PROJECT

Web Scraping Software for Clean, Export-Ready Data

Custom software development, Web scraping, Data extraction, Data cleaning, Deduplication, Data validation, Database integration, Export automation

  • Industry: Business services
  • Luxembourg Location: Luxembourg
  • Delivery time: 16 weeks
  • Technologies: Python, PHP
4+ months

This client spends time collecting records from websites, databases, and APIs before the data is ready for analysis or import. The web scraping software takes in pages, endpoints, and files. It extracts selected fields, then cleans and validates them. It returns consistent data for spreadsheets, SQL databases, or applications. It gives a data processing company a repeatable way to prepare mixed-source records while reducing manual work and rework.

Summary

Problem

The client spends time collecting and preparing mixed-source records by hand.

Solution

The solution became a repeatable web scraping and data-cleaning workflow for extraction, validation, and export.

Result

The software is made to reduce manual preparation time and errors before data reaches its destination.

Client story

The client is a data processing company from Luxembourg. It consolidates records from websites, databases, and APIs for further use.

Manual collection and preparation take time because sources have different structures and formats. Inconsistent fields also create rework before export or import.

The request was simple: create repeatable Python, command-line, notebook, or application-based workflows. They had to extract, clean, and validate data before sending it to the required destination.

The solution was shaped around source setup, a cleaning and validation workspace, and structured export. Rules for fields, schemas, and destinations were kept visible so the client team could work with its own standards.

The client asked for

  1. Run repeatable Python, command-line, notebook, or application workflows.
  2. Export clean results as CSV, Excel, Parquet, JSON, Sheets, text, or SQL.
  3. Clean, normalize, transform, deduplicate, filter, sort, and consolidate data.
  4. Provide logs, error reports, documentation, setup instructions, code, and dependencies.

Why ready-made software did not fit

Ready-made products cover parts of the task: web collection, managed extraction, data integration, or configurable scraping rules. The draft describes separate capabilities for source extraction, cleaning, and validation. It also describes destination mapping and reporting. The client's full workflow therefore required its own combination.

Project overview

The solution was designed around three stages: source and extraction setup, cleaning and validation, and structured export. This keeps the path from a source record to its destination clear.

The first screen brings websites, databases, and APIs into one setup. It also accepts files and defines the fields and destination structure for each run.

To handle mixed source formats, Python workflows were provided for extraction and preparation. HTML parsing, database access, spreadsheet reading, and document conversion support the specified sources.

The cleaning workspace shows raw and cleaned values, duplicate groups, and validation results. Cleaning and deduplication rules prepare a consistent structure.

The export view maps columns and schemas to CSV, Excel, or database destinations. Processing summaries and run logs keep the state of each run visible.

Documentation, setup instructions, and source code were included. Dependency information and operational logs help the client team manage the workflows and tune them to its own data rules.

Key features delivered

  • Web scraping for selected pages and fields
  • Extraction from databases, APIs, files, and documents
  • Repeatable Python and application workflows
  • Cleaning, normalization, and transformation
  • Duplicate detection and record merging
  • Schema and field validation
  • Skipped-record and error reporting
  • CSV, Excel, Parquet, JSON, text, Sheets, and SQL export

Can be added

+ Pagination handling + Dynamic-page and JavaScript support + Rate-limit handling + CAPTCHA handling + Additional destination connectors + Processing documentation and setup support

Who else can use this solution

Businesses with a similar data preparation task can adapt the same solution to their sources, rules, and destinations.

A research and analytics team

Task It prepares mixed-source records from websites, APIs, files, and databases for analysis and reporting.
How the solution helps The solution extracts selected fields, validates them, removes duplicates, and exports a consistent dataset.
Professional services

A document processing business

Task It processes batches of PDFs, reports, invoices, forms, and scanned documents.
How the solution helps The solution extracts document data, applies cleaning and validation rules, and records skipped or failed items.
Business services

A data operations team

Task It imports extracted records into spreadsheets, SQL databases, or applications.
How the solution helps The solution maps cleaned columns to the required destination and keeps a processing summary for each run.
Business services

Technology stack

Python

Extraction and cleaning workflows

Python runs the repeatable workflows that extract, clean, normalize, validate, and consolidate records.

PHP

Spreadsheet import and export

PHP supports spreadsheet and CSV processing where the workflow uses PHP-based tools.

JavaScript / TypeScript (Node.js)

Web and spreadsheet processing

Node.js supports HTML parsing, spreadsheet handling, and database connections in application-based workflows.

SQLAlchemy

Database abstraction

SQLAlchemy (a library that connects applications to databases through a common interface) supports database access and destination handling.

PyMySQL

MySQL database access

PyMySQL (a Python driver for MySQL and MariaDB) connects the workflow to MySQL records.

firecrawl-anydoc

Document conversion

firecrawl-anydoc (a library that converts documents and spreadsheets to Markdown) supports document content extraction.

openpyxl

XLSX reading and writing

openpyxl (a Python library for Excel workbooks) reads and writes XLSX files.

cheerio

HTML parsing and scraping

cheerio (a library for parsing and manipulating HTML) extracts selected content from web pages.

CSV, XLSX, JSON, Parquet

Structured file outputs

These formats carry cleaned records to files, spreadsheets, applications, and analysis tools.

Integrations

PostgreSQL, MySQL, and SQLite

The workflow reads database records and writes cleaned data to SQL destinations.

Google Sheets

Cleaned records are mapped into Google Sheets structures.

Excel

The system reads spreadsheet sources and exports mapped records as Excel files.

Websites and APIs

The scraper receives pages and API responses, then extracts selected fields.

The result

Sources and fields stay defined in one place

The source setup screen gives the data processing company a clear starting point for web scraping and data extraction. It holds source details, requested fields, and HTML selectors together.

Repeatable runs use the same extraction rules for recurring work, while the source type covers websites, databases, and APIs.

How it works

  1. The source setup reads URLs, endpoints, database details, file paths, selected fields, and target HTML elements.
  2. The extraction workflow parses source content and prepares the requested fields.
  3. The system applies the selected destination format or database target to the run.
  • Website fields can be selected with HTML element selectors.
  • Database, API, and file sources use their relevant connection or path details.
Tip: A first working version can be reviewed on typical client files before the remaining rules are tuned.
Source and extraction setup

Source and extraction setup

Functions on this screen
Scrape website data Fetch text data Extract data from HTML

Cleaning rules make records consistent

The cleaning workspace shows raw and cleaned values side by side with validation status. Duplicate groups and skipped-record reasons make exceptions easier to review before export.

The result is a consistent structure prepared for analysis, import, or querying rather than a collection of unprocessed source records.

How it works

  1. The cleaning workflow normalizes, transforms, filters, sorts, and consolidates field values.
  2. The deduplication module identifies duplicate groups and prepares merged records.
  3. The validation module checks records against schemas, templates, field lists, or expected formats.
  • Validation status is shown for each record or field.
  • Skipped records and processing errors remain visible in the workspace.
Cleaning and validation workspace

Cleaning and validation workspace

Functions on this screen
Extract Data with Python Deduplicate data Real-time Data Validation Validate Extracted Fields Log skipped records

Clean records reach the required destination

The export view connects cleaned data with the destination structure required by the client. It maps columns to files, spreadsheets, or databases.

A processing summary and run log show processed, skipped, and error counts for the export.

How it works

  1. The export module maps cleaned columns to the selected output format or destination schema.
  2. The system writes records as CSV, Excel, Parquet, JSON, plain text, Google Sheets, SQL, or database records.
  3. The run log records the processing summary, skipped items, and errors.
  • SQL destinations include PostgreSQL, MySQL, and SQLite records.
  • Spreadsheet destinations can use Excel or Google Sheets structures.
Note: Documentation, setup instructions, source code, and dependency information support ongoing management by the client team.
Structured export and destination view

Structured export and destination view

Functions on this screen
Organize extracted data Excel Export Tool Store data in SQL database Update Excel database automatically Insert New Records into MySQL

What the solution gives

With this solution, the data processing company can:

  • Run repeatable extraction workflows for recurring sources
  • Scrape selected website fields and extract records from files, APIs, and databases
  • Clean, normalize, transform, consolidate, and deduplicate records
  • Validate data before it reaches a destination
  • Export consistent records to files, spreadsheets, SQL databases, or applications
  • Review skipped records, errors, and processing summaries

Could ready-made systems do the job?

Before the design, the task was compared with ready-made products by their own descriptions.

What the client needs Data extraction software Data Extraction Web scraping platforms Oxylabs, Zyte, Scrapfly Data integration platforms Dataddo, Improvado Custom solution
Run repeatable extraction workflows
Export in required data formats —
Clean and deduplicate source data — — —
Provide logs, code, and documentation — — —
Fits the client's own rules: stages, roles, reports — — —
All steps in one place — — —
No manual steps between programs partly partly partly

By the public descriptions of the products and their category.

Ready-made products cover data extraction, web collection, and data movement, and a ready-made product is enough when only one of these parts is needed.

For this client, the required sources, cleaning rules, validation, reports, and destinations had to work together in one place.

The products' descriptions did not provide the full combination of the client's own rules and source-to-destination preparation.

In the end

In the end, the solution was made to reduce manual work between mixed-source records and clean files or databases.

FAQ

Thinking about building a similar system? Read the answers below to see how a custom extraction and data-cleaning workflow can fit your sources and destinations.

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