{"id":45648,"date":"2026-09-11T02:32:47","date_gmt":"2026-09-11T02:32:47","guid":{"rendered":"http:\/\/www.withdata.com\/blog\/?p=45648"},"modified":"2026-09-11T02:33:05","modified_gmt":"2026-09-11T02:33:05","slug":"datafilesplitter-2-5-released","status":"publish","type":"post","link":"https:\/\/www.withdata.com\/blog\/pr\/datafilesplitter-2-5-released.html","title":{"rendered":"DataFileSplitter 2.5 Released: New Output Formats \u2013 Fixed\u2011Width Text and YAML"},"content":{"rendered":"<p>September 10, 2026 \u2014 Withdata Software announces the release of <strong><a href=\"https:\/\/www.withdata.com\/data-file-splitter\/\">DataFileSplitter 2.5<\/a><\/strong>, the cross\u2011platform desktop utility for breaking oversized data files into smaller, manageable chunks. This release adds two new target output formats: split to fixed\u2011width text files and split to YAML files, extending support for legacy and configuration\u2011oriented data workflows.<\/p>\n<h2>What\u2019s New in DataFileSplitter 2.5<\/h2>\n<h3>1. Split to Fixed\u2011Width Text Files<\/h3>\n<p>You can now split large source datasets and export output as fixed\u2011width text files. This is ideal for legacy mainframe and COBOL\u2011style data pipelines that require fixed\u2011length record outputs.<\/p>\n<h3>2. Split to YAML Files<\/h3>\n<p>Generate split output files in YAML format. Perfect when working with configuration data, DevOps artifacts and structured records that need YAML output after splitting large source files.<\/p>\n<h3>3. Built\u2011in Proven Splitting Capabilities<\/h3>\n<p>Building on prior versions, DataFileSplitter continues to support rich splitting features:<\/p>\n<ul>\n<li>Split Excel by sheet, HTML by table, SQL by table<\/li>\n<li>Split by record count or column value<\/li>\n<li>Preserve JSON and XML structure during split<\/li>\n<li>Command\u2011line automation and saved splitting sessions<\/li>\n<li>100% local offline processing for data privacy<\/li>\n<\/ul>\n<h2>About DataFileSplitter<\/h2>\n<p><a href=\"https:\/\/www.withdata.com\/data-file-splitter\/\">DataFileSplitter<\/a> is a cross\u2011platform tool for splitting huge CSV, TSV, JSON, XML, Excel, SQL, HTML and log files. Available for Windows, macOS and Linux. It helps data engineers, analysts and DBAs divide oversized files for easier sharing, import and further processing, without uploading any sensitive data to external servers.<\/p>\n<ul>\n<li>Split by row count or column value<\/li>\n<li>GUI wizard plus command\u2011line interface for automated pipelines<\/li>\n<li>Save and reload split task sessions<\/li>\n<li>Preserve headers, original structure and file encoding<\/li>\n<li>Fully local processing to protect confidential datasets<\/li>\n<\/ul>\n<h2>Upgrade Today<\/h2>\n<p><a href=\"https:\/\/www.withdata.com\/data-file-splitter\/\">DataFileSplitter 2.5<\/a> is ready for download: <a href=\"https:\/\/www.withdata.com\/data-file-splitter\/download.html\">https:\/\/www.withdata.com\/data-file-splitter\/download.html<\/a>. Existing users can update directly. New users may download the free trial to test splitting to fixed\u2011width text and YAML outputs.<\/p>\n<p>Withdata Software will keep improving DataFileSplitter according to user feedback for real\u2011world large\u2011file processing scenarios.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>September 10, 2026 \u2014 Withdata Software announces the release of DataFileSplitter 2.5, the cross\u2011platform desktop utility for breaking oversized data files into smaller, manageable chunks. This release adds two new target output formats: split to fixed\u2011width text files and split to YAML files, extending support for legacy and configuration\u2011oriented data workflows. What\u2019s New in DataFileSplitter [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[69],"tags":[13758,13756,2047,13757,13753,13754],"class_list":["post-45648","post","type-post","status-publish","format-standard","hentry","category-pr","tag-data-engineering","tag-datafilesplitter","tag-etl","tag-file-split","tag-fixedwidth-text","tag-yaml"],"_links":{"self":[{"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/posts\/45648","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/comments?post=45648"}],"version-history":[{"count":1,"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/posts\/45648\/revisions"}],"predecessor-version":[{"id":45649,"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/posts\/45648\/revisions\/45649"}],"wp:attachment":[{"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/media?parent=45648"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/categories?post=45648"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.withdata.com\/blog\/wp-json\/wp\/v2\/tags?post=45648"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}