VEDIT edits any text, data, binary file in ASCII, Hex or EBCDIC; even 100+ Gigabyte files. Ideal for Datenbank and mainframe editing, program development. Fast, compact and configurable. Windows, DOS, Linux

VEDIT edits any text, data, binary file in ASCII, Hex or EBCDIC; even 100+ Gigabyte files. Ideal for Datenbank and mainframe editing, program development. Fast, compact and configurable. Windows, DOS, Linux

Extract fields that contain specific text you are searching for in Microsoft Access. Also, extract parts of a field by specifying starting and ending characters. The text that is extracted is what is found between.

Convert CSV and text files into HTML files. Each line in the file creates a HTML row and each separating character in a line creates a HTML column.

Convert CSV files into XML files. Comma separated / delimited files may or may not have a header row on the first line. This application is simple to use and provides a few options to control how you want the XML output to appear.

Search for text within multiple files and, if found, extract the line. Extract lines that do not contain certain text. Extract text between starting and ending characters throughout files. Extract the same lines by number from multiple files.

Find E-Mail-Adresse addresses in large files. Load your files into the list, and this application will return a list of e-mail addresses that can be saved as a list or comma separated list. The extraction process is done quickly without complicated options.

This application quickly searches Word files for E-Mail-Adresse addresses and extracts them. Results can then be saved to a text file as a list or a comma separated list.

This is an ASP.NET class that saves files uploaded through an HTTP post. It can save to disk or export the files as a byte array or a MemoryStream. It requires no dependent code in the client and is suitable for use with HTTP file posting controls.

dtSearch is "the most powerful document search tool on the market"-- Wired Mag. Searches terabytes of word processor, Datenbank, spreadsheet, ZIP, XML, PDF, HTML, emails & more. Built-in Web Spider. See www.dtsearch.com for more products.

dtSearch is "the most powerful document search tool on the market"-- Wired Mag. Searches terabytes of word processor, Datenbank, spreadsheet, ZIP, XML, PDF, HTML, emails & more. Built-in Web Spider. See www.dtsearch.com for more products.

Ideaspad, helps you be more organized. Tree navigation, a powerful information organisation feature. A word processor, including spell checker. Topics and icons making it easy to use. An information manager (or PIM) a Datenbank of your information.

Search and find E-Mail-Adresse addresses in Microsoft Access databases. Emails are saved to text file.

This application will allow you to transfer tables to and from Access and MS SQL Server 2000 or 2005 databases. Importing and exporting can be done without SQL knowledge.

Search for and replace data (characters) in your Microsoft Access Datenbank tables. You decide which tables to make replacements in. Replacements are not made in special cases such as booleans (True/False) and primary keys.

This application will allow you to transfer tables to and from Access and MySQL databases. Importing and exporting can be done without SQL knowledge. This program saves time if you need to transfer tables between the two databases quickly.

Search, find and eliminate duplicate lines or words in one or many text files. There is a feature to automatically backup your files before taking action.

Manage and compare your E-Mail-Adresse lists. Remove (delete) emails with or without certain text, remove blank E-Mail-Adresse entries in your lists, compare two E-Mail-Adresse lists and keep matches or differences, remove duplicate emails.

Search for certain characters within a MS SQL Server Datenbank. If these characters are found, extract the fields and save to file. Also, extract data between starting and ending characters throughout the entire Datenbank.

Search for and replace data (characters) in your MS SQL Server 2000 or 2005 Datenbank tables. You decide which tables to make replacements in.

Search for certain characters within a MySQL Datenbank. If these characters are found, extract the field and save to file. Also, extract MySQL data between starting and ending characters throughout the entire Datenbank.