The project's objective was to establish an automated pipeline for collecting and performing deep analysis on a large volume of job posting data from a multitude of heterogeneous sources, encompassing both Russian and European platforms. The primary challenge stemmed from the necessity to work with resources that differed radically from one another: they provided information in varying volumes and structures, employed diverse protection technologies, and used different data presentation formats. This demanded the creation of not just a parser, but a universal, resilient, and adaptive system.
To solve this task, a horizontally scalable system was developed, where the logic for data collection was clearly separated from the logic for its aggregation and analytical processing. This approach ensured high flexibility, fault tolerance, and the ability to independently scale individual components.
The core of the system became a central management module, which coordinates the work of distributed data collection robots. Specialized robots were developed to interact with each external resource. These robots are equipped with various engines and integrated with third-party services for efficient data extraction.
The management module intelligently distributes tasks, interacts with proxy services to ensure anonymity, and enriches raw data with additional information from external APIs. A crucial part of the architecture was the integration with LLM (AI) models, which are used for rapid cleansing, structuring, and extraction of key insights from heterogeneous texts, ensuring an optimal cost-to-quality ratio for processing.
The obtained and enriched data is aggregated, analyzed, and made available for in-depth study via an API for third-party BI systems. As a result, we created not merely a data scraper, but a vertically and horizontally scalable analytical platform capable of adapting to changing requirements and the continuously evolving protection systems of external sources.
We successfully solved the following key tasks:
The outcome of our work is a high-performance system capable of automatically handling large volumes of heterogeneous data. Key achievements include the full automation of the entire cycle - from planning and collection to cleansing, enrichment, and aggregation of information, without manual intervention. The implemented architecture ensures the rapid daily processing of thousands of job postings with high resilience to failures. Thanks to the integration with LLMs, the system doesn't just collect data but "understands" it, extracting the essence, categories, and key parameters, thereby transforming fragmented information into structured analytical views. The use of a built-in task scheduler allows for easy process configuration, and the system itself can be quickly adapted to new sources and evolving requirements. As a ready-made analytics platform, the system provides convenient API access to the cleansed data for subsequent visualization and in-depth analysis in BI tools, equipping the client with a powerful instrument for data-driven decision-making.
Django, Python, Selenium, Beautiful Soup, Scrapefly, OpenAPI, OpenAI, FastAPI, REST API, React JS, PostgreSQL, MongoDB, Suite CRM Integration
