Dataset

app-store-metadata-api

App Usage & Downloads API 1 source

Programmatic access delivers app store details, chart positions and software development kit intelligence for both iOS and Google Play.

Dataset card

The 42matters App Store Metadata API provides programmatic access to app store metadata, rankings and SDK information. It is a commercial data product covering mobile application ecosystems.

What this dataset contains

The dataset exposes metadata drawn from public app stores, with tags indicating coverage of apps, rankings, SDK detection and store-level attributes. The publisher, 42matters, is a known provider of app intelligence, and the product is delivered as an API rather than a static file. The size of the underlying corpus and the specific stores covered are not disclosed in the available documentation.

Structure and fields

No explicit schema, column list or sample rows are provided in the source materials, so the exact field names cannot be confirmed. The tags suggest that typical fields would relate to app identifiers, store rankings and SDK fingerprints, but these are inferred rather than documented. Analysts should consult the vendor documentation directly for the authoritative schema.

How analysts use it

App store metadata is commonly used by investment analysts to track downloads, rankings and technology adoption signals for public and private app publishers. SDK-level data can support competitive intelligence and ad-tech exposure analysis, while ranking changes may serve as a proxy for user engagement trends. Given the lack of confirmed fields in the source facts, any specific use case should be validated against the vendor's documentation.

Documentation & references 2
  1. 42matters/app-store-metadata-api: listing on the provider website
  2. 42matters: official website

Further reading

All guides
  • 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.
  • Alt data delivery formats and pipelinesAPI, S3, Snowflake, Parquet, webhooks: how datasets are actually delivered, what each format costs operationally, and how to design ingestion that survives schema drift and vendor quirks.
  • 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.

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