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.

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.

See https://databases.pacha.dev

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 maintenance for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best available 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

The code is about how to extract data from the MIMIC-III. (7Z)

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

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.

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.

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.

The layers in this service reference the Beach Profiling data in the MGS_Data SQL Server database. They are provided here for the use in the Maine Beach Profiling Hub Initiative tools including Survey123, web maps, and summary tables.

Dataset

This dataset was created by dkom

Released under Other (specified in description)

Contents

Manual review not performed.

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.

NSText2SQL dataset used to train NSQL models. The data is curated from more than 20 different public sources across the web with permissable licenses (listed below). All of these datasets come with existing text-to-SQL pairs. We apply various data cleaning and pre-processing techniques including table schema augmentation, SQL cleaning, and instruction generation using existing LLMs. The resulting dataset contains around 290,000 samples of text-to-SQL pairs.

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

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

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.

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