Deploy pre-built tools that crawl websites, extract structured data, and feed your applications. Reliable web data without maintaining scrapers.
Automate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
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Create and run cloud-based virtual machines.
Secure and customizable compute service that lets you create and run virtual machines.
Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications.
Content Based File level Data Backup in Python.
This is a utility to backup your files. It can do full and incremental backups.
It will take a directory as input, and will back up the files in that folder and all sub-folders to the backup destination directory.
It can compress each file individually while backing-up.
Mirrors the source directory structure under the target directory.
DataFinder is a data management client developed in Python that primarily targets the management of scientific technical data. The system is able to handle large amounts of data and can be easily integrated in existing working environments.
A set of tools to build your own customed Linux distribution with more managability than raw LFS(Linux From Scratch). Include source package manager, file system backup and realtime mirror apps, and some assistant datastructure writen in Python, etc...