> For the complete documentation index, see [llms.txt](https://docs.tickertrends.io/tickertrends/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tickertrends.io/tickertrends/documentation-v2/2.-data-coverage-and-data-types/2.1-coverage-overview.md). # 2.1 Coverage Overview One of the most common questions we get is about historical length, granularity, and update frequency. Below is a summary comparison of our key datasets: | Data Type | Historical Length | Granularity | Update Frequency | | --------------------------------------------------------- | --------------------------------- | ------------------------- | --------------------------- | | **Google Search** | 2004-present | Weekly | Weekly | | **Amazon Search** | 2+ years | Monthly | Monthly | | **YouTube Search** | 2008-present | Weekly | Weekly | | **YouTube Views & Subs** | Since YouTube channel inception | Daily | Daily | | **Instagram Followers & Likes** | Since Instagram account inception | Daily | Daily | | **Web Traffic (Main Domain)** | 2+ years | Daily | Weekly | | **Website Traffic (Domain Path)** | 2+ years | Daily | Monthly | | **Tech Adoption** | Multi-year | Weekly | Weekly | | **Mobile App Usage (USA & Global WAUs - iOS & Android)** | 2+ years | Weekly | Monthly | | **Mobile App Store Rankings (Top Free Charts)** | 2+ years | Daily | Weekly | | **Android App Review** | 2+ years | Daily | Daily | | **TikTok Views** | 3+ years | Daily | Weekly | | **TikTok Likes & Follows** | Since TikTok account inception | Daily | Daily | | **Reddit Subscribers** | Since Subreddit account inception | Daily | Daily | | **Reddit Post** | Since Subreddit account inception | Daily | Daily | | **Wikipedia (Page Views)** | Since Wikipedia page inception | Daily | Daily | | **Amazon Product Price & Product Ranking** | 3+ years | Daily | Daily | | **News Article Volume** | 3+ years | Monthly | Monthly | | **NPM Downloads** | Multi-month to multi-year | Daily | Daily | | **Homebrew Installs** | Multi-month to multi-year | Daily | Daily | | **PyPack Downloads** | Multi-month to multi-year | Daily | Daily | | **Pinterest Trends** | 1 year+ | Weekly | Weekly | | **Google Shopping** | 2004-present | Weekly | Weekly | | **Google Images** | 2004-present | Weekly | Weekly | | **Google News** | 2004-present | Weekly | Weekly | | **Podcast Mentions** | 3+ years | Weekly | Weekly | | **Job Skill Adoption** | Multiple years | Daily | Weekly | | **Keyword Mentions in Job Posts** | Multiple years | Daily | Weekly | | **New Company Adoption** | Multiple years | Daily | Weekly | | **Weighted New Company Adoption** | Multiple years | Daily | Weekly | | **Company Hiring Job Posts** | Multiple years | Daily | Weekly | | **Financial Data (Consensus, Estimates, News Sentiment)** | Varies (via external feeds) | Quarterly, Monthly, Daily | Varies (via external feeds) | | **Consumer /** **Developer Trackers** | Varies | Weekly | Varies (Usually bi-weekly) | | **ChatGPT Trends** | 1+ year | Monthly | Monthly | **Data Integrity and Normalization**\ We preserve raw data as closely as possible to the original source to ensure accuracy and transparency. Unlike many providers that pre-normalize datasets, our approach keeps the underlying signals intact. Normalization is applied only within our KPI tracking models, where it is necessary to align disparate signals into a unified framework for tracking company performance. This way, users always have access to both the raw, unadjusted data for direct analysis and the normalized outputs used in KPI tracking. Contact us for specific data or granularity needs at ****.