OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.

Google data search exercises can be used to practice finding data or statistics on a topic of interest, including using Google's own internal tools and by using advanced operators.

Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

Google Patents Public Data, provided by IFI CLAIMS Patent Services, is a worldwide bibliographic and US full-text dataset of patent publications. Patent information accessibility is critical for examining new patents, informing public policy decisions, managing corporate investment in intellectual property, and promoting future scientific innovation. The growing number of available patent data sources means researchers often spend more time downloading, parsing, loading, syncing and managing local databases than conducting analysis. With these new datasets, researchers and companies can access the data they need from multiple sources in one place, thus spending more time on analysis than data preparation.

Content

The Google Patents Public Data dataset contains a collection of publicly accessible, connected database tables for empirical analysis of the international patent system.

Acknowledgements

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:patents

For more info, see the documentation at https://developers.google.com/web/tools/chrome-user-experience-report/

“Google Patents Public Data” by IFI CLAIMS Patent Services and Google is licensed under a Creative Commons Attribution 4.0 International License.

Banner photo by Helloquence on Unsplash

You can check the fields description in the documentation: current Keyword database: https://docs.dataforseo.com/v3/databases/google/keywords/?bash; Historical Keyword database: https://docs.dataforseo.com/v3/databases/google/history/keywords/?bash. You don’t have to download fresh data dumps in JSON or CSV – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

a Beta-coefficients, p-values and R2 were calculated by using a simple time-lagged regression model SPYt+1 = β0t + β1t 〈Δn〉(t,Δt)+ β2tVIXt to investigate the correlation of the S&P 100 index development in the next period (SPYt+1) with the current change over all (and sectoral) company search queries 〈Δn〉(t,Δt) and, as a basic control variable, with the volatility index of the S&P 500 (VIXt), respectively.Influence of company search queries on the S&P 100 index development.

Looker Ecommerce Dataset Description

CSV version of Looker Ecommerce Dataset.

Overview Dataset in BigQuery TheLook is a fictitious eCommerce clothing site developed by the Looker team. The dataset contains information >about customers, products, orders, logistics, web events and digital marketing campaigns. The contents of this >dataset are synthetic, and are provided to industry practitioners for the purpose of product discovery, testing, and >evaluation. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This >means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on >this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public >datasets.

1. distribution_centers.csv

• Columns:
  • id: Unique identifier for each distribution center.
  • name: Name of the distribution center.
  • latitude: Latitude coordinate of the distribution center.
  • longitude: Longitude coordinate of the distribution center.

2. events.csv

• Columns:
  • id: Unique identifier for each event.
  • user_id: Identifier for the user associated with the event.
  • sequence_number: Sequence number of the event.
  • session_id: Identifier for the session during which the event occurred.
  • created_at: Timestamp indicating when the event took place.
  • ip_address: IP address from which the event originated.
  • city: City where the event occurred.
  • state: State where the event occurred.
  • postal_code: Postal code of the event location.
  • browser: Web browser used during the event.
  • traffic_source: Source of the traffic leading to the event.
  • uri: Uniform Resource Identifier associated with the event.
  • event_type: Type of event recorded.

3. inventory_items.csv

• Columns:
  • id: Unique identifier for each inventory item.
  • product_id: Identifier for the associated product.
  • created_at: Timestamp indicating when the inventory item was created.
  • sold_at: Timestamp indicating when the item was sold.
  • cost: Cost of the inventory item.
  • product_category: Category of the associated product.
  • product_name: Name of the associated product.
  • product_brand: Brand of the associated product.
  • product_retail_price: Retail price of the associated product.
  • product_department: Department to which the product belongs.
  • product_sku: Stock Keeping Unit (SKU) of the product.
  • product_distribution_center_id: Identifier for the distribution center associated with the product.

4. order_items.csv

• Columns:
  • id: Unique identifier for each order item.
  • order_id: Identifier for the associated order.
  • user_id: Identifier for the user who placed the order.
  • product_id: Identifier for the associated product.
  • inventory_item_id: Identifier for the associated inventory item.
  • status: Status of the order item.
  • created_at: Timestamp indicating when the order item was created.
  • shipped_at: Timestamp indicating when the order item was shipped.
  • delivered_at: Timestamp indicating when the order item was delivered.
  • returned_at: Timestamp indicating when the order item was returned.

5. orders.csv

• Columns:
  • order_id: Unique identifier for each order.
  • user_id: Identifier for the user who placed the order.
  • status: Status of the order.
  • gender: Gender information of the user.
  • created_at: Timestamp indicating when the order was created.
  • returned_at: Timestamp indicating when the order was returned.
  • shipped_at: Timestamp indicating when the order was shipped.
  • delivered_at: Timestamp indicating when the order was delivered.
  • num_of_item: Number of items in the order.

6. products.csv

• Columns:
  • id: Unique identifier for each product.
  • cost: Cost of the product.
  • category: Category to which the product belongs.
  • name: Name of the product.
  • brand: Brand of the product.
  • retail_price: Retail price of the product.
  • department: Department to which the product belongs.
  • sku: Stock Keeping Unit (SKU) of the product.
  • distribution_center_id: Identifier for the distribution center associated with the product.

7. users.csv

• Columns:
  • id: Unique identifier for each user.
  • first_name: First name of the user.
  • last_name: Last name of the user.
  • email: Email address of the user.
  • age: Age of the user.
  • gender: Gender of the user.
  • state: State where t...

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

Context

Recipe keywords' positions on search; Google and YouTube.
These datasets can be interesting for SEO research for the recipes industry.

Content

243 national recipes (based on Wikipedia's national dish list)
2 keyword versions dish recipe and how to make dish
Total 486 queries (10 results each)

Google: 4,860 rows (defaults to 10 per result, and some missing)
YouTube: 1,455 rows (defaults to 5 per result, and some missing)

Acknowledgements

Google CSE API, YouTube API, Python, requests, pandas, advertools.

Inspiration

It's interesting to know about how things are visible from a search engine perspective, and compare Google and YouTube as well.
National dishes are mostly delicious as well!

Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data was reported at 0.000 % in 09 Mar 2025. This records a decrease from the previous number of 0.030 % for 08 Mar 2025. Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data is updated daily, averaging 0.070 % from Mar 2025 (Median) to 09 Mar 2025, with 9 observations. The data reached an all-time high of 0.070 % in 05 Mar 2025 and a record low of 0.000 % in 09 Mar 2025. Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Belarus – Table BY.SC.IU: Internet Usage: Search Engine Market Share.

Google Search Trends: Travel & Accommodations: American Airlines data was reported at 2.000 Score in 30 Nov 2025. This records an increase from the previous number of 1.000 Score for 29 Nov 2025. Google Search Trends: Travel & Accommodations: American Airlines data is updated daily, averaging 3.000 Score from Dec 2021 (Median) to 30 Nov 2025, with 1461 observations. The data reached an all-time high of 47.000 Score in 30 Jan 2025 and a record low of 0.000 Score in 15 Nov 2025. 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 Taiwan – Table TW.Google.GT: Google Search Trends: by Categories.

The global search engine market, valued at $37.39 billion in 2025, is projected to experience robust growth, driven by the increasing adoption of smartphones and internet penetration across emerging economies. A Compound Annual Growth Rate (CAGR) of 14.82% from 2025 to 2033 indicates a significant expansion of this market. Key drivers include the rising demand for enhanced search capabilities, the proliferation of voice search technology, and the growing importance of search engine optimization (SEO) for businesses. The market's segmentation reveals a dynamic landscape, with both online and offline distribution channels contributing significantly. The end-user segment is divided between personal and commercial use, with the latter showing strong growth potential fueled by the increasing reliance on data-driven marketing and advertising strategies. Major players like Google, Amazon, and Baidu are at the forefront of innovation, constantly refining their algorithms and expanding their functionalities to maintain a competitive edge. The competitive landscape is further shaped by the emergence of specialized search engines catering to niche markets, driving innovation and competition. The market's geographical distribution showcases varying growth rates across regions. North America and Europe currently hold substantial market share, driven by high internet penetration and technological advancement. However, Asia-Pacific is poised for rapid growth due to its expanding digital economy and the rising number of internet users. Factors such as data privacy concerns, increasing regulatory scrutiny, and the potential for algorithm bias represent key restraints to market growth. To mitigate these challenges, search engine companies are investing heavily in responsible AI development and data security measures. The forecast period from 2025 to 2033 will likely see a continuous shift towards personalized search experiences, advanced analytics capabilities, and a greater focus on user privacy, ultimately shaping the future of online information retrieval. Recent developments include: February 2023: Microsoft launched "Binging," a cutting-edge search engine driven by AI. This innovative search engine is powered by a state-of-the-art OpenAI model, specifically fine-tuned to optimize search capabilities. The new OpenAI model draws from the expertise of ChatGPT and GPT-3.5, resulting in even faster and more precise search technology., November 2022: Google introduced local search features that were previously showcased earlier in the year. These features include the ability to search your surroundings using your phone's camera. Google has also unveiled an option to search for restaurants based on specific dishes and a new search functionality integrated into Google Maps' Live View., November 2022: Up until this point, search insights were exclusively accessible in English, focusing on users from the US, India, Canada, and the UK. However, YouTube is currently experimenting with expanding the availability of Search Insights on the desktop to more languages, starting with Japanese, Korean, and Hindi, and with plans to include additional languages in the future.. Key drivers for this market are: Increasing Focus to Improve Customer Experience Across Professional Services, Self Service and Personal Segment to Witness the Highest Growth. Potential restraints include: Increasing Focus to Improve Customer Experience Across Professional Services, Self Service and Personal Segment to Witness the Highest Growth. Notable trends are: Self Service and Personal Segment to Witness the Highest Growth.

Google Search Trends: Travel & Accommodations: American Airlines data was reported at 1.000 Score in 28 Nov 2025. This stayed constant from the previous number of 1.000 Score for 27 Nov 2025. Google Search Trends: Travel & Accommodations: American Airlines data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 28 Nov 2025, with 1459 observations. The data reached an all-time high of 19.000 Score in 02 Nov 2022 and a record low of 0.000 Score in 16 Nov 2025. 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 China – Table CN.Google.GT: Google Search Trends: by Categories.

According to Google search data, from July 2024 and June 2025 the most frequently searched queries in India were “video” and “WhatsApp". These popular searches highlight key content consumption patterns in the country. The strong interest in video-related queries aligns with the fact that video-sharing platforms account for the largest share of monthly social network users in this country. Optimization of video streaming Not surprisingly, video content is witnessing exponential growth in recent years. It is evident in the fact that video streaming accounts for a major share of online mobile traffic across the nation. Recent trends suggest an increase in consumption of video over graphic or text content. Hence, a sound implementation of SEO in videos has become a necessity for a successful content creating channel. One of the major optimization strategies is to cater to the demographic of the nation, which incorporates efficient description, headline, and tag implementation. Keyword search trends Searches related to local preferences are gaining momentum, rendering local SEO invaluable to promoting visibility of the content. Phrases like “near me” and “close to me” have witnessed a significant increase in their frequency of appearances in queries. Since the coronavirus (COVID-19) outbreak, the latter part of 2020 has seen a significant rise in the usage of queries related to the pandemic. This is testament to the influence of recent events on keywords and optimized phrases for improved channel visibility.

Auto-generated structured data of Google Search Console Field Reference from table Available options

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

Google Search Trends: Computer & Electronics: Apple data was reported at 40.000 Score in 14 May 2025. This records an increase from the previous number of 33.000 Score for 13 May 2025. Google Search Trends: Computer & Electronics: Apple data is updated daily, averaging 37.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 09 Sep 2024 and a record low of 0.000 Score in 19 Apr 2025. Google Search Trends: Computer & Electronics: Apple data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Iceland – Table IS.Google.GT: Google Search Trends: by Categories.

It starts with a simple habit: you open your browser and type a question. A few keystrokes later, Google gives you answers, videos, maps, and suggestions before you even finish your thought. For billions of people around the world, this daily interaction is second nature. But behind that blinking cursor...

The global query engine market is booming, projected to reach $40B+ by 2033 with a robust 11% CAGR. This in-depth analysis explores market drivers, trends, restraints, and key players like Google, Baidu, and Microsoft, offering valuable insights into this dynamic sector. Discover the latest market data and projections for personal and commercial applications.

This dataset provides comprehensive access to product search results from Google Shopping in real-time. Search and compare products, offers, and reviews across multiple major retailers and sources. Perfect for e-commerce applications, price comparison tools, and product discovery platforms. The dataset is delivered in a JSON format via REST API.

This dataset investigates the relationship between Wordle answers and Google search spikes, particularly for uncommon words. It spans from June 21, 2021 to June 24, 2025.

It includes daily data for each Wordle answer, its search trend on that day, and frequency-based commonality indicators.

🔍 Hypothesis

Each Wordle answer causes a spike in search volume on the day it appears — more so if the word is rare.

This dataset supports exploration of:

• Wordle Answers
• Trends for wordle answers
• Correlation between wordle answer rarity and search interest

Columns

Notes: - All trend values are relative (0–100 scale, per Google Trends)

🧮 Methodology

• Wordle answers were scraped from wordfinder.yourdictionary.com
• Commonality scores were computed using:
  • wordfreq Python library
  • SUBTLEX-US dataset (subtitle frequency, approximating spoken English)
• Trend data was fetched using Google Trends API via pytrends

📊 Analysis

Can find analysis done using this data in the blog post