Search Results for "structured storage"

...The project includes all the infrastructure required to run a full instance locally or in production, combining web servers, databases, and search indexing systems into a scalable architecture. It relies heavily on technologies such as Elasticsearch for search functionality and MariaDB for structured data storage, enabling fast and efficient querying across massive datasets. The system is designed with redundancy and replication in mind, allowing distributed deployments and mirrored environments to handle high traffic and large data volumes. It also includes tooling for importing datasets, managing metadata, and maintaining structured archives using custom formats.

...Developers can use the software either as a standalone command line application or integrate it into their own Python applications through its library interface. Extracted article data can be stored in different formats and systems, including JSON files or database-backed storage solutions.

Scrapy is a fast, open source, high-level framework for crawling websites and extracting structured data from these websites. Portable and written in Python, it can run on Windows, Linux, macOS and BSD. Scrapy is powerful, fast and simple, and also easily extensible. Simply write the rules to extract the data, and add new functionality if you wish without having to touch the core. Scrapy does the rest, and can be used in a number of applications. It can be used for data mining, monitoring...

Cloud Custodian enables users to be well managed in the cloud. The simple YAML DSL allows you to easily define rules to enable a well-managed cloud infrastructure, that's both secure and cost-optimized. It consolidates many of the ad-hoc scripts organizations have into a lightweight and flexible tool, with unified metrics and reporting. Custodian supports managing AWS, Azure, and GCP public cloud environments. Besides just providing reports of issues, Custodian can actively enforce the...

...It aggregates many independent crawler examples created by contributors and organized into separate subprojects that target specific sites or data sources. These examples demonstrate how to build and operate web scrapers capable of collecting structured information such as product listings, news content, job postings, social media data, and other publicly available web data. It aims to help developers understand the full workflow of web scraping, including request simulation, data extraction, storage, and handling anti-scraping techniques. It includes crawlers for platforms such as ecommerce marketplaces, blogging platforms, recruitment sites, and social networks, providing real-world practice scenarios. ...