lake storage for large-volume datasets
🐍
Python + Pandas
Data aggregation, transformation, and quality checking
🌐
Residential Proxies
Millions of clean IPs for uninterrupted large-scale collection
🔄
Apache Airflow
Workflow orchestration for complex multi-step pipelines
💰 Starts from $20

Free sample before payment · Quote within 2 hours · No long-term contracts required

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Common Questions About Our Data Collection Services

Have a question not covered here? We respond within 30 minutes on WhatsApp.

How many records can you collect in a single project?+
There is no practical upper limit for publicly available data. We have collected datasets ranging from 10,000 records for niche research projects to 50 million+ records for large enterprise clients. Infrastructure scales with demand — we provision additional crawler nodes as needed, and the price scales proportionally.
How do you ensure data quality when collecting from many different sources?+
Each source gets source-specific validation rules. We then apply cross-source deduplication and a global QA pass covering: required field completeness, format validation, numeric range checks, and statistical outlier detection. Every delivery includes a quality report with field-by-field completeness percentages.
What is the difference between Data Collection and Data Extraction?+
Data extraction typically refers to pulling data from one or a few specific websites. Data collection is a broader term covering multi-source aggregation, large-scale crawling, and building structured databases from many sources. In practice, collection projects are usually larger in scope and include more QA and schema design work.
Can I use the collected data to train AI models?+
Yes — this is one of our most common use cases. We build AI training datasets covering text, images, structured data, and labelled records. We follow responsible collection practices — only publicly available content, properly attributed, with deduplication and quality filtering appropriate for ML use.
How long does a large-scale data collection project take?+
A 100,000-record single-source project is typically done in 24–48 hours. A multi-source, 10M-record project may take 1–2 weeks. A 500M+ token NLP corpus typically takes 2–4 weeks. We provide a detailed timeline estimate before starting and share daily progress updates for large projects.
Can you collect data from websites protected by CAPTCHAs or aggressive rate limits?+
Yes. We integrate CAPTCHA-solving services and implement respectful rate throttling per source. For heavily protected sources, we discuss feasibility, expected yield rate, and additional cost before committing — so there are no surprises.
What format will the final dataset be in?+
We deliver in your preferred format: CSV, Excel, JSON, Parquet (for ML), SQL database dump, or direct database push (PostgreSQL, MySQL, MongoDB). For AI teams, we also support Hugging Face Datasets format and Apache Arrow. If your toolchain uses a specific format, we will match it.
How much does large-scale data collection cost?+
Pricing depends on: number of sources, total records, source complexity, QA depth, and delivery frequency. Small one-time collections start from $20. Enterprise-scale projects (millions of records, many sources, monthly refresh) are quoted based on scope. Free sample collection before full commitment.
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Scrapers Commonly Used For This Service

Ready-built for the platforms our clients request most.

🕷️ Google Maps Scraper 🕷️ LinkedIn Scraper 🕷️ Amazon Scraper 🕷️ Yelp Scraper View all 23 scrapers →

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