Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.

I’ve decided to host different light/medium size by using PostgreSQL, MySQL and SQL Server backends (in strict descending order of preference!).

Why 3 database backends? I think there are a ton of small edge cases when moving between DB back ends and so testing lots with live databases is quite valuable. With this resource you can benchmark speed, compression, and DDL types.

Please send me a tweet if you need the connection strings for your lectures or workshops. My Twitter username is @pachamaltese. See the SQL dumps on each section to have the data locally.

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

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenace for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best avaialble data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats. Additional metadata resouce: https://geoportalprod-ordot.msappproxy.net/geoportal/catalog/main/home.page

Chi squared test, not including missing data for each variable other than NHS number*At least one social risk factor including drug use, homelessness, alcohol misuse/ abuse, prisonDescriptive analysis of case notifications dataset for records with and without an NHS number.

The Database as a Service (DaaS) platform market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for real-time data processing. Let's assume, for illustrative purposes, a 2025 market size of $50 billion with a Compound Annual Growth Rate (CAGR) of 15% for the forecast period of 2025-2033. This implies significant expansion, reaching an estimated market value exceeding $150 billion by 2033. This growth is fueled by several key trends including the proliferation of big data analytics, the expanding adoption of serverless architectures, and the growing preference for managed services that reduce operational overhead for businesses. Major players like AWS, Microsoft Azure, Google Cloud Platform, and others are heavily investing in enhancing their DaaS offerings, fostering competition and innovation. However, challenges remain, including security concerns related to data stored in the cloud, vendor lock-in, and the complexity of migrating existing databases to a DaaS environment. The competitive landscape is intensely dynamic, with established tech giants alongside specialized DaaS providers vying for market share. The segmentation of the market is likely based on deployment model (public, private, hybrid), database type (SQL, NoSQL), and industry vertical. Future growth will be influenced by factors such as advancements in database technologies (e.g., graph databases, in-memory databases), increasing adoption of artificial intelligence and machine learning for database management, and the growing demand for data sovereignty and compliance solutions. The market's continued expansion is assured, but the precise trajectory will depend on the evolution of cloud technologies, regulatory changes, and the ability of providers to address security and scalability challenges effectively. This robust growth presents significant opportunities for both established and emerging players within the DaaS landscape.

The Database Platform as a Service (DBPaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for data analytics. The market's expansion is fueled by businesses migrating legacy on-premise databases to cloud-based alternatives, seeking enhanced agility, and leveraging the advantages of pay-as-you-go models. Major players like Amazon Web Services, Microsoft Azure, and Google Cloud Platform dominate the market, offering a wide range of DBPaaS options catering to diverse needs, from relational databases to NoSQL solutions. The market is segmented by deployment model (public cloud, private cloud, hybrid cloud), database type (SQL, NoSQL, NewSQL), and industry vertical (BFSI, healthcare, retail, etc.). Competition is fierce, with established players constantly innovating and new entrants emerging to challenge the status quo. Factors like data security concerns and integration complexities pose some challenges to market growth. However, advancements in serverless computing and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are expected to drive further expansion. The forecast period (2025-2033) is projected to witness substantial growth, driven by ongoing digital transformation initiatives across various industries. The increasing adoption of cloud-native applications and microservices architectures further necessitates robust and scalable DBPaaS solutions. While the initial investment in migrating to the cloud can be significant, the long-term cost savings and improved efficiency make DBPaaS an attractive option. The market's growth is expected to be particularly strong in regions with high cloud adoption rates and robust digital infrastructure. The competitive landscape will likely remain dynamic, with mergers and acquisitions, strategic partnerships, and continuous product innovation shaping the market's trajectory. Overall, the DBPaaS market is poised for substantial growth, driven by a confluence of technological advancements and evolving business needs. Assuming a conservative CAGR of 20% (a reasonable estimate considering the high growth sectors involved), and a 2025 market size of $50 Billion, we can project substantial future growth.

E.g. house number and street name*E.g. city.Description of missing data on variables used for the linkage from the laboratory, case notifications and an example pre-entry screening dataset, by NHS number availability and validity.

Global In-memory database market is expected to revenue of around USD 36.21 billion by 2032, growing at a CAGR of 19.2% between 2024 and 2032.

Managed Database Service Market is Segmented by Service (Data Administration, Backup and Recovery, and More), Deployment Model (Public Cloud, Private Cloud, Hybrid/Multi-cloud), Database Type (Relational SQL, Nosql, and More), Application (CRM, ERP, SCM, and More), Industry Vertical (BFSI, Healthcare, and More), Organisation Size (Large Enterprises, Smes), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

The AdventureWorks DW 2008 dataset, originally provided by Microsoft, has been converted into CSV files for easier use, making it accessible for data exploration on platforms like Kaggle. The dataset is licensed under the Microsoft Public License (MS-PL), which is a permissive open-source license. This means you are free to use, modify, and share the dataset, whether for personal or commercial purposes, provided that you include the original license terms. However, it's important to note that the dataset is provided "as-is" without any warranty or guarantee from Microsoft.

I really enjoy working with the AdventureWorks DW 2008 dataset. It offers a rich and well-structured environment that's perfect for writing and learning SQL queries. The data warehouse includes a variety of tables, such as facts and dimensions, making it an excellent resource for both beginners and experienced SQL users to practice querying and exploring relational databases.

Now, with the dataset available in CSV format, it can be easily used with Python for exploratory data analysis (EDA), and it’s also well-suited for applying machine learning techniques such as regression, classification, and clustering.

If you’re planning to dive into the data, all the best! It's a fantastic resource to learn from and experiment with. Cheers!

Calculation of sensitivity and specificity for probabilistic matching without manual review, not including address variables and using an ETS dataset that only including non-UK born individuals.

The Open Trade Statistics initiative was developed to ease access to international trade data by providing downloadable SQL database dumps, a public API, a dashboard, and an R package for data retrieval. This project was born out of the recognition that many academic institutions in Latin America lack access to academic subscriptions and comprehensive datasets like the United Nations Commodity Trade Statistics Database. The OTS project not only offers a solution to this problem regarding international trade data but also emphasizes the importance of reproducibility in data processing. Through the use of open-source tools, the project ensures that its datasets are accessible and easy to use for research and analysis.

OTS, based on the official correlation tables, provides a harmonized dataset where the values are converted to HS revision 2012 for the years 1980-2021 and it involved transforming some of the reported data to find equivalent codes between the different classifications. For instance, the HS revision 1992 code '271011' (aviation spirit) does not have a direct equivalent in HS revision 2012 and it can be converted to the more general code '271000' (oils petroleum, bituminous, distillates, except crude). The same process was applied to the SITC codes.

Country codes are also standardized in OTS. For instance, missing ISO-3 country codes in the raw data were replaced by the values expressed in UN COMTRADE documentation. For instance, the numeric code '490' corresponds to 'e-490' but it appears as a blank value in the raw data, and UN COMTRADE documentation
indicates that 'e-490' corresponds to 'Other Asia, Not Elsewhere Specified (NES)'.

Commercial purposes are strictly out of the boundaries of what you can do with this data according to UN Comtrade dissemination clauses.

Visit tradestatistics.io to access the dashboard and R package for data retrieval.

Combining human expertise with information from book-consumer digital data may generate what it takes to face the following changes in such a critical market. Along with the publishing industry, researchers rely on book-related data to develop tools and applications, drawing constructive conclusions to make better informed and faster decisions. Such solutions range from best-selling prediction models to natural language processing to classify raw text. Besides require complex Artificial Intelligence (AI) methods, all of them are essentially data-dependent, mainly book-related data-dependent.

Data, and more specifically data growth, is essential for developing and performing such AI-powered applications. None of these efforts can be achieved without a preliminary collection of data on literary works, readers, and their reading habits. Therefore, it is critically important to build and make available datasets that fully comprise the essential elements of the book industry ecosystem. Although some efforts have been made for English language books, little has been done regarding other lesser-spoken languages, such as Portuguese. The evaluation of specific data is of fundamental importance for literature analysis, as Portuguese has its own literary peculiarities. Hence, we present PPORTAL, a Public domain PORTuguese-lAnguage Literature dataset. PPORTAL's contributions are summarized as follows:

• Data integration of numerous public domain works from three digital libraries;
• Enriched metadata for works, authors and online reviews extracted from Goodreads;
• Feature engineering on the metadata to create meaningful additional features; and
• Unrestricted access in two formats (SQL database and compressed .csv files

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

The size of the Relational Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 45481.69 million by 2032, with an expected CAGR of 12.50% during the forecast period. This growth trajectory is primarily driven by the advent of hybrid seeds, which offer superior yield and improved disease resistance. Government initiatives aimed at promoting food security and the adoption of advanced technologies further fuel market expansion. Key applications for hybrid seeds encompass field crops, horticulture, and fodder crops. Leading players in the market include Monsanto, DuPont Pioneer, Syngenta, and Bayer CropScience. Recent developments include: October 2022: Oracle released latest advancements in database technology with the announcement of Oracle Database 23c Beta. It accommodates diverse data types, workloads, and development styles. The release incorporates numerous innovations across Oracle's database services and product portfolio., October 2023: Microsoft has launched a public preview of a new Azure SQL Database free offering, marking a significant addition to its cloud services. Users can access a 32 GB general purpose, serverless Azure SQL database with 100,000 vCore seconds of compute free monthly..

This feature layer displays all project locations as tracked by engineering staff at Leon County Public Works. This feature layer is a subset (view) of a parent hosted feature layer that is updated twice daily from a cloud (Azure) hosted SQL database that is administered by Leon County Applications team and maintained by engineering staff at Leon County Public Works.

Relational Database as a Service Market Outlook

According to our latest research, the global Relational Database as a Service (DBaaS) market size reached USD 22.4 billion in 2024, reflecting robust adoption across multiple industries. The market is projected to expand at a CAGR of 15.7% from 2025 to 2033, reaching an estimated USD 67.7 billion by 2033. This significant growth trajectory is driven by the increasing need for scalable, cost-effective, and high-performance database solutions, particularly as organizations accelerate their digital transformation initiatives and migrate critical workloads to the cloud. As per our latest research, the combination of rapid cloud adoption, surging data volumes, and the demand for flexible data management models are the primary growth engines for the global relational DBaaS market.

One of the most compelling growth factors for the relational DBaaS market is the exponential rise in data generation across enterprises of all sizes. As organizations increasingly rely on data-driven insights to inform business strategies, the need for robust, secure, and easily accessible databases has become paramount. Relational DBaaS platforms offer automated maintenance, backup, and scaling capabilities, greatly reducing the administrative burden on IT teams. This automation not only enhances operational efficiency but also allows businesses to focus on innovation and core competencies, rather than infrastructure management. The proliferation of cloud-native applications and the shift towards microservices architectures further amplify the demand for relational DBaaS, as these platforms provide seamless integration, high availability, and disaster recovery out of the box.

Another major driver fueling the relational DBaaS market is the continuous evolution of cloud computing technologies and the expansion of multi-cloud and hybrid cloud strategies. Enterprises are increasingly adopting flexible deployment models to optimize performance, cost, and compliance. Relational DBaaS solutions, with their inherent flexibility, allow organizations to deploy databases in public, private, or hybrid cloud environments, depending on specific business needs and regulatory requirements. This adaptability is particularly attractive to industries with stringent data governance mandates, such as BFSI and healthcare, where data sovereignty and security are critical. Moreover, the ability to scale resources dynamically in response to fluctuating workloads ensures that organizations can maintain optimal performance while controlling costs, further driving the adoption of DBaaS solutions globally.

Additionally, the growing emphasis on digital transformation across sectors such as retail, manufacturing, and government is catalyzing the adoption of relational DBaaS. As these sectors modernize legacy systems and embrace cloud-first strategies, the demand for managed database services that offer high reliability, security, and compliance is surging. The integration of advanced analytics, artificial intelligence, and machine learning capabilities with relational DBaaS platforms is also enabling organizations to unlock deeper business insights and drive competitive advantage. Furthermore, the rise of remote work and distributed teams has heightened the need for centralized, cloud-based data management solutions, making relational DBaaS an essential component of modern enterprise IT infrastructure.

From a regional perspective, North America continues to dominate the relational DBaaS market, driven by the presence of major cloud service providers, early technology adoption, and significant investments in digital infrastructure. However, the Asia Pacific region is experiencing the fastest growth, fueled by rapid digitalization, expanding cloud adoption, and the proliferation of small and medium enterprises seeking agile and cost-effective database solutions. Europe is also witnessing steady growth, supported by strong regulatory frameworks and a focus on data privacy and security. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, as organizations in these regions increasingly recognize the benefits of cloud-based database services in enhancing business agility and operational efficiency.

Database Type Analysis

The relational DBaaS market is segmented by database type into SQL, NewSQL, and Others, each catering to distinct business needs and use cases. SQL databases, such as MySQL, PostgreSQL,

The LTAR network maintains stations for standard meteorological measurements including, generally, air temperature and humidity, shortwave (solar) irradiance, longwave (thermal) radiation, wind speed and direction, barometric pressure, and precipitation. Many sites also have extensive comparable legacy datasets. The LTAR scientific community decided that these needed to be made available to the public using a single web source in a consistent manner. To that purpose, each site sent data on a regular schedule, as frequently as hourly, to the National Agricultural Library, which has developed a web service to provide the data to the public in tabular or graphical form. This archive of the LTAR legacy database exports contains meteorological data through April 30, 2021. For current meteorological data, visit the GeoEvent Meteorology Resources page, which provides tools and dashboards to view and access data from the 18 LTAR sites across the United States. Resources in this dataset:Resource Title: Meteorological data. File Name: ltar_archive_DB.zipResource Description: This is an export of the meteorological data collected by LTAR sites and ingested by the NAL LTAR application. This export consists of an SQL schema definition file for creating database tables and the data itself. The data is provided in two formats: SQL insert statements (.sql) and CSV files (.csv). Please use the format most convenient for you. Note that the SQL insert statements take much longer to run since each row is an individual insert. Description of zip files The ltararchive*.zip files contain database exports. The schema is a .sql file; the data is exported as both SQL inserts and CSV for convenience. There is a README in markdown and PDF in the zips. Contains the database export of the schema and data for the site, site_station, and met tables as SQL insert statements. ltar_archive_db_sql_export_20201231.zip --> has data until 2020-12-31 ltar_archive_db_sql_export_20210430.zip --> has data until 2021-04-30 Contains the database export of the schema and data for the site, site_station, and met tables as CSV. ltar_archive_db_csv_export_20201231.zip --> has data until 2020-12-31 ltar_archive_db_csv_export_20210430.zip --> has data until 2021-04-30 Contains the raw CSV files that were sent to NAL from the LTAR sites/stations. ltar_rawcsv_archive.zip --> has data until 2021-04-30

SQL Server Performance Monitoring Tools and Software Market Outlook

According to our latest research, the SQL Server Performance Monitoring Tools and Software market size reached USD 1.87 billion in 2024, with a compound annual growth rate (CAGR) of 13.2% projected over the forecast period. By 2033, the market is anticipated to achieve a value of USD 5.73 billion. The primary growth factor driving this market is the increasing demand for real-time database performance optimization and the rapid digital transformation across industries, which is compelling organizations to ensure seamless, reliable, and high-performing SQL Server environments.

One of the most significant growth drivers for the SQL Server Performance Monitoring Tools and Software market is the exponential increase in data volumes and the complexity of enterprise IT infrastructures. As organizations migrate more workloads to SQL Server databases, the need to maintain optimal performance, uptime, and security becomes paramount. This scenario is further complicated by the proliferation of hybrid and multi-cloud environments, which require advanced monitoring solutions that can provide unified visibility across diverse deployments. Enterprises are investing in sophisticated monitoring tools to proactively identify bottlenecks, predict potential failures, and automate performance tuning, all of which contribute to higher operational efficiency and reduced downtime. The growing emphasis on digital transformation and data-driven decision-making ensures that robust performance monitoring remains a top priority for IT leaders globally.

Another key factor propelling the adoption of SQL Server Performance Monitoring Tools and Software is the rise in regulatory compliance and cybersecurity requirements across various industries. Sectors such as BFSI, healthcare, and government are subject to stringent data protection regulations, necessitating continuous monitoring of database activity and performance. Advanced monitoring tools now offer features such as anomaly detection, predictive analytics, and real-time alerting, which help organizations not only optimize performance but also maintain compliance with industry standards like GDPR, HIPAA, and PCI DSS. The integration of artificial intelligence and machine learning into these tools further enhances their capability to detect unusual patterns and mitigate risks proactively, thereby reinforcing the need for comprehensive performance monitoring solutions.

The surge in cloud adoption and the shift towards cloud-native architectures are also significantly impacting the SQL Server Performance Monitoring Tools and Software market. As businesses increasingly deploy SQL Server instances in public, private, or hybrid clouds, they require monitoring tools that are cloud-agnostic and scalable to dynamic workloads. Cloud-based monitoring solutions offer the flexibility, scalability, and cost-effectiveness that modern enterprises demand, enabling them to monitor performance metrics in real-time, regardless of deployment model. This trend is particularly pronounced among small and medium enterprises (SMEs), which benefit from the lower upfront costs and ease of management associated with cloud-based tools. As a result, vendors are intensifying their focus on delivering SaaS-based monitoring platforms with advanced analytics and intuitive dashboards, further accelerating market growth.

Regionally, the SQL Server Performance Monitoring Tools and Software market is witnessing robust growth in North America, driven by early technology adoption, a large base of SQL Server users, and the presence of leading market players. Europe follows closely, with strong demand from sectors such as BFSI, healthcare, and government, while Asia Pacific is emerging as a high-growth region due to rapid digitalization and increasing cloud adoption. Latin America and the Middle East & Africa are gradually catching up, supported by investments in IT infrastructure and the expansion of enterprise applications. As organizations worldwide seek to modernize their database environments and enhance operational resilience, the demand for advanced SQL Server performance monitoring solutions is expected to remain strong throughout the forecast period.

In the realm of database management, "https://growthmarketreports.com/report/storage-monitoring-software-market" target="_blank">Storage Monitoring Software&l