Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.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 Google Analytics 4 is Google analytics latest service that enables you to measure traffic and engagement across your website as well as app.

Content This is a sample dataset of GA4 for Google Merchendise store for the month of Jan 2021.

Acknowledgements This information is exported from the google bigquery public data set.

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:ga4_obfuscated_sample_ecommerce.events_*

Inspiration To study google analytics it is really very difficult to get sample data so here's making it easy for some in need of one.

Aptos is a Layer 1 blockchain that prioritizes scalability, security, and fast transaction speeds. Aptos utilizes a unique smart contract programming language called Move. Move was originally designed by Meta (formerly Facebook) for their Diem blockchain project and focuses on resource safety and verification. Data freshness can range between minutes to hours depending on chain activity and transaction volumes. Questions? Please reach out to cloud-blockchain-analytics-help@google.com

Dataset

This dataset was created by Stephen Rokitka

Released under Other (specified in description)

Contents

Sui is a Layer 1 blockchain which aims to overcome blockchain limitations of slow speeds, high costs, and complex onboarding to make Web3 accessible and efficient for a wide range of users. Sui is built by Mysten labs, a blockchain infrastructure company founded by four ex-Meta engineers who worked on the Diem blockchain project. Sui leverages the Move programming language for smart contract development, offering resource safety and formal verification for secure development. Data freshness can range between minutes to hours depending on chain activity and transaction volumes. Questions? Please reach out to cloud-blockchain-analytics-help@google.com

Context

Bitcoin and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Like its predecessor, Bitcoin, the Ethereum blockchain can be described as an immutable distributed ledger. However, creator Vitalik Buterin also extended the set of capabilities by including a virtual machine that can execute arbitrary code stored on the blockchain as smart contracts.

Both Bitcoin and Ethereum are essentially OLTP databases, and provide little in the way of OLAP (analytics) functionality. However the Ethereum dataset is notably distinct from the Bitcoin dataset:

• The Ethereum blockchain has as its primary unit of value Ether, while the Bitcoin blockchain has Bitcoin. However, the majority of value transfer on the Ethereum blockchain is composed of so-called tokens. Tokens are created and managed by smart contracts.

• Ether value transfers are precise and direct, resembling accounting ledger debits and credits. This is in contrast to the Bitcoin value transfer mechanism, for which it can be difficult to determine the balance of a given wallet address.

• Addresses can be not only wallets that hold balances, but can also contain smart contract bytecode that allows the programmatic creation of agreements and automatic triggering of their execution. An aggregate of coordinated smart contracts could be used to build a decentralized autonomous organization.

Content

The Ethereum blockchain data are now available for exploration with BigQuery. All historical data are in the ethereum_blockchain dataset, which updates daily.

Our hope is that by making the data on public blockchain systems more readily available it promotes technological innovation and increases societal benefits.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_ethereum.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Cover photo by Thought Catalog on Unsplash

Inspiration

• What are the most popularly exchanged digital tokens, represented by ERC-721 and ERC-20 smart contracts?
• Compare transaction volume and transaction networks over time
• Compare transaction volume to historical prices by joining with other available data sources like Bitcoin Historical Data

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

Context

This curated dataset consists of 269,353 patent documents (published patent applications and granted patents) spanning the 1976 to 2016 period and is intended to help identify promising R&D on the horizon in diagnostics, therapeutics, data analytics, and model biological systems.

Content

USPTO Cancer Moonshot Patent Data was generated using USPTO examiner tools to execute a series of queries designed to identify cancer-specific patents and patent applications. This includes drugs, diagnostics, cell lines, mouse models, radiation-based devices, surgical devices, image analytics, data analytics, and genomic-based inventions.

Acknowledgements

“USPTO Cancer Moonshot Patent Data” by the USPTO, for public use. Frumkin, Jesse and Myers, Amanda F., Cancer Moonshot Patent Data (August, 2016).

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

Banner photo by Jaron Nix on Unsplash

The Analytical Data Store Tools market is experiencing robust growth, driven by the increasing need for real-time insights and advanced analytics across diverse industries. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors: the proliferation of big data, the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, and the growing demand for improved decision-making capabilities across organizations. Key trends include the increasing integration of AI and machine learning into analytical data store tools, the emergence of serverless architectures, and a focus on enhanced data security and governance. While the market faces challenges like data integration complexities and the need for skilled professionals, the overall outlook remains positive, driven by continued innovation and expanding enterprise adoption. The competitive landscape is highly dynamic, with major players like Google, Snowflake, Microsoft, Amazon, and Oracle leading the charge. These established players are constantly innovating and expanding their offerings to meet evolving customer needs, while smaller, specialized companies are emerging to cater to niche requirements. The market's segmentation reflects this diversity, with solutions catering to various data volumes, industry verticals, and deployment models (cloud, on-premise, hybrid). Geographical expansion, particularly in rapidly developing economies, presents a significant opportunity for growth. The historical period (2019-2024) likely saw a slower growth rate than the projected future growth, reflecting the time taken for market maturity and broader adoption of cloud technologies. The continued focus on data-driven decision-making across industries ensures the sustained growth trajectory of the Analytical Data Store Tools market.

The cloud data warehouse market is experiencing robust growth, driven by the increasing need for scalable, cost-effective, and readily accessible data analytics solutions. The market's expansion is fueled by several key factors, including the burgeoning adoption of cloud computing across various industries, the proliferation of big data, and the growing demand for real-time business intelligence. Organizations are migrating from on-premise data warehouses to cloud-based solutions to leverage enhanced scalability, reduced infrastructure costs, and improved agility. This shift is further accelerated by the availability of advanced analytics tools and services within the cloud ecosystem, enabling businesses to derive actionable insights from their data more efficiently. Competitive pressures and the need to gain a competitive edge are also significant drivers, pushing enterprises to adopt sophisticated data warehousing solutions capable of handling complex analytical workloads. The market is highly fragmented, with major players such as Amazon, Google, Microsoft, and others competing intensely through innovation, strategic partnerships, and aggressive pricing strategies. While the market shows significant promise, certain challenges persist. Data security and privacy concerns remain a major obstacle to wider adoption, particularly in regulated industries. Integration complexities with existing on-premise systems and the need for skilled professionals to manage and maintain cloud data warehouses also present hurdles. However, ongoing technological advancements in areas such as data encryption, access control, and automated data integration are mitigating these challenges. Furthermore, the emergence of new technologies, such as serverless architectures and AI-powered analytics, is continuously reshaping the market landscape, fostering innovation and expanding the market's potential. Over the forecast period (2025-2033), consistent growth is anticipated, fueled by ongoing digital transformation initiatives across various sectors. We estimate a conservative CAGR (considering industry averages for similar tech sectors) of 15% over this period, indicating substantial growth opportunities.

Context

Blockchain technology, first implemented by Satoshi Nakamoto in 2009 as a core component of Bitcoin, is a distributed, public ledger recording transactions. Its usage allows secure peer-to-peer communication by linking blocks containing hash pointers to a previous block, a timestamp, and transaction data. Bitcoin is a decentralized digital currency (cryptocurrency) which leverages the Blockchain to store transactions in a distributed manner in order to mitigate against flaws in the financial industry.

Nearly ten years after its inception, Bitcoin and other cryptocurrencies experienced an explosion in popular awareness. The value of Bitcoin, on the other hand, has experienced more volatility. Meanwhile, as use cases of Bitcoin and Blockchain grow, mature, and expand, hype and controversy have swirled.

Content

In this dataset, you will have access to information about blockchain blocks and transactions. All historical data are in the bigquery-public-data:crypto_bitcoin dataset. It’s updated it every 10 minutes. The data can be joined with historical prices in kernels. See available similar datasets here: https://www.kaggle.com/datasets?search=bitcoin.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_bitcoin.[TABLENAME]. Fork this kernel to get started.

Method & Acknowledgements

Allen Day (Twitter | Medium), Google Cloud Developer Advocate & Colin Bookman, Google Cloud Customer Engineer retrieve data from the Bitcoin network using a custom client available on GitHub that they built with the bitcoinj Java library. Historical data from the origin block to 2018-01-31 were loaded in bulk to two BigQuery tables, blocks_raw and transactions. These tables contain fresh data, as they are now appended when new blocks are broadcast to the Bitcoin network. For additional information visit the Google Cloud Big Data and Machine Learning Blog post "Bitcoin in BigQuery: Blockchain analytics on public data".

Photo by Andre Francois on Unsplash.

Inspiration

• How many bitcoins are sent each day?
• How many addresses receive bitcoin each day?
• Compare transaction volume to historical prices by joining with other available data sources

This dataset surfaces data from the Avalanche blockchain and includes tables for blocks, transactions, logs, and more. Avalanche is a decentralized, open-source proof of stake blockchain with smart contract functionality. AVAX is the native cryptocurrency of the platform. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. For more information, see the Blockchain Analytics documentation . 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 surfaces data from the Polygon blockchain and includes tables for blocks, transactions, logs, and more. Polygon is a Layer 2 decentralized, blockchain-based operating system with smart contract functionality, proof-of-stake principles as its consensus algorithm and a cryptocurrency native to the system, known as MATIC. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. For more information, see the Blockchain Analytics documentation . 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 Data Warehousing market, valued at $31.15 billion in 2025, is projected to experience robust growth, driven by the increasing need for businesses to leverage data for informed decision-making. A Compound Annual Growth Rate (CAGR) of 13.64% from 2025 to 2033 indicates a significant expansion, fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting businesses of all sizes. Furthermore, the growing volume and complexity of data necessitate efficient data warehousing solutions for better analysis and insights. Advancements in big data technologies and analytics further propel market growth, enabling businesses to derive valuable insights from previously untapped data sources. Competitive landscape analysis reveals a mix of established players like Microsoft, Oracle, and IBM, alongside emerging innovative companies focusing on cloud-native solutions and specialized analytics. The market segmentation, categorized by deployment (on-premise, hybrid, cloud-based), reflects the evolving preferences of businesses towards flexible and adaptable data warehousing solutions. Geographic expansion, particularly in rapidly developing economies of Asia-Pacific and parts of the Middle East and Africa, offers significant potential for future growth. However, challenges remain, including data security concerns, integration complexities, and the need for skilled professionals to manage and interpret data effectively. The forecast period (2025-2033) anticipates a substantial market expansion, with the cloud-based segment likely dominating due to its inherent scalability and cost advantages. North America is expected to maintain a substantial market share, driven by technological advancements and early adoption, but regions like Asia-Pacific and Europe are expected to witness faster growth due to increasing digital transformation initiatives and government investments in data infrastructure. The competitive landscape remains dynamic, with companies continually innovating and expanding their product offerings to meet evolving business needs and cater to specific industry verticals. Future growth hinges on addressing data security challenges, simplifying data integration processes, and fostering a skilled workforce capable of utilizing the full potential of data warehousing technologies.

The Big Data Processing and Distribution Systems market is experiencing robust growth, driven by the exponential increase in data volume across various industries. The market, estimated at $50 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions, offering scalability and cost-effectiveness, is a significant driver. Furthermore, the increasing demand for real-time analytics and advanced data processing capabilities across sectors like finance, healthcare, and e-commerce are propelling market growth. The emergence of new technologies such as edge computing and AI-powered analytics is further accelerating the adoption of sophisticated big data processing solutions. However, market growth is not without its challenges. Data security and privacy concerns, coupled with the complexity of implementing and managing big data systems, remain significant restraints. The need for specialized skills and expertise in data science and engineering also contributes to the overall cost and complexity of adoption. Despite these challenges, the market's continued expansion is anticipated, driven by the persistent need for efficient and insightful data management in an increasingly data-driven world. Segmentation within the market is diverse, encompassing various solutions including cloud-based platforms, on-premise systems, and specialized tools for data integration, processing, and visualization. Leading players such as Google, AWS, Microsoft, Snowflake, and Databricks are fiercely competing to capture market share, further stimulating innovation and driving market expansion.

The global Financial Cloud Data Warehouse Solutions market is experiencing robust growth, driven by the increasing need for real-time data analytics and improved decision-making within the financial sector. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors: the rising adoption of cloud computing for enhanced scalability and cost-efficiency, the increasing volume and complexity of financial data requiring advanced analytical capabilities, and stringent regulatory compliance demands necessitating robust data management solutions. The banking, insurance, and securities sectors are major contributors to this growth, actively seeking solutions to improve customer experience, optimize risk management, and enhance fraud detection capabilities. Key players like Amazon Web Services, Snowflake, and Microsoft Azure are leading the charge, offering comprehensive data warehouse platforms and tools catering to the specific needs of the financial industry. The market segmentation reveals a strong preference for cloud-based Data Warehouse Platforms, reflecting a broader industry shift towards cloud-native architectures. However, the demand for data warehouse tools and related services remains substantial, indicating a diverse range of needs across different financial institutions. Geographical analysis reveals a strong presence in North America and Europe, driven by early adoption and mature technological infrastructure. However, significant growth potential exists in Asia-Pacific, particularly in rapidly developing economies like China and India, as these regions increasingly embrace cloud technologies and data-driven decision-making practices. While challenges remain, such as data security concerns and the need for skilled professionals, the overall market outlook remains extremely positive, signifying a continuous expansion in the coming years.

This dataset surfaces data from the Ethereum blockchain and includes tables for blocks, transactions, logs, and more. Ethereum is a decentralized open-source blockchain system that features its own cryptocurrency, Ether. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. For more information, see the Blockchain Analytics documentation .

The size of the Cloud Analytics Market was valued at USD 23.82 billion in 2023 and is projected to reach USD 82.22 billion by 2032, with an expected CAGR of 19.36% during the forecast period. The cloud analytics market has witnessed significant growth, driven by the rising demand for data-driven decision-making and the increasing adoption of cloud computing technologies. Organizations are leveraging cloud analytics to process and analyze vast amounts of structured and unstructured data, enabling them to gain actionable insights and improve operational efficiency. The market's expansion is fueled by the scalability, cost-effectiveness, and real-time capabilities of cloud-based solutions compared to traditional on-premises systems. Industries such as retail, healthcare, banking, and IT are increasingly integrating cloud analytics into their operations to enhance customer experiences, optimize supply chains, and mitigate risks. Furthermore, advancements in artificial intelligence and machine learning are augmenting the analytical capabilities of cloud platforms, allowing businesses to forecast trends and automate complex processes. The growing popularity of hybrid and multi-cloud environments is also contributing to the market's growth by offering flexibility and addressing data security concerns. As organizations continue to prioritize digital transformation and data utilization, the cloud analytics market is poised for sustained expansion, driven by technological innovations and the increasing importance of real-time data insights. Recent developments include: July 2020: Google LLC, a technology company, launched BigQuery Omni, a multi-cloud analytics solution that enables enterprises to access and securely analyze the data across Amazon Web Services, Google Cloud, and Microsoft Azure., September 2020: TIBCO Software Inc., a leading enterprise data solution providing company TIBCO Hyperconverged Analytics. The Hyperconverged Analytics solution and services the company offers aid in combining data science, visual analytics, and streaming analytics to provide companies with expanded analytical strategies.. Key drivers for this market are: Increasing data volumes and the need for insights Growing adoption of cloud computing platforms Advances in AI and machine learning Demand for real-time analytics Enhanced data security and compliance requirements. Potential restraints include: Data privacy and security concerns Data integration complexities Lack of skilled professionals High implementation and maintenance costs Data center outages and downtime. Notable trends are: Hybrid cloud analytics models Predictive maintenance and prescriptive analytics Edge analytics and IoT integration Advanced data visualization techniques.

My case study for the Google analytics certificate. The data came from publicly available FCC and google data found on BigQuery. I have put each query needed to generate these tables in the about this file of each table. I am looking for interesting trends between the behavior of political ad spend with traditional media (radio and TV) vs internet (google Adspend only).

This dataset surfaces data from the Tron blockchain and includes tables for blocks, transactions, logs, and more. TRON is a decentralized, blockchain-based operating system with smart contract functionality, proof-of-stake principles as its consensus algorithm and a cryptocurrency native to the system, known as TRX. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. For more information, see the Blockchain Analytics documentation . 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 .