DataSieve: Text to Data

What is DataSieve?

DataSieve: Text to Data is an offline app for iPhone, iPad, and Mac that extracts structured information from unstructured text and files. It scans input you provide—such as documents, archives, or code/log text—to pull out items like emails, dates, URLs, and other data types.

The core purpose is to help you turn messy or mixed-content sources into cleaner, organized outputs quickly, using local processing (no cloud and no data sharing).

Key Features

• Extracts multiple data types from one scan (e.g., emails, phone numbers, URLs, dates), useful when inputs contain mixed information.
• Works with different input sources, including plain text and file-based inputs such as JSON, HTML, CSV, XLSX, ODS, DOCX/ODT, PDF, EPUB, and ZIP/other archives.
• Batch processing via drag & drop: you can drag files or folders into the app to extract data across multiple items.
• Archive support: ZIP and other archives can be processed by extracting and scanning the files inside.
• Custom extract types (Version 2.1): define and save your own data patterns to extract exactly what you need.
• Export options for extracted results: copy as text/JSON/HTML or export as CSV, XLSX, DOCX, ODS, or ODT.
• Private by design: DataSieve operates entirely offline—no cloud, tracking, or data sharing is indicated.

How to Use DataSieve

1. Open DataSieve and provide input by pasting/selecting text or using drag & drop to add files, folders, or archives.
2. Start the extraction to scan the input for supported data types (or use custom extract types if you’ve set them up).
3. Review the extracted results and export them using copy (text/JSON/HTML) or file export formats (CSV/XLSX/DOCX/ODS/ODT).

Use Cases

• Extract contact information from mixed sources: pull emails and phone numbers from text snippets or documents without manually searching.
• Parse reports, PDFs, or EPUBs for key details: locate dates, addresses, URLs, and related items across document content.
• Clean up batch data for analysis: extract and consolidate fields from many files (including folders) into structured outputs.
• Pull structured information from code/log material: scan JSON/HTML/CSV and text logs to identify URLs, keywords, file paths, and similar elements.
• Build repeatable extraction workflows: create custom extract patterns (Version 2.1) to target specific formats you encounter regularly.

FAQ

• Is DataSieve cloud-based? No. The app is described as working entirely offline with no cloud, no tracking, and no data sharing.

• What kinds of files can it process? The App Store listing includes support for text, JSON, HTML, CSV, XLSX, ODS, Word (DOCX/ODT), PDF, EPUB, ZIP and other archives, and folders.

• What can it extract? The listing mentions emails, phone numbers, URLs, dates, addresses, hashtags, coordinates, credit card numbers, keywords, and file paths, among other items.

• How can I save the extracted results? You can copy extracted data as text, JSON, or HTML, or export as CSV, XLSX, DOCX, ODS, or ODT.

• Can I define my own extraction patterns? Yes. In Version 2.1, the app adds the ability to create custom extract types by defining and saving data patterns.

Alternatives

• Text/data extraction utilities (general category): alternatives may focus on regex-based extraction from text, which can be flexible but may require more manual setup than DataSieve’s file and archive scanning.
• Spreadsheet or document workflows (CSV/Excel/Sheets + parsing): for some tasks, exporting to spreadsheets and using built-in parsing can work, but it typically depends on preparing the input first rather than extracting directly from documents/archives.
• Local document parsing scripts/tools (developer category): scripts can extract specific fields from PDFs/archives, but they usually require coding and a more custom workflow to handle varied file types and outputs.
• OCR/document mining tools (adjacent category): for documents that contain scanned images or complex layouts, OCR-focused tools may be more appropriate, while DataSieve emphasizes extracting from provided text and supported file formats.