Microservice For Daily Job Scraping: Data Collection Automation
The client needed a dynamic parsing service capable of extracting job postings from various platforms, ensuring seamless integration into their existing system.
We developed a robust scraping and integration system using Node.js and Puppeteer. The system dynamically adapts to diverse platform structures, ensuring consistent and accurate data retrieval. It runs autonomously, but with an intuitive interface to enable manual overrides or deactivate individual parsers when necessary.
The solution streamlined the job publishing workflow, reducing manual data processing time by 85%. The system processed an average of 28000 job postings per day, increasing the platform’s overall efficiency by 40% and improving user satisfaction with up-to-date and accurate listings.
Challenges
- Analysis approaches for each job platform had a unique structure, often without an API for direct data access. This required the creation of custom parsers tailored to the specific layouts and formats of each resource to ensure accurate data extraction.
- Extracting and validating search fields from unstructured text was a significant challenge. A solution was needed to accurately identify key parameters in different job posting formats.
- Developing a system that could parse job descriptions to assign categories. This algorithm was to assign one of the predefined categories based on the job context.
- Data Relevance: The system had to scrape data twice a day, using robust mechanisms to detect and exclude duplicate job postings.
Work Stages
Planning
1
Design
2
Development
3
Testing and Optimization
4
Deployment
5
6
Maintenance
We meticulously analyzed the client’s requirements and formulated a strategy to address their challenges effectively.
Our team conceptualized an intuitive interface tailored to enhance user engagement and facilitate seamless interactions.
Leveraging modern technologies, we engineered a scalable and customizable solution aligned with the client’s specific needs.
Rigorous testing ensured the functionality across various scenarios, followed by iterative refinements to enhance performance and accuracy.
With meticulous attention to detail, we seamlessly integrated the system into the client’s existing infrastructure, ensuring minimal disruption.
Post-deployment, we provided comprehensive support to monitor performance, address any issues, and ensure optimal functionality round the clock.
Node JS
Express.js
Puppeteer
Amazon EC2
Solutions & Technologies
We effectively combined these technologies to create an optimized solution that quickly and accurately collects data from sites. Using Node.js provided flexibility of integration, and parallel requests will speed up processing. Express server makes it possible to add a scraper for each individual resource and manage the launch of each individual parser. Puppeteer allowed to automate the process for sites with closed data.
Results
The solution we developed provides efficient and autonomous processing of large amounts of data on a regular basis. Parallel processes increased the speed of obtaining results by 30%. The system flexibly adds new modules without affecting the work schedule, ensuring smooth integration of additional services. If an error occurs, it is immediately detected and blocks only the affected module, without affecting the overall performance of the system, minimizing risks and ensuring stability.
The deep analysis algorithm allowed for precise segmentation according to specified criteria, which, in turn, increased the accuracy of customer search queries. Thanks to our product, site traffic increased by 24%, and the number of users who used the service until full contact with the employer increased by 18%.
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