The global market for Non-relational SQL (often referred to as NoSQL databases) is poised for exceptional growth, projected to reach a significant $3070.1 million by 2025. This surge is fueled by an impressive Compound Annual Growth Rate (CAGR) of 28.1%, indicating a rapid and sustained expansion throughout the forecast period of 2025-2033. The primary drivers behind this robust expansion are the increasing adoption of big data analytics, the proliferation of e-commerce platforms, the ever-growing demand for scalable mobile and web applications, and the critical need for efficient metadata storage and cache memory solutions. As businesses across all sectors grapple with the challenges of managing and processing vast, unstructured datasets, NoSQL databases are emerging as the go-to solution due to their flexibility, scalability, and ability to handle diverse data types. This market's dynamism is further underscored by the emergence of new trends such as the rise of multi-model databases that combine different NoSQL approaches, and the increasing integration of NoSQL with cloud-native architectures for enhanced agility and cost-effectiveness. Despite the overwhelmingly positive growth trajectory, certain restraints might moderate the pace of adoption in specific niches. These include the perceived complexity of migrating from traditional relational databases, the need for specialized skill sets among developers and administrators to effectively manage NoSQL environments, and ongoing concerns around data consistency for highly transactional applications. However, these challenges are being steadily addressed by advancements in database management tools, comprehensive training programs, and the development of hybrid solutions. The market is segmented by application, with Data Storage, Metadata Store, Cache Memory, Distributed Data Depository, e-Commerce, Mobile Apps, Web Applications, Data Analytics, and Social Networking representing key areas of adoption. By type, the market encompasses Key-Value Stores, Document Databases, Column-Based Stores, and Graph Databases, each catering to distinct data management requirements. Leading companies such as Microsoft SQL Server, MySQL, MongoDB, PostgreSQL, Oracle Database, DynamoDB, and IBM are at the forefront of innovation, offering a wide array of solutions to meet the evolving needs of businesses worldwide. The Asia Pacific region is anticipated to be a significant growth engine, driven by rapid digital transformation and a burgeoning tech industry, while North America and Europe will continue to represent mature and substantial markets. Here's a unique report description on Non-relational SQL, incorporating your specified elements:

A relational database is a common way way of storing and organising data.In a relational database is typically structured across multiple tables which can be joined together by a common value. Data is stored in several smaller tables rather than one large table, this makes data storage more efficient as not every piece of information needs to be recorded against each record.

SQL In-Memory Database Market size was valued at USD 9.26 Billion in 2024 and is projected to reach USD 35.7 Billion by 2032, growing at a CAGR of 20.27% from 2026 to 2032.

SQL In-Memory Database Market Drivers

Demand for Real-Time Analytics and Processing: Businesses increasingly require real-time insights from their data to make faster and more informed decisions. SQL In-Memory databases excel at processing data much faster than traditional disk-based databases, enabling real-time analytics and operational dashboards.

Growth of Big Data and IoT Applications: The rise of Big Data and the Internet of Things (IoT) generates massive amounts of data that needs to be processed quickly. SQL In-Memory databases can handle these high-velocity data streams efficiently due to their in-memory architecture.

Improved Performance for Transaction Processing Systems (TPS): In-memory databases offer significantly faster query processing times compared to traditional databases. This translates to improved performance for transaction-intensive applications like online banking, e-commerce platforms, and stock trading systems.

Reduced Hardware Costs (in some cases): While implementing an in-memory database might require an initial investment in additional RAM, it can potentially reduce reliance on expensive high-performance storage solutions in specific scenarios.

Focus on User Experience and Application Responsiveness: In today's digital landscape, fast and responsive applications are crucial. SQL In-Memory databases contribute to a smoother user experience by enabling quicker data retrieval and transaction processing.

However, it's important to consider some factors that might influence market dynamics:

Limited Data Capacity: In-memory databases are typically limited by the amount of available RAM, making them less suitable for storing massive datasets compared to traditional disk-based solutions.

Higher Implementation Costs: Setting up and maintaining an in-memory database can be more expensive due to the additional RAM requirements compared to traditional databases.

Hybrid Solutions: Many organizations opt for hybrid database solutions that combine in-memory and disk-based storage, leveraging the strengths of both for different data sets and applications.

This is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

Discover the booming global storage engine market forecast to 2033. This in-depth analysis reveals key drivers, trends, and challenges impacting the growth of InnoDB, MyISAM, NoSQL databases like MongoDB & Cassandra, and major players like AWS, Azure, and IBM. Explore regional market shares and CAGR projections.

The SQL In-Memory Database market is projected to witness significant growth in the coming years, driven by the increasing need for real-time data processing and analytics. The rise of big data and the Internet of Things (IoT) has led to an explosion of data, making it essential for businesses to have the ability to quickly and efficiently process and analyze data in order to gain actionable insights. SQL In-Memory Databases, which store data in memory rather than on disk, offer superior performance and speed, making them ideal for handling large and complex datasets in real-time. The growing adoption of cloud computing is another factor contributing to the growth of the SQL In-Memory Database market. Cloud-based SQL In-Memory Databases offer a number of advantages, including scalability, flexibility, and cost-effectiveness. They allow businesses to easily scale their database up or down as needed, and they eliminate the need for expensive hardware and maintenance costs. As a result, cloud-based SQL In-Memory Databases are becoming increasingly popular with businesses of all sizes.

In-Memory Database as a Service Market Outlook

As per our latest research, the global In-Memory Database as a Service (DBaaS) market size reached USD 3.85 billion in 2024, reflecting robust adoption across industries. The market is expected to grow at a strong CAGR of 25.4% from 2025 to 2033, reaching a projected value of USD 31.1 billion by 2033. This remarkable growth trajectory is driven by the increasing demand for ultra-fast data processing, real-time analytics, and the proliferation of cloud-based services across diverse sectors.

A key growth factor for the In-Memory DBaaS market is the exponential increase in data generation and the need for real-time data processing. Organizations are increasingly relying on data-driven decision-making, which necessitates rapid access to and analysis of large datasets. In-memory databases, by storing data directly in the main memory rather than on disk, offer significantly faster data retrieval and transaction processing. This capability is particularly vital for applications in financial services, telecommunications, retail, and healthcare, where milliseconds can make a substantial difference in outcomes. As enterprises continue to digitalize operations and customer expectations for instantaneous services grow, the demand for in-memory database solutions delivered as a service is expected to surge.

Another major driver is the widespread adoption of cloud computing and the shift towards hybrid and multi-cloud strategies. In-Memory DBaaS platforms offer organizations the flexibility to scale resources up or down based on workload demands, without the need for significant capital investment in physical infrastructure. The cloud-based delivery model also simplifies database management, maintenance, and disaster recovery, making it an attractive proposition for both small and large enterprises. Additionally, the integration of advanced technologies such as artificial intelligence, machine learning, and IoT with in-memory databases is enhancing their capabilities, enabling more sophisticated analytics and supporting complex, data-intensive applications.

Furthermore, the increasing focus on digital transformation initiatives across industries is propelling the adoption of In-Memory DBaaS solutions. Companies are seeking to modernize their IT infrastructures to stay competitive, improve operational efficiency, and deliver enhanced customer experiences. In-memory databases provide the performance, scalability, and reliability required for next-generation applications, such as personalized recommendations, fraud detection, and real-time supply chain optimization. The availability of managed DBaaS offerings from leading cloud providers is further lowering the barriers to entry, enabling organizations of all sizes to leverage the benefits of in-memory computing without the need for specialized in-house expertise.

From a regional perspective, North America currently holds the largest share of the global In-Memory DBaaS market, driven by the presence of major technology companies, early adoption of cloud services, and significant investments in digital infrastructure. However, the Asia Pacific region is expected to exhibit the highest growth rate over the forecast period, fueled by rapid digitalization, expanding IT and telecom sectors, and increasing investments in cloud computing across countries such as China, India, and Japan. Europe and Latin America are also witnessing growing adoption, supported by favorable regulatory environments and the rising need for agile, real-time data solutions in sectors like BFSI, healthcare, and retail.

Database Type Analysis

The In-Memory Database as a Service market is segmented by database type into Relational, NoSQL, and NewSQL databases. Relational databases continue to dominate the market, owing to their widespread use in enterprise applications that require robust transactional integrity and structured data management. The familiarity of SQL and the maturity of relational database management systems make them a preferred choice for organizations migrating mission-critical workloads to the cloud. Many leading DBaaS providers offer fully managed relational in-memory solutions, enabling seamless integration with existing enterprise ecosystems and supporting a wide range of business applications.

NoSQL in-memory databases are gaining significant traction, particularly among organizations dealing with unstructured or semi-structured data and requiring h

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

This paper explores the rise of NoSQL (Not Only SQL) databases as a modern alternative that addresses the demands of today’s dynamic, large-scale data environments. The goal is to provide a comprehensive and accessible overview of NoSQL systems and their increasing significance in modern data management.

According to Cognitive Market Research, the global In-Memory Database market size was USD 7.8 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 19.1% from 2024 to 2031. Market Dynamics of In-Memory Database Market

Key Drivers for In-Memory Database Market

Increasing Volume of Data - The exponential growth of data generated by various sources, including social media, IoT devices, and enterprise applications, is another key driver for the IMDB market. Organizations are increasingly seeking efficient ways to manage and analyze this vast amount of data to gain actionable insights and maintain a competitive edge. In-memory databases are well-suited to handle large volumes of data with high throughput, providing the scalability needed to accommodate the growing data influx. The ability to scale horizontally by adding more nodes to the database cluster ensures that IMDBs can meet the demands of data-intensive applications.
The increasing dependence on real-time analytics and decision-making is anticipated to drive the In-Memory Database market's expansion in the years ahead.

Key Restraints for In-Memory Database Market

The amount of available RAM, which can restrict their scalability for very large datasets, limits the In-Memory Database industry growth.
The market also faces significant difficulties related to the high cost of implementation.

Introduction of the In-Memory Database Market

The In-Memory Database market is experiencing robust growth, driven by the need for high-speed data processing and real-time analytics across various industries. In-memory databases store data directly in the main memory (RAM) rather than on traditional disk storage, allowing for significantly faster data retrieval and manipulation. This technology is particularly advantageous for applications requiring rapid transaction processing and real-time data insights, such as financial services, telecommunications, and e-commerce. Despite its benefits, the market faces challenges, including high implementation costs and limitations on data storage capacity due to RAM constraints. Additionally, concerns about data volatility and the need for continuous power supply further complicate adoption. However, advancements in memory technology, declining costs of RAM, and the increasing demand for real-time analytics are driving market growth. As businesses seek to enhance performance and decision-making capabilities, the In-Memory Database market is poised for continued expansion, providing critical solutions for high-performance data management.

The cloud database market is experiencing robust growth, driven by the increasing adoption of cloud computing across various industries. Businesses are migrating their on-premise databases to the cloud to leverage scalability, cost-effectiveness, and enhanced security. The market is segmented by application (Small and Medium Businesses (SMBs) and Large Enterprises) and type (Database Application Builder, Data Scaling and Replication, Backup and Recovery, Database Encryption, and Others). Large enterprises are currently the dominant segment, owing to their greater technological investment and need for robust data management solutions. However, SMBs are exhibiting rapid growth, driven by the availability of cost-effective cloud solutions and the increasing need for digital transformation. Key market drivers include the rising volume of data generated globally, increasing demand for data analytics, and the growing adoption of cloud-native applications. Trends include the increasing popularity of serverless databases, the integration of AI and machine learning into database management systems, and the rising demand for managed cloud database services. While competitive pressures and data security concerns act as restraints, the overall market outlook remains positive, with a projected compound annual growth rate (CAGR) signifying significant expansion over the forecast period (2025-2033). We estimate the 2025 market size to be $80 billion, growing to approximately $120 billion by 2033, based on projected growth rates from similar technology sectors. Leading vendors like Amazon, Microsoft, Oracle, and Google are aggressively investing in research and development, further fueling market expansion through innovation and competition. The geographical distribution of the cloud database market shows strong presence in North America and Europe, attributed to high technological adoption and established digital infrastructure. However, Asia Pacific and Middle East & Africa are showing the highest growth rates, driven by increasing internet penetration and government initiatives promoting digital transformation. The market is characterized by intense competition among established players and emerging cloud providers. Strategic partnerships, acquisitions, and continuous product innovation are key strategies being employed by these players to gain market share. Future growth is anticipated to be fuelled by the increasing adoption of 5G technology, the growth of the Internet of Things (IoT), and the continued expansion of big data analytics. The focus will remain on enhanced security, compliance, and improved ease-of-use to attract a wider range of customers across diverse industries.

Discover the booming Key-Value Database market! Our analysis reveals a $1288.8 million market in 2025, projecting robust growth driven by cloud adoption and big data. Learn about key players (AWS, Azure, etc.), market trends, and regional insights. Explore the future of NoSQL and high-performance data storage.

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.

Database Servers Market size was valued at USD 15.84 Billion in 2023 and is projected to reach USD 29.3 Billion by 2031, growing at a CAGR of 8.8% during the forecast period 2024-2031.

Global Database Servers Market Drivers

The market for database servers is driven by several key factors:

Growing Data Volume: The exponential growth in data generated by various sources such as social media, e-commerce, and IoT devices is driving the need for robust database servers to store, manage, and analyze this data. Digital Transformation: Organizations are increasingly adopting digital transformation strategies, which necessitate the use of advanced database servers to support new applications, services, and processes. Cloud Adoption: The shift towards cloud computing is a significant driver, as cloud-based database servers offer scalability, flexibility, and cost savings, making them attractive to businesses of all sizes. Big Data and Analytics: The demand for big data analytics to gain insights and drive business decisions is boosting the adoption of powerful database servers capable of handling large datasets and complex queries. IoT Proliferation: The rise of Internet of Things (IoT) devices, which continuously generate data, is creating a need for efficient database servers to process and analyze IoT data in real-time. Enterprise Applications: The increasing deployment of enterprise applications such as ERP, CRM, and SCM systems requires reliable and high-performance database servers to ensure seamless operations and data integrity. Regulatory Compliance: Strict regulatory requirements regarding data storage, privacy, and security are pushing organizations to invest in advanced database servers that can ensure compliance and protect sensitive information. Technological Advancements: Continuous innovations in database technologies, including in-memory databases, NoSQL databases, and NewSQL databases, are driving market growth by offering improved performance, scalability, and flexibility. AI and Machine Learning: The integration of artificial intelligence and machine learning into business processes is increasing the need for powerful database servers that can support the storage and processing requirements of AI and ML algorithms. Backup and Disaster Recovery: The growing importance of data backup and disaster recovery solutions is driving the demand for robust database servers that can ensure data availability and business continuity in the event of data loss or system failure. E-commerce Growth: The rapid expansion of the e-commerce sector is creating a need for database servers that can handle high transaction volumes and provide real-time data processing capabilities.

Distributed SQL Database as a Service Market Outlook

According to our latest research, the global Distributed SQL Database as a Service market size reached USD 1.12 billion in 2024, reflecting robust momentum in cloud-native database adoption. The market is poised for substantial growth, projected to expand at a CAGR of 25.6% from 2025 to 2033. By the end of 2033, the market is expected to achieve a value of approximately USD 8.8 billion. This remarkable growth trajectory is primarily driven by enterprises’ increasing demand for high-availability, scalable, and globally distributed data management solutions, as well as the proliferation of cloud infrastructure and digital transformation initiatives across all major industries.

A key growth factor for the Distributed SQL Database as a Service market is the rapid shift towards cloud-native architectures and microservices-based applications. Enterprises are increasingly realizing the limitations of traditional relational databases in handling globally distributed workloads and mission-critical, real-time transactional data. The need for elastic scalability, continuous availability, and seamless geo-distribution has propelled organizations to adopt distributed SQL databases delivered as a service. This shift is further reinforced by the growing adoption of hybrid and multi-cloud strategies, which require databases capable of operating efficiently across diverse cloud and on-premises environments. As organizations prioritize agility and business continuity, the demand for Distributed SQL Database as a Service is expected to accelerate over the forecast period.

Another significant driver is the surge in data volumes generated by digital business processes, IoT devices, and customer-facing applications. Modern enterprises, especially those in sectors such as BFSI, retail, e-commerce, and telecommunications, require robust data platforms that can process, analyze, and store massive amounts of structured and semi-structured data in real time. Distributed SQL Database as a Service solutions offer horizontal scaling, strong consistency, and automated failover, making them ideal for supporting high-throughput transaction management and analytics workloads. Furthermore, the integration of advanced security features, compliance capabilities, and automated management tools has made these solutions attractive for organizations seeking to reduce operational complexity and total cost of ownership.

The market’s expansion is also fueled by the increasing focus on digital transformation and modernization of legacy IT systems. As enterprises embark on cloud migration journeys, they are leveraging Distributed SQL Database as a Service to modernize their data infrastructure, enhance application performance, and improve customer experiences. The proliferation of SaaS, mobile, and edge computing applications necessitates databases that can operate seamlessly across geographies and deliver low-latency access to data. Additionally, the availability of flexible deployment models, including public, private, and hybrid clouds, allows organizations to tailor their database strategies to meet regulatory, security, and performance requirements. These factors collectively contribute to the sustained growth of the Distributed SQL Database as a Service market.

From a regional perspective, North America continues to dominate the Distributed SQL Database as a Service market, accounting for the largest revenue share in 2024, owing to the early adoption of cloud technologies and the presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increased cloud investments, and expanding IT infrastructure in countries such as China, India, and Japan. Europe also demonstrates strong growth potential, supported by stringent data protection regulations and the rising adoption of cloud-based database solutions among enterprises. Latin America and the Middle East & Africa are gradually catching up, with increasing awareness and investments in cloud-native data platforms. The regional landscape is expected to evolve further as organizations worldwide embrace distributed database technologies to gain competitive advantage.

The "Wikipedia SQLite Portable DB" is a compact and efficient database derived from the Kensho Derived Wikimedia Dataset (KDWD). This dataset provides a condensed subset of raw Wikimedia data in a format optimized for natural language processing (NLP) research and applications.

I am not affiliated or partnered with the Kensho in any way, just really like the dataset for giving my agents to query easily.

Key Features:

Contains over 5 million rows of data from English Wikipedia and Wikidata Stored in a portable SQLite database format for easy integration and querying Includes a link-annotated corpus of English Wikipedia pages and a compact sample of the Wikidata knowledge base Ideal for NLP tasks, machine learning, data analysis, and research projects

The database consists of four main tables:

• items: Contains information about Wikipedia items, including labels and descriptions
• properties: Stores details about Wikidata properties, such as labels and descriptions
• pages: Provides metadata for Wikipedia pages, including page IDs, item IDs, titles, and view counts
• link_annotated_text: Contains the link-annotated text of Wikipedia pages, divided into sections

This dataset is derived from the Kensho Derived Wikimedia Dataset (KDWD), which is built from the English Wikipedia snapshot from December 1, 2019, and the Wikidata snapshot from December 2, 2019. The KDWD is a condensed subset of the raw Wikimedia data in a form that is helpful for NLP work, and it is released under the CC BY-SA 3.0 license. Credits: The "Wikipedia SQLite Portable DB" is derived from the Kensho Derived Wikimedia Dataset (KDWD), created by the Kensho R&D group. The KDWD is based on data from Wikipedia and Wikidata, which are crowd-sourced projects supported by the Wikimedia Foundation. We would like to acknowledge and thank the Kensho R&D group for their efforts in creating the KDWD and making it available for research and development purposes. By providing this portable SQLite database, we aim to make Wikipedia data more accessible and easier to use for researchers, data scientists, and developers working on NLP tasks, machine learning projects, and other data-driven applications. We hope that this dataset will contribute to the advancement of NLP research and the development of innovative applications utilizing Wikipedia data.

https://www.kaggle.com/datasets/kenshoresearch/kensho-derived-wikimedia-data/data

Tags: encyclopedia, wikipedia, sqlite, database, reference, knowledge-base, articles, information-retrieval, natural-language-processing, nlp, text-data, large-dataset, multi-table, data-science, machine-learning, research, data-analysis, data-mining, content-analysis, information-extraction, text-mining, text-classification, topic-modeling, language-modeling, question-answering, fact-checking, entity-recognition, named-entity-recognition, link-prediction, graph-analysis, network-analysis, knowledge-graph, ontology, semantic-web, structured-data, unstructured-data, data-integration, data-processing, data-cleaning, data-wrangling, data-visualization, exploratory-data-analysis, eda, corpus, document-collection, open-source, crowdsourced, collaborative, online-encyclopedia, web-data, hyperlinks, categories, page-views, page-links, embeddings

Usage with LIKE queries: ``` import aiosqlite import asyncio

class KenshoDatasetQuery: def init(self, db_file): self.db_file = db_file

async def _aenter_(self):
  self.conn = await aiosqlite.connect(self.db_file)
  return self

async def _aexit_(self, exc_type, exc_val, exc_tb):
  await self.conn.close()

async def search_pages_by_title(self, title):
  query = """
  SELECT pages.page_id, pages.item_id, pages.title, pages.views, 
      items.labels AS item_labels, items.description AS item_description,
      link_annotated_text.sections
  FROM pages 
  JOIN items ON pages.item_id = items.id
  JOIN link_annotated_text ON pages.page_id = link_annotated_text.page_id
  WHERE pages.title LIKE ?
  """
  async with self.conn.execute(query, (f"%{title}%",)) as cursor:
    return await cursor.fetchall()

async def search_items_by_label_or_description(self, keyword):
  query = """
  SELECT id, labels, description 
  FROM items
  WHERE labels LIKE ? OR description LIKE ?
  """
  async with self.conn.execute(query, (f"%{keyword}%", f"%{keyword}%")) as cursor:
    return await cursor.fetchall()

async def search_items_by_label(self, label):
  query = """
  SELECT id, labels, description
  FROM items 
  WHERE labels LIKE ?
  """
  async with self.conn.execute(query, (f"%{label}%",)) as cursor:
    return await cursor.fetchall()

async def search_properties_by_label_or_desc...

The global Data Base Management Systems (DBMS) market is poised for significant expansion, projected to reach a substantial valuation by 2033. Fueled by an estimated Compound Annual Growth Rate (CAGR) of 12%, the market's robust trajectory is driven by the escalating need for efficient data handling, storage, and recovery across diverse industries. The proliferation of digital data, coupled with the increasing adoption of cloud computing and big data analytics, acts as a primary catalyst. Organizations are increasingly reliant on sophisticated DBMS solutions to manage complex datasets, ensure data integrity, and derive actionable insights. Applications such as advanced data management, critical data recovery services, and scalable data storage solutions are experiencing heightened demand. Furthermore, the growing importance of database operation management and comprehensive database maintenance management underscores the market's evolution towards more intelligent and automated data governance. This dynamic landscape necessitates continuous innovation in DBMS technologies to address the evolving challenges of data security, performance optimization, and regulatory compliance. The DBMS market is characterized by intense competition and strategic collaborations among major players including Microsoft, IBM, Oracle, and PostgreSQL, alongside specialized providers like NCR, Pervasive Software, and Tandem. While the North American region currently holds a dominant market share, significant growth is anticipated in the Asia Pacific and emerging economies, driven by rapid digital transformation initiatives and increasing IT infrastructure investments. However, the market faces certain restraints, including the high cost of implementation and maintenance for certain advanced systems and the ongoing challenge of finding skilled professionals for complex database administration. Despite these hurdles, the market's underlying growth drivers, such as the continuous surge in data generation and the critical need for efficient data utilization, are expected to outweigh these challenges. The ongoing development of cloud-native DBMS, AI-powered database optimization, and enhanced security features are anticipated to shape the future of the DBMS landscape, ensuring its continued relevance and expansion in the coming years. This comprehensive report delves into the dynamic landscape of Data Base Management Systems (DBMS), offering an in-depth analysis of market dynamics, trends, and future projections. Spanning the Study Period: 2019-2033, with a keen focus on the Base Year: 2025 and Forecast Period: 2025-2033, this report utilizes robust methodologies to provide actionable insights. The Historical Period: 2019-2024 lays the groundwork for understanding past performance and identifying key evolutionary shifts.

The global Database Software market is poised for substantial growth, projected to reach a market size of approximately $75 billion by 2025 and expand at a Compound Annual Growth Rate (CAGR) of around 10% through 2033. This robust expansion is primarily driven by the escalating volume of data generated across all industries and the increasing demand for sophisticated data management and analysis capabilities. Large enterprises, in particular, are significant contributors to this market, leveraging advanced database solutions for complex operations and strategic decision-making. However, the growing adoption of cloud-based solutions and the rise of specialized analytical and operational database software are also fueling market momentum. Furthermore, the increasing integration of AI and machine learning in database management systems is creating new opportunities for innovation and market penetration, as businesses seek to unlock deeper insights from their data. The market is characterized by a dynamic competitive landscape, with established giants like Oracle, Microsoft, and IBM continually innovating their offerings alongside agile cloud providers such as Amazon Web Services. The proliferation of Small and Medium-sized Enterprises (SMEs) adopting digital transformation initiatives is also creating a burgeoning segment for more accessible and scalable database solutions. Key trends include the shift towards distributed and cloud-native databases, emphasizing scalability, resilience, and cost-effectiveness. While the market benefits from strong growth drivers, certain restraints, such as data security concerns and the high cost of implementation for some advanced solutions, are present. Nonetheless, the overarching need for efficient data storage, processing, and retrieval positions the Database Software market for sustained and significant growth in the coming years. This comprehensive report delves into the dynamic landscape of the global Database Software market, providing in-depth analysis and forecasts for the period 2019-2033. The Base Year for this study is 2025, with estimates and projections focusing on the Forecast Period of 2025-2033, building upon the Historical Period of 2019-2024. The report will meticulously examine market size, growth drivers, challenges, and the competitive ecosystem, leveraging data in the million unit for financial metrics.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs).Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.