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⚙️Data Processing Services

Raw Data In. Clean, Structured, Analysis-Ready Data Out.

We take your raw, messy, inconsistent data and transform it into a clean, standardised, analysis-ready dataset. Whether it arrives from scrapers, manual exports, legacy systems, or third-party APIs — our processing pipelines handle it all.

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

In plain english

In plain English: Raw data is messy — duplicates, wrong formats, missing fields, inconsistent spellings. We clean it all up and restructure it so it's ready to use in your CRM, BI dashboard, or analysis pipeline. Think of us as your data janitorial service.

🎯Who Is This For

Built For These Teams & Businesses

Click your role to see what we build for you.

🏢 Businesses with CRM Data Issues

Deduplicate thousands of messy contact records before a CRM migration or marketing campaign

✨What's Included

Everything You Need — Nothing You Don't

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

Data cleaning: remove

Data cleaning: remove duplicates, fix encoding issues, handle nulls, and standardise values

Format conversion: CSV

Format conversion: CSV to JSON to SQL to XML to Parquet — any format, any direction

Field normalisation: consistent

Field normalisation: consistent date formats, phone number formatting, address standardisation

Record deduplication using

Record deduplication using exact match, fuzzy matching, and probabilistic record linkage

Data enrichment: append

Data enrichment: append missing fields from secondary sources (geocoding, company data, etc.)

ETL pipeline development

ETL pipeline development: automated Extract-Transform-Load workflows for ongoing data flows

Data validation against

Data validation against custom business rules and schemas with detailed error reporting

PII detection and

PII detection and masking for privacy-compliant data processing and GDPR compliance

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📁Real-World Use Cases

How Clients Actually Use Our Data Processing Services

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

🏢CRM / Sales

THE CHALLENGE

Deduplicate and enrich 120,000 contact records before a Salesforce migration

A Mumbai enterprise was migrating to Salesforce with 120K contacts in a legacy CRM — 30% estimated duplicates, inconsistent phone formats, missing company data. We cleaned, deduped, and enriched the dataset. Migration went live without a single duplicate or format error.

🛒E-commerce

THE CHALLENGE

Normalize 80,000 product catalogue entries from 6 suppliers

A B2B marketplace received product feeds from 6 suppliers — each with different column names, date formats, and category taxonomies. We built an ETL pipeline that normalises all 6 feeds into a unified product catalogue delivered daily to their PostgreSQL database.

🏠Real Estate

THE CHALLENGE

Merge and deduplicate property listings from 4 portals — zero duplicates

A PropTech platform was showing duplicate listings because the same property appeared on MagicBricks, 99acres, and Housing.com with different addresses. Our fuzzy-matching deduplication pipeline reduced their listing database by 23% while improving data completeness.

🤖AI / NLP

THE CHALLENGE

Clean and deduplicate 200M row text corpus for LLM training

An AI lab had a raw 200M-row text dataset with near-duplicates, encoding errors, and low-quality fragments. We ran MinHash LSH deduplication, quality filtering, and Unicode normalization — delivering a 147M-row clean corpus with documented quality metrics.

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🔄Our Process

How We Deliver — Step by Step

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

01
01

Data Audit & Problem Assessment

⏱ Audit report: within 24 hours
Your side

Send us a sample of your raw data (100–1,000 records). No need to prepare or clean it first — the messier the better, so we can see exactly what we are dealing with.

Our side

We run an automated audit to identify quality issues: missing fields, inconsistent formats, duplicate records, encoding errors, and outliers. We send you a detailed audit report with specific findings and a proposed processing plan.

02
02

Processing Pipeline Design

⏱ Pipeline design: 1–2 days
Your side

Review and approve the processing plan. Clarify any business rules we should apply (e.g., "prefer the most recent record when deduplicating").

Our side

We design a custom ETL pipeline with every transformation step clearly documented in plain English. No black boxes — you see exactly what happens to your data and why.

03
03

Sample Processing & Validation

⏱ Sample: within 48 hours
Your side

Review the sample output and validate it against your expectations. Confirm the column names, formats, and deduplication decisions are correct.

Our side

We process a sample batch first, compute output quality metrics (field completeness, duplicate rate, value distributions), and address any issues. Full processing begins only after you approve the sample.

04
04

Full Processing & Delivery

⏱ Delivery as scoped
Your side

Receive your clean dataset and the processing report. Use it directly in your BI tools, CRM, or AI pipeline.

Our side

We run the full pipeline, apply final validation, and deliver with a processing report: records in, records out, records rejected (with rejection reasons), transformations applied, and before/after quality metrics.

📦Deliverables

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
✅Clean, deduplicated, validated dataset in your preferred format
📋Processing report: records processed, rejected, and transformation log
📊Before/after quality metrics: field completeness, duplicate rate, format error rate
📄Rejection log: every removed record with the reason for rejection
🔄Automated ETL pipeline for ongoing processing — available as monthly contract
🔒PII detection report and masking documentation for compliance requirements
⚖️Why Not DIY?

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
🔧Tech Stack

Tools & Technologies We Use

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

🐍
Python + Pandas
Core data wrangling: filtering, reshaping, and transformation
⚡
Apache Spark
Distributed processing for datasets too large for a single machine
🔧
dbt (data build tool)
SQL-based transformation with testing and documentation
🔍
OpenRefine
Faceted browsing and clustering for manual data cleaning
🔗
Dedupe Python Library
Machine learning-based record linkage and deduplication
✅
Great Expectations
Automated data quality testing and validation framework
🔄
Apache Airflow
Pipeline orchestration, scheduling, and monitoring
🔤
Regex & NLP Tools
Pattern-based field extraction and text normalisation
💰 Starts from $20

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

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❓FAQ

Common Questions About Our Data Processing Services

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

What kinds of raw data can you process and clean?+
We process virtually any structured or semi-structured data: CSV, Excel, JSON, XML, SQL database dumps, Parquet, HTML tables, PDF tables (via extraction tools), and API responses. If you have a format not listed — send a sample and we will confirm within 24 hours.
How do you deduplicate records without losing important data?+
We use a multi-stage deduplication approach: first exact matching on unique fields (ID, URL, email), then fuzzy matching on name/address fields using algorithms like Levenshtein distance and Jaro-Winkler. For ambiguous pairs, we apply business rules you define (e.g., "prefer the record with the most complete fields") rather than blindly deleting. You review a sample of deduplication decisions before we apply to the full dataset.
Can you handle data in multiple languages or character encodings?+
Yes. We have processed data in English, Hindi, Arabic, Chinese, German, Spanish, French, and other languages. We normalise character encodings (UTF-8, Latin-1, Windows-1252) and handle diacritics, special characters, and right-to-left text correctly — no mojibake or encoding corruption.
What is an ETL pipeline and do I need one?+
ETL stands for Extract, Transform, Load — it is a process that takes raw data from a source, cleans and restructures it (transforms), and loads it into a destination (database, BI tool, CRM). You need one if you have ongoing data coming in regularly that always needs the same cleaning and transformation. We build custom ETL pipelines that run automatically on a schedule — so your data warehouse always has clean, up-to-date data without anyone touching it manually.
How do you handle personally identifiable information (PII) in data processing?+
We follow a privacy-by-design approach. We detect PII fields (names, emails, phone numbers, addresses, national IDs) using automated classifiers, then apply masking, pseudonymisation, or removal per your requirements. We operate in compliance with GDPR and India's DPDP Act 2023, and provide documentation of what PII handling was applied.
Can you enrich my dataset with additional data from external sources?+
Yes. Common enrichment services: geocoding addresses to lat/long coordinates, appending company LinkedIn URL or industry from company name, validating phone numbers, filling missing postcodes from city/state, and standardising product categories. Enrichment is typically an add-on to a cleaning project.
How do you charge for data processing — by record, by hour, or by project?+
We quote by project scope, not by record count (which can be gamed). After reviewing your data sample and understanding the processing requirements, we give a fixed-price quote. Ongoing monthly ETL pipelines are priced on a retainer basis. First 1,000 records processed free as a sample — no payment until you approve.
What happens if the processing has errors or the output quality is not what I expected?+
We have a clear revision policy: unlimited free revisions on the sample before full processing. For full deliveries, if quality does not match the approved sample, we re-process free of charge. We stand behind every delivery with a documented quality guarantee — the quality metrics are right there in the processing report.
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