Results for chunked-file-reader

Provides a large file reading as chunked

[](https://www.npmjs.com/package/@aws-sdk/chunked-stream-reader-node) [](http

This module is an abstracted reader for use in various Teraslice readers. It uses an externally-defined reader to read data and the packages up the data in a DataEntity for use with other Teraslice processors.

micromark utility to splice and push with giant arrays

Properties file reader for Node.js

Tokenized zip support

Read binary packets...

MSG Reader

Minimal and efficient cross-platform file watching library

Asynchronous, buffered, line-by-line file/stream reader

React component that handles csv file input.

Tabular data chunked into scrollable panes

Maxmind DB (MMDB) Library

File Upload widget with multiple file selection, drag&drop support, progress bar, validation and preview images, audio and video for jQuery. Supports cross-domain, chunked and resumable file uploads. Works with any server-side platform (Google App Engine,

Reads PHP arrays from a string into a JavaScript object

A Node.js/Typescript module to read tabular data from the dBase DB (.dbf) file.

Ledger devices

Collection of utilities which helps to process different kind of test results as Allure Results

A React Component for reading QR codes from the webcam

A promise based streaming tokenizer

Various helper utilities for working with buffers and binary data

Allure Reader API

A generic toolkit to help with accessibility

Parse http headers, works with browserify/xhr

SmarterCSV is a high-performance CSV reader and writer for Ruby focused on fastest end-to-end ingestion — not just parsing. It returns ready-to-use hashes with configurable header and value transformations, intelligent defaults, and automatic delimiter discovery. Built for real-world data pipelines, SmarterCSV supports chunked processing for large files, streaming via Enumerable APIs, and C acceleration to optimize the full ingestion path (parsing + hash construction + conversions). Designed to handle messy user-uploaded CSV while remaining easy to integrate with Rails, ActiveRecord imports, Sidekiq jobs, parallel processing, and S3-based workflows.