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.

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; 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.

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

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

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.

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.

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.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of 1244.08. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

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..

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.

Stay updated with Market Research Intellect's Database As A Service Market Report, valued at USD 8.5 billion in 2024, projected to reach USD 20.5 billion by 2033 with a CAGR of 10.5% (2026-2033).

Uncover Market Research Intellect's latest Database Platform As A Service Dbpaas Solutions Market Report, valued at USD 10.5 billion in 2024, expected to rise to USD 27.8 billion by 2033 at a CAGR of 14.5% from 2026 to 2033.

The Cloud Database MySQL market is experiencing robust growth, driven by the increasing adoption of cloud computing and the inherent scalability and cost-effectiveness of MySQL. The market's substantial size, estimated at $15 billion in 2025, reflects a significant shift towards cloud-based database solutions. This preference is fueled by factors such as reduced infrastructure costs, enhanced agility, and improved data accessibility. Key market drivers include the expanding need for robust and scalable database solutions for applications ranging from e-commerce to enterprise resource planning (ERP). Furthermore, the rising demand for data analytics and business intelligence solutions is further propelling market expansion. The competitive landscape is intensely populated by major players including Microsoft, Amazon Web Services (AWS), Google Cloud, Oracle, and Alibaba Cloud, leading to innovation and a diverse range of offerings. These companies continuously enhance their services with improved performance, security features, and managed services options, catering to a broader customer base. Trends such as serverless databases, the increasing adoption of containerization technologies (like Docker and Kubernetes), and the growth of hybrid cloud deployments are reshaping the market landscape. However, challenges like data security concerns and complexities associated with cloud migration may act as restraints on market growth, though these are being addressed through advanced security measures and streamlined migration processes. Looking ahead, the Cloud Database MySQL market is poised for sustained growth, with a projected Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033. This growth trajectory is underpinned by the continuing digital transformation across industries and the expanding global adoption of cloud technologies. Segmentation within the market is likely based on deployment model (public, private, hybrid), pricing models, and industry verticals. The substantial market size, coupled with a healthy CAGR, positions Cloud Database MySQL as a highly attractive and strategically important segment within the broader cloud computing market. The continued innovation and competition among major vendors ensures that the market remains dynamic and responsive to evolving user needs.

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 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.

Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py (the "production" branch is the one used for Open Context's primary public deployment).

We also provide a Docker based approach for installing Open Context via this code repository: https://github.com/opencontext/oc-docker (the "production" branch installs the branch of code used for Open Context's primary public deployment).

The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).

This database dump includes all published structured data organized used by Open Context (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.

These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included. The data are provided in a plain text SQL dump (for restoration into a version 14+ PostgreSQL database) and in the non-proprietary (but binary) parquet file format.

IMPORTANT

This database dump contains data from roughly 190+ different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.

This feature layer displays historical 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.