Every day, millions of searches reveal what’s capturing people’s attention. This dataset tracks daily trending Google searches in the United States, collected automatically via the Google Trends API since 19 September 2025.

A lightweight pipeline handles the workflow: - A scheduled script pulls the latest trending topics once a day. - The data is stored in Google BigQuery, where it undergoes only basic cleaning (removing duplicates, normalizing dates). Beyond that, the dataset is intentionally left “as-is” to reflect real-world data pipelines and give Kagglers room to tackle the imperfections themselves. - Fresh records are published directly to Kaggle, ensuring the dataset is always current.

This resource is designed for anyone who wants to explore what’s “buzzing” in the US: - Content creators can spot ideas that audiences are actively searching for. - Data analysts can visualize patterns in search interest over time. - Students and learners can practice building queries, dashboards, and predictive models with live, evolving data. - Researchers can study how cultural moments and events show up in online behavior.

With daily updates and historical records preserved in BigQuery, this dataset balances realism and usability: clean enough to work with, but still raw enough to present a genuine challenge.

Discover key Google usage statistics, including search volume, user demographics, popular services, mobile trends, and global reach!

Daily Google Trends data from January 2025 to April 2026 covering worldwide search interest.

This dataset contains real trending topics with interest scores, categories, regions, and related queries — perfect for trend analysis, NLP, forecasting, sentiment tracking, and building dashboards.

Key Features: - Daily trending topics with Google Interest Score (0-100) - Categorized into AI, Crypto, Politics, Entertainment, Sports, and more - Multi-region coverage including Worldwide, USA, India, UK, and others - Over 10,000 rows of clean, ready-to-use data

Ideal for projects on trend prediction, topic modeling, time series analysis, and viral content research.

Google Search Trends: Travel & Accommodations: American Airlines data was reported at 0.000 Score in 15 Apr 2026. This stayed constant from the previous number of 0.000 Score for 14 Apr 2026. Google Search Trends: Travel & Accommodations: American Airlines data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 15 Apr 2026, with 1597 observations. The data reached an all-time high of 38.000 Score in 15 Oct 2022 and a record low of 0.000 Score in 15 Apr 2026. Google Search Trends: Travel & Accommodations: American Airlines data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Myanmar – Table MM.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Training: Udemy data was reported at 6.000 Score in 15 Apr 2026. This records a decrease from the previous number of 7.000 Score for 14 Apr 2026. Google Search Trends: Online Training: Udemy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 15 Apr 2026, with 1597 observations. The data reached an all-time high of 100.000 Score in 12 Nov 2022 and a record low of 0.000 Score in 10 Apr 2026. Google Search Trends: Online Training: Udemy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Myanmar – Table MM.Google.GT: Google Search Trends: by Categories.

This dataset pulls job postings from Google's search results for Data Analyst positions in the United States.

Data collection started on November 4th, 2022, and adds ~100 new job postings to this dataset daily.

Google Search Trends: Online Games: Call of Duty data was reported at 0.000 Score in 07 Apr 2026. This stayed constant from the previous number of 0.000 Score for 06 Apr 2026. Google Search Trends: Online Games: Call of Duty data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 07 Apr 2026, with 1589 observations. The data reached an all-time high of 82.000 Score in 10 Feb 2022 and a record low of 0.000 Score in 07 Apr 2026. Google Search Trends: Online Games: Call of Duty data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Guyana – Table GY.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Training: Coursera data was reported at 6.000 Score in 07 Apr 2026. This records an increase from the previous number of 3.000 Score for 06 Apr 2026. Google Search Trends: Online Training: Coursera data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 07 Apr 2026, with 1589 observations. The data reached an all-time high of 88.000 Score in 02 Nov 2024 and a record low of 0.000 Score in 26 Mar 2026. Google Search Trends: Online Training: Coursera data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Guyana – Table GY.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Games: Fortnite data was reported at 11.000 Score in 15 Apr 2026. This records a decrease from the previous number of 15.000 Score for 14 Apr 2026. Google Search Trends: Online Games: Fortnite data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 15 Apr 2026, with 1597 observations. The data reached an all-time high of 100.000 Score in 22 May 2023 and a record low of 0.000 Score in 03 Apr 2026. Google Search Trends: Online Games: Fortnite data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Myanmar – Table MM.Google.GT: Google Search Trends: by Categories.

Experimental studies in the area of Psychology and Behavioral Economics have suggested that people change their search pattern in response to positive and negative events. Using Internet search data provided by Google, we investigated the relationship between stock-specific events and related Google searches. We studied daily data from 13 stocks from the Dow-Jones and NASDAQ100 indices, over a period of 4 trading years. Focusing on periods in which stocks were extensively searched (Intensive Search Periods), we found a correlation between the magnitude of stock returns at the beginning of the period and the volume, peak, and duration of search generated during the period. This relation between magnitudes of stock returns and subsequent searches was considerably magnified in periods following negative stock returns. Yet, we did not find that intensive search periods following losses were associated with more Google searches than periods following gains. Thus, rather than increasing search, losses improved the fit between people’s search behavior and the extent of real-world events triggering the search. The findings demonstrate the robustness of the attentional effect of losses.

Google Search Trends: Online Classroom: Zoom data was reported at 0.000 Score in 24 Nov 2024. This stayed constant from the previous number of 0.000 Score for 23 Nov 2024. Google Search Trends: Online Classroom: Zoom data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 24 Nov 2024, with 1090 observations. The data reached an all-time high of 11.000 Score in 23 May 2022 and a record low of 0.000 Score in 24 Nov 2024. Google Search Trends: Online Classroom: Zoom data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Laos – Table LA.Google.GT: Google Search Trends: by Categories.

Content

Puerto Rico Media

This folder contains data behind the stories: * The Media Really Has Neglected Puerto Rico * The Media Really Started Paying Attention To Puerto Rico When Trump Did

Online News Data

Data about Online News was collected using the Media Cloud dashboard, an open source suite of tools for analyzing a database of online news.

• mediacloud_hurricanes.csv contains the number of sentences per day that mention Hurricanes Harvey, Irma, Jose, and Maria in online news.
• mediacloud_states.csv (Updated through 10/10/2017) contains the number of sentences per day that mention Puerto Rico, Texas, and Florida in online news.
• mediacloud_trump.csv (Updated through 10/10/2017) contains the number of headlines that mention Puerto Rico, Texas, and Florida, as well as headlines that mention those three locations along with 'President' or 'Trump'.
• mediacloud_top_online_news.csv contains a list of sources included in Media Cloud's "U.S. Top Online News" collection.

TV News Data

TV News Data was collected from the Internet TV News Archive using the Television Explorer tool.

• tv_hurricanes.csv - contains the percent of sentences per day in TV News that mention Hurricanes Harvey, Irma, Jose, and Maria.
• tv_hurricanes_by_network.csv - contains the percent of sentences per day in TV News per network that mention Hurricanes Harvey, Irma, Jose, and Maria.
• tv_states.csv (Updated through 10/10/2017) - contains the percent of sentences per day in TV News that mention Puerto Rico, Texas, and Florida.

Google Search Queries

Google search data was collected using the Google Trends dashboard.

• google_trends.csv - Contains data on google trend searches for Hurricanes Harvey, Irma, Jose, and Maria.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

During my tenure at Microsoft, I was often tasked with performing extensive Exploratory Data Analysis (EDA) on some historical data. One major challenge we data analysts encountered was the difficulty in understanding the significance of specific events from several months or even years prior. Although we could employ various filtering mechanisms to sift through news articles, it was still a challenge to gauge whether a subject was a trending topic at that time.

To address this, I developed a Python-based web scraping utility. This tool captures the top 20 search results from various countries—specifically the US, UK, Germany (DE), France (FR), Netherlands (NL), Italy (IT), and Australia (AUS)—and stores them in my personal SQL database.

It's worth noting that there are gaps in the collected data, owing to a range of unforeseen circumstances such as power outages, residential moves, and server maintenance among others.

You are welcome to use this dataset for various types of analysis. For example, you could examine how long public sentiment around a particular event, like the outbreak of the Ukrainian War, remains prevalent in a specific country, like the United States.

I plan to update this dataset on a monthly basis and will also release past datasets for further scrutiny.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14336685%2F6450d3dca3c242ffe165e5c414a03c93%2Fstarbucks-coffee-honduras.jpg?generation=1766374382953542&alt=media" alt="">

Starbucks Multi-Dimensional Time Series Dataset (2007-2024)

Overview

This comprehensive dataset combines financial, operational, economic, and digital metrics related to Starbucks Corporation across multiple time frequencies. It's designed for advanced time series analysis, forecasting, and understanding the relationship between various business indicators and market performance.

Time Range: 2007-2024
Granularities: Daily, Monthly, Quarterly
Primary Markets: United States and China

Dataset Contents

1. Daily Data (starbucks_daily.csv)

4,627 observations covering daily market and economic indicators:

• Date: Trading date
• Stock Market Data:
  • Open, High, Low, Close prices
  • Trading volume
• Economic Indicators:
  • T10Y3M: US 10-Year Treasury Constant Maturity Minus 3-Month Treasury (yield curve indicator)
  • China Bond Metrics:
  • 1-year bond percentage
  • 10-year bond percentage
  • Bond difference (spread between 10-year and 1-year)

Note: T10Y3M has 36 missing values out of 4,627 observations

2. Monthly Data (starbucks_monthly.csv)

211 observations with 433 features covering:

• Google Trends Data: Search interest across different platforms

  • YouTube searches (US)
  • News mentions (US)
  • Web searches (US)
  • Image searches (US)
  • Web searches (China)

• US Coffee Import Statistics: CIF (Cost, Insurance, and Freight) import values and quantities

  • By Country: Import data from 160+ countries including major suppliers (Brazil, Colombia, Vietnam, etc.)
  • By US Port: Import data through 40+ major US ports (Los Angeles, New York, Seattle, etc.)
  • Includes both import values (in USD) and quantities (First Unit of Quantity)

3. Quarterly Data (starbucks_quarterly.csv)

60 observations covering core business metrics:

• Financial Performance:
  • Revenue (both formatted string and numeric values)
• Store Operations:
  • Number of company-operated stores
  • Number of licensed stores

Note: Store count data has 1 missing value

Data Collection & Sources

Financial Data

• Stock prices: Historical daily trading data
• Economic indicators: Federal Reserve Economic Data (FRED)
• China bond data: Chinese financial market sources

Operational Data

• Quarterly reports: Starbucks investor relations (quarterly earnings reports)
• Store counts: Aggregated from quarterly reports
  • Pre-2012: US vs. International division
  • Post-2012: Americas vs. Asia vs. Middle East/Africa division
  • Dataset provides consolidated totals for consistency

Trade Data

• US Coffee Imports: Official US trade statistics
• Comprehensive coverage of import sources and entry points

Digital Presence

• Google Trends: Search interest data across multiple Google properties
• Separate tracking for US and China markets

Data Notes & Considerations

1. Temporal Alignment: The three datasets operate at different frequencies and may require resampling or aggregation for joint analysis

2. Missing Data:

   • T10Y3M: 36 missing values (0.8% of daily data)
   • Store counts: 1 missing value (1.7% of quarterly data)

3. Geographic Focus: Primary concentration on US and China operations, reflecting Starbucks' two largest markets

4. Store Count Methodology: Total store counts are aggregated across all regions due to changing reporting structures in quarterly reports

5. Import Data Granularity: Coffee import data provides both country-of-origin and port-of-entry perspectives

Potential Use Cases

• Time Series Forecasting: Predict stock prices, revenue, or store expansion using multi-frequency data
• Economic Analysis: Study relationships between economic indicators (yield curves, bond spreads) and company performance
• Supply Chain Analysis: Analyze coffee import patterns and their relationship to business operations
• Digital Marketing Insights: Correlate search trends with business performance
• Multi-Market Comparison: Compare US and China market dynamics
• Feature Engineering: Create lagged features, rolling averages, and cross-frequency indicators
• Anomaly Detection: Identify unusual patterns in operational or market data
• Causal Inference: Study the impact of economic events on retail performance

Dataset Structure

starbucks_daily.csv   - 4,627 rows × 10 columns
starbucks_monthly.csv  - 211 rows × 433 columns
starbucks_quarterly.csv - 60 rows × 5 columns

Citation & Acknowledgments

Data compiled from: - Starbucks Investor Relations (quarterly and annual reports) - US Federal Reser...

⭐If this dataset helps you, consider giving it an upvote !

🔍 Context

Google LLC, a subsidiary of Alphabet Inc., is one of the most influential technology companies in the world. Founded in 1998 and headquartered in Mountain View, California, Google has transformed how people access information and interact with the digital world.

From a simple search engine to a global tech powerhouse, Google now operates across multiple domains:

• 🌐 Google Services — Search, YouTube, Ads, Android, Chrome
• ☁️ Google Cloud — Cloud computing & enterprise solutions
• 🤖 AI & Innovation — Machine learning, DeepMind, automation
• 🚀 Other Bets — Health tech, and more

💡 With a market capitalization often in the trillions of USD, Google remains a dominant force in the global digital economy.

📁 Dataset Overview

• 📅 Time Period: Full historical data available
• 📊 Unit of Analysis: Alphabet Inc. (Google) stock prices
• 💲 Currency: USD

🧾 Variables Description

🚀 What You Can Do With This Dataset

🎯 Use Cases

• 📈 Trend & return analysis
• ⚠️ Risk & volatility insights
• 🔮 Forecasting & prediction
• 💻 Algorithmic trading
• 💼 Portfolio optimization

✨ Perfect playground for data enthusiasts, ML engineers, and future quants — build, experiment, and innovate!

🚀 Why This Dataset?

✔ Clean and structured financial data
✔ Ideal for EDA, visualization, and ML
✔ Real-world dataset used in finance & research
✔ Great for projects, competitions, and learning

📌 Whether you're just starting with data analysis or building advanced trading models — this dataset gives you a solid playground to experiment, learn, and create something impactful.

Google Search Trends: Economic Measures: Short-Time Working data was reported at 0.000 Score in 15 Apr 2026. This stayed constant from the previous number of 0.000 Score for 14 Apr 2026. Google Search Trends: Economic Measures: Short-Time Working data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 15 Apr 2026, with 1597 observations. The data reached an all-time high of 99.000 Score in 16 Sep 2025 and a record low of 0.000 Score in 15 Apr 2026. Google Search Trends: Economic Measures: Short-Time Working data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Myanmar – Table MM.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Games: Call of Duty data was reported at 0.000 Score in 21 Feb 2026. This stayed constant from the previous number of 0.000 Score for 20 Feb 2026. Google Search Trends: Online Games: Call of Duty data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 21 Feb 2026, with 1544 observations. The data reached an all-time high of 2.000 Score in 17 Dec 2022 and a record low of 0.000 Score in 21 Feb 2026. Google Search Trends: Online Games: Call of Duty data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Taiwan (China) – Table TW.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Computer & Electronics: Samsung Electronics data was reported at 27.000 Score in 15 Apr 2026. This records a decrease from the previous number of 29.000 Score for 14 Apr 2026. Google Search Trends: Computer & Electronics: Samsung Electronics data is updated daily, averaging 45.000 Score from Dec 2021 (Median) to 15 Apr 2026, with 1597 observations. The data reached an all-time high of 87.000 Score in 25 Jul 2023 and a record low of 0.000 Score in 03 Jul 2023. Google Search Trends: Computer & Electronics: Samsung Electronics data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Myanmar – Table MM.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Classroom: Google Classroom data was reported at 28.000 Score in 21 Feb 2026. This records an increase from the previous number of 19.000 Score for 20 Feb 2026. Google Search Trends: Online Classroom: Google Classroom data is updated daily, averaging 79.000 Score from Dec 2021 (Median) to 21 Feb 2026, with 1544 observations. The data reached an all-time high of 100.000 Score in 02 Sep 2025 and a record low of 0.000 Score in 02 Aug 2025. Google Search Trends: Online Classroom: Google Classroom data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Taiwan (China) – Table TW.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Computer & Electronics: Samsung Electronics data was reported at 35.000 Score in 21 Feb 2026. This records a decrease from the previous number of 38.000 Score for 20 Feb 2026. Google Search Trends: Computer & Electronics: Samsung Electronics data is updated daily, averaging 36.000 Score from Dec 2021 (Median) to 21 Feb 2026, with 1544 observations. The data reached an all-time high of 100.000 Score in 11 Feb 2025 and a record low of 0.000 Score in 03 Mar 2023. Google Search Trends: Computer & Electronics: Samsung Electronics data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Taiwan (China) – Table TW.Google.GT: Google Search Trends: by Categories.