As of June 2024, the most popular open-source database management system (DBMS) in the world was MySQL, with a ranking score of 1061. Oracle was the most popular commercial DBMS at that time, with a ranking score of 1244.

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.

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.

*Sequence features that are multimerization interfaces were calculated in Molmol based on residues that were less than 3.25 Å away from at least one residue in another subunit [63].

The MySQL Training Services market is experiencing robust growth, driven by the increasing adoption of MySQL databases across various industries and the growing demand for skilled MySQL professionals. The market, estimated at $1.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $4.2 billion by 2033. This growth is fueled by several key factors. The rise of big data and cloud computing necessitates proficient database administrators and developers, significantly boosting the need for comprehensive MySQL training. Furthermore, the prevalence of open-source databases like MySQL in both large enterprises and SMEs is creating a large talent pool requiring continuous upskilling and reskilling. The market is segmented by application (Large Enterprises and SMEs) and type (Community and Enterprise Editions), with the enterprise segment dominating due to higher budgets for training and development. Geographical distribution shows strong growth across North America and Europe, reflecting the high concentration of technology companies and advanced digital infrastructure. However, the Asia-Pacific region is emerging as a significant growth driver, fueled by rapid technological adoption and a large, burgeoning IT workforce. Competitive forces are intense, with established players like Oracle and Udemy competing with specialized training providers like Pluralsight and Simplilearn. The market's future success hinges on providers' ability to adapt to evolving technologies within the MySQL ecosystem, offer flexible learning formats (online, in-person, blended), and cater to the specific needs of diverse learner segments. The restraints on market growth include the availability of free online resources, potentially reducing the demand for paid training. However, the complexity of advanced MySQL functionalities and the need for certified professionals will continue to drive demand for structured, quality training programs. Furthermore, the continuous evolution of the MySQL database and related technologies necessitates ongoing investment in training and development initiatives. The market is expected to see increased consolidation as larger players acquire smaller training firms to expand their reach and service offerings. The focus on providing specialized training programs tailored to specific industry needs, coupled with the integration of cloud-based learning platforms, will be crucial for sustained growth. Successful players will be those that successfully differentiate themselves through high-quality content, expert instructors, and engaging learning experiences.

Traffic analytics, rankings, and competitive metrics for mysql.com as of May 2025

Dataset Description: Clean and Ready for Relational Database Import This dataset is a comprehensive collection of well-structured and meticulously cleaned data, meticulously prepared for seamless integration into a relational database. The dataset has undergone thorough data cleansing procedures to ensure that it is free from inconsistencies, missing values, and duplicate records. This guarantees a smooth and efficient data analysis experience for users, without the need for additional preprocessing steps.

Auto-generated structured data of MySQL from table Fields

Quantitative data on product chemical composition is a necessary parameter for characterizing near-field exposure. This data set comprises reported and predicted information on >75,000 chemicals contained in >15,000 consumer products. The data’s primary intended use is for exposure, risk, and safety assessments. The data set includes specific products with quantitative or qualitative ingredient information, which has been publicly disclosed through material safety data sheets (MSDS) and ingredient lists. A single product category from a refined and harmonized set of categories has been assigned to each product. The data set also contains information on the functional role of chemicals in products, which can inform predictions of the concentrations in which they occur. These data will be useful to exposure and risk assessors evaluating chemical and product safety.

The data set presented here is in the form of a MySQL relational database, which mimics CPDat data available under the ‘Exposure’ tab of the CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard) as of August 2017.

The Maven Dependency Dataset contains the data as described in the paper "Mining Metrics, Changes and Dependencies from the Maven Dependency Dataset". NOTE: See the README.TXT file for more information on the data in this dataset. The dataset consists of multiple parts: A snapshot of the Maven repository dated July 30, 2011 (maven.tar.gz), a MySQL database (complete.tar.gz) containing information on individual methods, classes and packages of different library versions, a Berkeley DB database (berkeley.tar.gz) containing metrics on all methods, classes and packages in the repository, a Neo4j graph database (graphdb.tar.gz) containing a call graph of the entire repository, scripts and analysis files (scriptsAndData.tar.gz), Source code and a binary package of the analysis software (fullmaven.jar and fullmaven-sources.jar), and text dumps of data in these databases (graphdump.tar.gz, processed.tar.gz, calls.tar.gz and units.tar.gz).

The Cloud Database MySQL market is experiencing notable growth as businesses increasingly turn to cloud solutions for their database management needs. MySQL, an open-source relational database management system, is widely adopted across various sectors-from e-commerce and finance to healthcare and education-due to i

MySQL Training Service Market Outlook

The global MySQL Training Service market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.2% during the forecast period. The substantial growth in this market can be attributed to the increasing demand for database management skills across various industries. As organizations increasingly rely on data-driven decision-making, the need for skilled professionals who can handle and manipulate MySQL databases has become more critical, driving the demand for specialized training services.

One of the primary growth factors for the MySQL Training Service market is the rapid digital transformation across industries. Enterprises are increasingly adopting digital technologies to enhance operational efficiency, improve customer experience, and gain a competitive edge. This digital shift necessitates a strong foundation in database management, propelling the demand for MySQL training services. Additionally, the proliferation of big data analytics, cloud computing, and Internet of Things (IoT) technologies has further accentuated the need for proficient MySQL professionals.

Another significant driver is the widespread adoption of MySQL as a preferred database management system. Known for its reliability, scalability, and open-source nature, MySQL has become a staple in various industry verticals, including IT and telecommunications, BFSI, healthcare, retail, and manufacturing. As more organizations integrate MySQL into their IT infrastructure, the demand for training services to upskill employees and ensure optimal database performance has surged. This trend is particularly prominent among enterprises that prioritize cost-effective and efficient database solutions.

The increasing emphasis on data security and compliance also plays a crucial role in the market's growth. With stringent regulatory requirements and the rising threat of cyberattacks, organizations are keen on equipping their workforce with the necessary skills to secure and manage their databases effectively. MySQL training services offer specialized courses that cover security best practices, data encryption, and compliance frameworks, thereby addressing a critical need in the market. This focus on security and compliance is expected to drive sustained demand for MySQL training services in the coming years.

From a regional perspective, North America holds a significant share of the MySQL Training Service market, owing to the high concentration of technology companies and the early adoption of digital technologies. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. This growth can be attributed to the rapid economic development in countries like India and China, the increasing penetration of internet services, and the expanding IT industry. The growing number of startups and small and medium-sized enterprises (SMEs) in the region also contribute to the burgeoning demand for MySQL training services.

Training Type Analysis

In terms of training type, the MySQL Training Service market is segmented into online training, classroom training, and corporate training. Online training has gained significant traction in recent years, driven by the convenience and flexibility it offers. With the rise of e-learning platforms and the increasing availability of high-speed internet, professionals can now access MySQL training modules from the comfort of their homes or offices. This mode of training is particularly popular among working professionals who seek to upskill without disrupting their work schedule. Additionally, online training often comes with interactive features like live sessions, discussion forums, and virtual labs, enhancing the learning experience.

Classroom training, on the other hand, continues to be a preferred choice for individuals who benefit from face-to-face interactions with instructors and peers. This traditional mode of training is particularly effective for hands-on learning, where participants can engage in real-time problem-solving and receive immediate feedback. Classroom training programs are commonly offered by academic institutions, training centers, and specialized boot camps. Despite the growing popularity of online training, classroom training remains relevant due to its structured approach and the personal touch it provides.

Corporate training is another critical segment in the MySQL Training Service market. Enterprises often invest in corporate training p

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.

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.

Introduction

This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.

NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

Datasets

The firts dataset was colleted to train the detection models (D1) and other collected using different attacks than those used in training to test the models and ensure their generalization (D2).

The datasets contain both benign and malicious traffic. All collected datasets are balanced.

The version of NetFlow used to build the datasets is 5.

    Dataset
    Aim
    Samples
    Benign-malicious
    traffic ratio




    D1
    Training
    400,003
    50%


    D2
    Test
    57,239
    50%

Infrastructure and implementation

Two sets of flow data were collected with DOROTHEA. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor ipt_netflow installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.

DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)

Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).

The malicious traffic collected (SQLI attacks) was performed using SQLMAP. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.

The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.

    Parameters
    Description




    '--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'
    Enumerate users, password hashes, privileges, roles, databases, tables and columns


    --level=5
    Increase the probability of a false positive identification


    --risk=3
    Increase the probability of extracting data


    --random-agent
    Select the User-Agent randomly


    --batch
    Never ask for user input, use the default behavior


    --answers="follow=Y"
    Predefined answers to yes

Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the MYSQL or SQLServer database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).

The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24. The malicious traffic in the test sets was collected under different conditions. For D1, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.

However, for D2, BlindSQL SQLIAs were performed against the web form connected to a PostgreSQL database. The IP address spaces of the networks were also different from those of D1. In D2, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.

To run the MySQL server we ran MariaDB version 10.4.12. Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.

RaMP (Relational Database of Metabolic Pathways) MySQL sql dump file to be used with the RaMP R package. https://github.com/ncats/RaMP-DB

From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:

• Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.

• Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.

• Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.

• Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.

This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:

1. A diagram of the MySQL database schema.
2. A SQL dump of the MySQL database schema, excluding the data.
3. A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
4. Raw data from each database table in CSV format.
5. A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
6. A text file containing the definitions of the size categories used in the collection_unit table.

The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:

Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.

Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.

Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.

In 2023, over ** percent of surveyed software developers worldwide reported using PostgreSQL, the highest share of any database technology. Other popular database tools among developers included MySQL and SQLite.

The global cloud database MySQL market is experiencing robust growth, driven by increasing adoption of cloud-based solutions across various industries. The market's expansion is fueled by several factors, including the scalability and cost-effectiveness of cloud databases, the rising demand for data-driven decision-making, and the growing need for robust and secure data management solutions. While precise figures are unavailable from the provided information, a reasonable estimation, based on the prevalence of MySQL in the broader cloud database market and considering growth trends in similar sectors, suggests a market size of approximately $15 billion in 2025. Assuming a conservative Compound Annual Growth Rate (CAGR) of 18% (reflecting moderate growth within a maturing but still expanding market), the market is projected to reach approximately $35 billion by 2033. This growth trajectory is influenced by the ongoing digital transformation across enterprises, the burgeoning adoption of DevOps methodologies, and the expanding capabilities of MySQL itself, particularly in areas like performance optimization and enhanced security features. This growth is not uniform across all regions. North America and Europe are expected to maintain significant market shares due to early adoption and mature technological infrastructure. However, faster growth is anticipated in Asia-Pacific and other emerging markets, driven by increasing digitalization and infrastructure investments. The competitive landscape is highly dynamic, with major players like Microsoft, Amazon Web Services, Google, and Oracle vying for market dominance. Smaller, specialized cloud providers also play a crucial role, catering to niche market requirements and fostering innovation. However, challenges remain, such as data security concerns, vendor lock-in, and the complexity associated with migrating existing on-premise databases to the cloud. Overcoming these hurdles will be crucial for sustained market growth in the years to come.

Korean Text to MySQL Dataset

  Dataset Summary

Korean Text to MySQL is a dataset comprising approximately 3,300 samples generated using OpenAI's gpt-4o model. This dataset is designed to train models that convert natural language questions in Korean into MySQL queries. The data generation process was inspired by the Self-Instruct method and followed the steps outlined below.

  Data Generation Process

1. Creation of SEED Dataset

Approximately 100 SEED samples were… See the full description on the dataset page: https://huggingface.co/datasets/won75/text_to_sql_ko.