This dataset contains two tables: creative_stats and removed_creative_stats. The creative_stats table contains information about advertisers that served ads in the European Economic Area or Turkey: their legal name, verification status, disclosed name, and location. It also includes ad specific information: impression ranges per region (including aggregate impressions for the European Economic Area), first shown and last shown dates, which criteria were used in audience selection, the format of the ad, the ad topic and whether the ad is funded by Google Ad Grants program. A link to the ad in the Google Ads Transparency Center is also provided. The removed_creative_stats table contains information about ads that served in the European Economic Area that Google removed: where and why they were removed and per-region information on when they served. The removed_creative_stats table also contains a link to the Google Ads Transparency Center for the removed ad. Data for both tables updates periodically and may be delayed from what appears on the Google Ads Transparency Center website. About BigQuery This data is hosted in Google BigQuery for users to easily query using SQL. Note that to use BigQuery, users must have a Google account and create a GCP project. This public dataset is included in BigQuery's 1TB/mo of free tier processing. 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. What is BigQuery . Download Dataset This public dataset is also hosted in Google Cloud Storage here and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. We provide the raw data in JSON format, sharded across multiple files to support easier download of the large dataset. A README file which describes the data structure and our Terms of Service (also listed below) is included with the dataset. You can also download the results from a custom query. See here for options and instructions. Signed out users can download the full dataset by using the gCloud CLI. Follow the instructions here to download and install the gCloud CLI. To remove the login requirement, run "$ gcloud config set auth/disable_credentials True" To download the dataset, run "$ gcloud storage cp gs://ads-transparency-center/* . -R" 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. What is BigQuery .

Google Cloud Platform Consulting Service Market size was valued at USD 5 Billion in 2024 and is projected to reach USD 20 Billion by 2032, growing at a CAGR of 16% during the forecast period 2026 to 2032.Growing Cloud Adoption: The pervasive shift from traditional on premise infrastructure to agile, scalable cloud first strategies is perhaps the most significant catalyst for the GCP consulting market. As companies worldwide divest from legacy systems, they encounter a complex landscape requiring expert navigation for efficient planning, seamless migration, and effective management of cloud environments.Data Driven Use Cases: Google Cloud Platform stands out for its robust and sophisticated capabilities in data analytics, exemplified by services like BigQuery, and its advanced Artificial Intelligence/Machine Learning (AI/ML) offerings, such as Vertex AI.

This dataset is maintained by the European Centre for Disease Prevention and Control (ECDC) and reports on the geographic distribution of COVID-19 cases worldwide. This data includes COVID-19 reported cases and deaths broken out by country. This data can be visualized via ECDC’s Situation Dashboard . More information on ECDC’s response to COVID-19 is available here . 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. What is BigQuery . This dataset is hosted in both the EU and US regions of BigQuery. See the links below for the appropriate dataset copy: US region EU region This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. Users of ECDC public-use data files must comply with data use restrictions to ensure that the information will be used solely for statistical analysis or reporting purposes.

Context

Adapted from Wikipedia: OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

To aid researchers, data scientists, and analysts in the effort to combat COVID-19, Google is making a hosted repository of public datasets including OpenStreetMap data, free to access. To facilitate the Kaggle community to access the BigQuery dataset, it is onboarded to Kaggle platform which allows querying it without a linked GCP account. Please note that due to the large size of the dataset, Kaggle applies a quota of 5 TB of data scanned per user per 30-days.

Content

This is the OpenStreetMap (OSM) planet-wide dataset loaded to BigQuery.

Tables: - history_* tables: full history of OSM objects. - planet_* tables: snapshot of current OSM objects as of Nov 2019.

The history_* and planet_* table groups are composed of node, way, relation, and changeset tables. These contain the primary OSM data types and an additional changeset corresponding to OSM edits for convenient access. These objects are encoded using the BigQuery GEOGRAPHY data type so that they can be operated upon with the built-in geography functions to perform geometry and feature selection, additional processing.

Resources

You can read more about OSM elements on the OSM Wiki. This dataset uses BigQuery GEOGRAPHY datatype which supports a set of functions that can be used to analyze geographical data, determine spatial relationships between geographical features, and construct or manipulate GEOGRAPHYs.

This dataset provides download statistics for all package downloads from the Python Package Index (PyPI). It also includes a dataset containing all the metadata for every distribution released on PyPI. The data is streamed in near-real-time from PyPI CDN, after which it is periodically loaded into the BigQuery dataset. 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. What is BigQuery .

Context

To aid researchers, data scientists, and analysts in the effort to combat COVID-19, Google is making a hosted repository of public datasets including OpenStreetMap data, free to access. To facilitate the Kaggle community to access the BigQuery dataset, it is onboarded to Kaggle platform which allows querying it without a linked GCP account. Please note that due to the large size of the dataset, Kaggle applies a quota of 5 TB of data scanned per user per 30-days.

Terms of use

By downloading or using the data, you agree to Google's Terms of Service

Description

This dataset aims to provide insights into what has changed in response to policies aimed at combating COVID-19. It reports movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

This dataset is intended to help remediate the impact of COVID-19. It shouldn’t be used for medical diagnostic, prognostic, or treatment purposes. It also isn’t intended to be used for guidance on personal travel plans.

To learn more about the dataset, the place categories and how we calculate these trends and preserve privacy, read the data documentation

Context

This dataset corresponds to a three-part Medium.com article series covering a fully reproducible walkthrough of Production-Scale Best Practices for IoT data streaming on GCP. Covered topics include: IoT devices setup and streaming into GCP's IoT Core and PubSub, movement of data from PubSub to BigQuery via Dataflow, visualization of that data in Data Studio, and an effective machine learning model construction and deployment process via BigQuery ML (using AutoML Tables).

The dataset can be ultimately be used to generate a machine learning model that identifies in near real-time whether or not a particular window in my home is open based on temperature values from three different sensors. The entire workflow from data ingestion to continually deployed ML predictions are achieved with a fully-managed, auto-scaling, and serverless architecture.

Content

Present in this dataset are two files:

1. temperature.csv. This contains 10.5M rows of raw temperature sensor data streaming up from three different sensors. Sensor 258* and 270* are each positioned close to their own window, while sensor 275* is about 8 feet away from a third window. The column names are largely self-explanatory: timestamp_utc,timestamp_epoch,temp_f,temp_c,device_id
2. window_opened_closed.csv. This defines the time frames when a particular window was opened. All time points from the raw dataset where a window was not open should be assumed to have all windows closed. The column names are largely self-explanatory: DayPST,StartTimePST,EndTimePST,ObjectCode,ObjectName

All GitHub gists showcasing how to manipulate and make use of these files are present in the Medium.com articles:

Part 1 (IoT device setup and streaming into IoT Core / PubSub): https://blog.doit-intl.com/production-scale-iot-best-practices-implementation-with-gcp-part-1-3-44e2fa0e6554

Part 2 (Shuttling IoT data from PubSub into BigQuery via Dataflow): https://blog.doit-intl.com/production-scale-iot-best-practices-implementation-with-gcp-part-2-3-4a9e59d51214

Part 3 (BigQuery ML model training, deployment, and near real-time predictions): https://blog.doit-intl.com/production-scale-iot-best-practices-implementation-with-google-cloud-part-3-3-7f2fa99f6785

Acknowledgements

This dataset and the corresponding articles were produced by Matthew Porter, a Senior Cloud Architect at DoiT International.

Inspiration

My beautiful nine week old Corgi puppy Maple, whose as-of-yet lack of potty training has led to many air-refreshing opened windows in the heart of winter!

Context

To aid researchers, data scientists, and analysts in the effort to combat COVID-19, Google is making a hosted repository of public datasets including OpenStreetMap data, free to access. To facilitate the Kaggle community to access the BigQuery dataset, it is onboarded to Kaggle platform which allows querying it without a linked GCP account. Please note that due to the large size of the dataset, Kaggle applies a quota of 5 TB of data scanned per user per 30-days.

Description

This data from USAFacts provides US COVID-19 case and death counts by state and county. This data is sourced from the CDC, and state and local health agencies.

For more information, see the USAFacts site on the Coronavirus. Interactive data visualizations are also available via USAFacts.

Bitcoin is a crypto currency leveraging blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. To learn more, read the Bitcoin Wiki . This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program. The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. To further interoperate with Ethereum and ERC-20 token transactions, we also created some views that abstract the blockchain ledger to be presented as a double-entry accounting ledger. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out our blog post on the Google Cloud Big Data Blog and try the sample query below to get started. 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. What is BigQuery .

Ethereum is a crypto currency which leverages blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing "tree" of blocks, where each block contains a number of transactions. To learn more, read the "Ethereum in BigQuery: a Public Dataset for smart contract analytics" blog post by Google Developer Advocate Allen Day. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. 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. What is BigQuery .

Context :

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole. Update frequency: Historic (none)

Dataset source

United States Census Bureau

Sample Query

SELECT zipcode, population FROM bigquery-public-data.census_bureau_usa.population_by_zip_2010 WHERE gender = '' ORDER BY population DESC LIMIT 10

Terms of use

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/us-census-data

Dogecoin is an open source peer-to-peer digital currency, favored by Shiba Inus worldwide. It is qualitatively more fun while being technically nearly identical to its close relative Bitcoin. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. 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. What is BigQuery .

Context

Using Ethereum public data to analyze for trading signal becomes a trend. Google BigQuery is way too costly. This forever free public dataset is created and updated for public to avoid the over charge by GCP.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Weather Source, a leading provider of weather and climate technologies for business intelligence, is offering complimentary data for those researching the potential connections between weather and COVID-19 viability and transmission. This share includes: Global historical weather data dating back to October 2019 Present data Forecast data out to 15 days The data supports temperature and humidity, both specific and relative, at the daily level. 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. What is BigQuery . This dataset is created and owned by Weather Source and made available for educational and academic research purposes. This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate.

This dataset contains information on how much money is spent by verified advertisers on political advertising across Google Ad Services. In addition, insights on demographic targeting used in political ad campaigns by these advertisers are also provided. Finally, links to the actual political ad in the Google Transparency Report (https://adstransparency.google.com) are provided. Data for an election expires 7 years after the election. After this point, the data are removed from the dataset and are no longer available.

Update frequency: Daily

Dataset source: Transparency Report: Political Advertising on Google

Terms of use:

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/transparency-report/google-political-ads

For more information see: The Political Advertising on Google Transparency Report at https://adstransparency.google.com

The supporting Frequently Asked Questions at https://support.google.com/transparencyreport/answer/9575640?hl=en&ref_topic=7295796

NOTICE: NEW GOES-19 Data!!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete. NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer be available. Operational GOES-West products can be found in the GOES-18 bucket. The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the next generation of geostationary weather satellites. The GOES-R series will significantly improve the detection and observation of environmental phenomena that directly affect public safety, protection of property and our nation’s economic health and prosperity. GOES satellites (GOES-16, GOES-17, GOES-18, and GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS. GOES data can be found in the GCS buckets: gs://gcp-public-data-goes-16 gs://gcp-public-data-goes-18 gs://gcp-public-data-goes-19 Pub/Sub topics you can subscribe to for updates: projects/gcp-public-data---goes-16/topics/gcp-public-data-goes-16 projects/gcs-public-datasets/topics/gcp-public-data-goes-18 projects/noaa-public/topics/gcp-public-data-goes-19 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. What is BigQuery .

BREATHE is a large-scale biomedical database containing entries from 10 major repositories of biomedical research. Our dataset contains both abstract and full body texts of biomedical papers going back for decades and contains more than 16 million unique papers. This dataset can be used to train language models to better understand outcomes from biomedical research and uncover insights to combat the COVID-19 pandemic. This dataset is also available for access in Google Cloud Storage. 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. What is BigQuery .

Bitcoin Cash is a cryptocurrency that allows more bytes to be included in each block relative to it’s common ancestor Bitcoin. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. 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. What is BigQuery .

The ISB Cancer Gateway in the Cloud (ISB-CGC) is one of three National Cancer Institute (NCI) Cloud Resources tasked with bringing cancer data and computation power together through cloud platforms. It is a collaboration between the Institute for Systems Biology (ISB) and General Dynamics Information Technology Inc. (GDIT). Since starting in 2014 as part of NCI’s Cloud Pilot Resource initiative, ISB-CGC has provided access to increasing amounts of cancer data in the cloud. In Google BigQuery, ISB-CGC stores high-level clinical, biospecimen, genomic and proteomic cancer research data obtained from the NCI Genomic Data Commons (GDC) and Proteomics Data Commons (PDC). It also stores a large amount of metadata about files that are stored in the GDC Google Cloud Storage, as well as genome reference sources (e.g. GENCODE, miRBase, etc.). The majority of these datasets and tables are completely open access and available to the research community. ISB-CGC has consolidated the data by research program and data type (ex. Clinical, DNA Methylation, RNAseq, Somatic Mutation, etc.) and transformed it into ISB-CGC Google BigQuery tables for ease of access and analysis. This novel approach allows users to quickly analyze information from thousands of patients. The ISB-CGC BigQuery Table Search UI is a discovery tool that allows users to explore and search for ISB-CGC hosted BigQuery tables. 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. What is BigQuery

These are the full-resolution boundary zip code tabular areas (ZCTA), derived from the US Census Bureau's TIGER/Line Shapefiles. The dataset contains polygons that roughly approximate each of the USPS 5-digit zip codes. It is one of many geography datasets available in BigQuery through the Google Cloud Public Dataset Program to support geospatial analysis. You can find more information on the other datasets at the US Geographic Boundaries Marketplace page . Though they do not continuously cover all land and water areas in the United States, ZCTAs are a great way to visualize geospatial data in an understandable format with excellent spatial resolution. This dataset gives the area of land and water within each zip code, as well as the corresponding city and state for each zip code. This makes the dataset an excellent candidate for JOINs to support geospatial queries with BigQuery’s GIS capabilities. Note: BQ-GIS is in public beta, so your GCP project will need to be whitelisted to try out these queries. You can sign up to request access here . 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. What is BigQuery .