Dataset

SAMPLE Forager ai Global Job Postings Data Set 732M Records 21M Updates Month Delivery

Jobs & Workforce Databricks 1 source

Dataset card

SAMPLE Forager ai Global Job Postings Data Set 732M Records 21M Updates Month Delivery is listed on Databricks Marketplace by Forager ai.

The data

Check the source listing for a detailed description and documentation.

Documentation & references 1
  1. Forager ai/SAMPLE Forager ai Global Job Postings Data Set 732M Records 21M Updates Month Delivery: listing on Databricks Marketplace

Further reading

All guides
  • What is alternative data?Alternative data in investment research: what the term covers, how it differs from data format, and why provenance, timing, access and validation matter.
  • Job postings as an investment signalWhy hiring data predicts fundamentals: growth intent, expansion geography, technology adoption and cost control, how postings are collected, structured and turned into tradeable indicators.
  • Web scraping and compliance in alternative dataHow scraped datasets are actually collected, where terms of service and computer-muse laws draw lines, what hiQ v. LinkedIn settled and did not, and the diligence checklist for legally fragile data.
  • Free alternative data sourcesFree alternative data sources for filings, hiring, economic history, satellite imagery and web research, with access details and commercial-use limits.
  • Alternative data for investingHow funds turn alternative data into an investment edge: signal families, evaluation, backtesting, compliance and the build-vs-buy decision, mapped to the datasets and providers indexed on this encyclopedia.
  • Job market data: datasets and providersCompare job-posting APIs, free hiring indexes and official labour statistics. Learn what each measures and how to test coverage, duplicates and historical timing.
  • Alternative data pricing: costs and comparisonCompare free sources, API credits and bulk data licences. Work out total cost using published pricing examples and a practical vendor-quote worksheet.
  • Point-in-time data: avoiding look-ahead biasPoint-in-time data preserves what was available at a past decision date. Learn to handle revisions, delayed records and historical joins with worked examples.

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