Search Results for "fielded text"
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Showing 4 open source projects for "fielded text"
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SKUDONET ADC, operates at the application layer, efficiently distributing network load and application load across multiple servers. This not only enhances the performance of your application but also ensures that your web servers can handle more traffic seamlessly.
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HubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
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TFieldedText
Fielded Text (CSV) file parser/generator
TFieldedText is a component which allows you to easily: generate and parse Fielded Text Files (eg. CSV files); and create and edit Fielded Text Meta files. For more information about the Fielded Text standard see http://www.fieldedtext.org -
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FTEditor is an editor for Fielded Text files (see http://www.fieldedtext.org). It lets you visually and interactively create and edit the Meta for a Fielded Text file. It also lets you edit a Fielded Text file with a simultaneous text and grid view.
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ftvalid
Fielded Text (CSV) File Validator
ftvalid is a command line executable which can validate a text file against a fielded text meta file. It can also verify the uniqueness of a set of fields in the file. A file will be successfully validated if its format matches that specified in the Meta file and the specified fields (if any) are unique across all lines. -
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Nucular Archiving System for creating full text indices for fielded data. Python API, web, and command line interfaces. Fast. Very light weight. Concurrent read/writes with no possible locking issues. No server process. Proximity. Facets. Funny name.
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BigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
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