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.

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

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

The statistic displays the most popular SQL databases used by software developers worldwide, as of **********. According to the survey, ** percent of software developers were using MySQL, an open-source relational database management system (RDBMS).

The global database market, currently valued at $131.67 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 14.21% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of cloud-based solutions offers scalability and cost-effectiveness, fueling market expansion. Furthermore, the burgeoning demand for real-time data analytics across diverse sectors, including BFSI (Banking, Financial Services, and Insurance), retail & e-commerce, and healthcare, is significantly boosting database market growth. The rise of big data and the need for robust data management solutions to handle massive datasets are other significant contributors. While on-premises deployments still hold a significant market share, particularly among large enterprises with stringent security requirements, the cloud segment is projected to witness the highest growth rate over the forecast period. The market is segmented by deployment (cloud, on-premises), enterprise size (SMEs, large enterprises), and end-user vertical (BFSI, retail & e-commerce, logistics & transportation, media & entertainment, healthcare, IT & telecom, others). Competition is intense, with established players like MongoDB, MarkLogic, Redis Labs, and Teradata alongside tech giants such as Microsoft, Amazon, and Google vying for market share through innovation and strategic partnerships. The competitive landscape is characterized by both established vendors and new entrants, leading to continuous innovation in database technologies. The market is witnessing a shift towards NoSQL databases, driven by the need to handle unstructured data and the increasing popularity of cloud-native applications. However, challenges such as data security concerns, the complexity of managing distributed database systems, and the need for skilled professionals to manage and maintain these systems pose potential restraints. The market's growth trajectory is largely positive, with continued expansion anticipated across all key segments and regions. North America and Europe are currently the dominant markets, but rapid growth is expected in Asia-Pacific, driven by increased digitalization and technological advancements in developing economies such as India and China. This comprehensive report provides an in-depth analysis of the global database market, encompassing historical data (2019-2024), current estimates (2025), and future forecasts (2025-2033). It examines key market segments, growth drivers, challenges, and emerging trends, offering valuable insights for businesses, investors, and stakeholders seeking to navigate this dynamic landscape. The study period covers the significant evolution of database technologies, from traditional relational databases to the rise of NoSQL and cloud-based solutions. The report utilizes a robust methodology and extensive primary and secondary research to provide accurate and actionable market intelligence. Keywords include: database market size, database market share, cloud database, NoSQL database, relational database, database management system (DBMS), database market trends, database market growth, database technology. Recent developments include: January 2024: Microsoft and Oracle recently announced the general availability of Oracle Database@Azure, allowing Azure customers to procure, deploy, and use Oracle Database@Azure with the Azure portal and APIs.November 2023: VMware, Inc. and Google Cloud announced an expanded partnership to deliver Google Cloud’s AlloyDB Omni database on VMware Cloud Foundation, starting with on-premises private clouds.. Key drivers for this market are: Increasing Penetration Of Trends Like Big Data And IoT, Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Potential restraints include: Increasing Penetration Of Trends Like Big Data And IoT, Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Notable trends are: Retail and E-commerce to Hold Significant Share.

As of December 2022, relational database management systems (RDBMS) were the most popular type of DBMS, accounting for a ** percent popularity share. The most popular RDBMS in the world has been reported as Oracle, while MySQL and Microsoft SQL server rounded out the top three.

Open-Source Database Software Market Outlook

The global open-source database software market size was estimated at USD 12.3 billion in 2023 and is projected to reach USD 33.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.8% during the forecast period. The growth factors propelling this market include the increasing adoption of open-source solutions due to cost-efficiency, flexibility, and scalability, alongside the rising volume of data generated by enterprises globally.

One of the primary growth drivers for the open-source database software market is the increasing adoption of big data analytics. Organizations across various sectors are harnessing the power of data to drive decision-making processes, optimize operations, and improve customer experiences. Open-source databases offer the flexibility and scalability required to handle vast amounts of data, making them an ideal choice for companies looking to leverage big data. Moreover, the integration of advanced technologies like artificial intelligence and machine learning with database management systems is further boosting the adoption of open-source databases.

Another significant factor contributing to the market growth is the cost-effectiveness of open-source database solutions. Traditional proprietary database systems often come with high licensing fees and maintenance costs, which can be a significant burden for small and medium-sized enterprises (SMEs). Open-source databases, on the other hand, eliminate these costs, providing a budget-friendly alternative without compromising on functionality. This cost advantage is particularly appealing to startups and SMEs, driving widespread adoption across these segments.

The growing emphasis on data security and privacy is also fueling the demand for open-source database software. With increasing instances of data breaches and stringent regulatory requirements, organizations are prioritizing robust data security measures. Open-source databases offer transparency, allowing organizations to inspect the source code and ensure there are no hidden vulnerabilities. Additionally, the active community support and frequent updates associated with open-source projects contribute to enhanced security, making them a preferred choice for businesses aiming to protect sensitive data.

Regionally, the Asia Pacific region is expected to witness the highest growth in the open-source database software market. The rapid digital transformation across industries, coupled with the increasing adoption of cloud-based solutions, is driving the demand for open-source databases in this region. Countries like China, India, and Japan are leading the charge, with numerous startups and tech companies leveraging open-source technologies to gain a competitive edge. Moreover, government initiatives promoting digitalization and data-driven decision-making are further accelerating the market growth in the Asia Pacific.

Database Type Analysis

The open-source database software market can be segmented by database type into SQL, NoSQL, and NewSQL. SQL databases, known for their structured query language, have traditionally been the backbone for relational database management systems. Despite the emergence of new database types, SQL databases continue to hold a significant market share due to their robustness, reliability, and widespread adoption across various industries. Enterprises rely on SQL databases for critical applications that require ACID (atomicity, consistency, isolation, durability) compliance and complex transactional processes.

NoSQL databases have gained significant traction in recent years, driven by the need to handle unstructured and semi-structured data. These databases offer high scalability and flexibility, making them ideal for applications involving big data, real-time analytics, and internet of things (IoT) deployments. NoSQL databases, such as MongoDB and Cassandra, allow organizations to store and process large volumes of data with ease, enabling faster data retrieval and improved performance. The increasing adoption of web applications and the growing popularity of cloud computing are further propelling the demand for NoSQL databases.

NewSQL databases represent a hybrid approach, combining the benefits of traditional SQL databases with the scalability and flexibility of NoSQL solutions. These databases are designed to address the limitations of both SQL and NoSQL databases, providing high performance, scalability, and transactional consistency. NewSQL databases, such as CockroachDB and VoltDB, are gaining populari

Approximately ** percent of the surveyed software companies in Russia mentioned PostgreSQL, making it the most popular database management system (DBMS) in the period between February and May 2022. MS SQL and MySQL followed, having been mentioned by ** percent and ** percent of respondents, respectively.

Open Source Database Software Market Outlook

The global open source database software market size was valued at approximately USD 11.5 billion in 2023 and is projected to reach an impressive USD 26.8 billion by 2032, growing at a robust CAGR of 9.5% during the forecast period. The exponential growth in this market is attributed to the increasing adoption of cloud-based solutions, surge in enterprise data volume, and the rising demand for cost-effective database management solutions. Organizations across various sectors are increasingly opting for open source database software due to its flexibility, scalability, and ability to handle large volumes of data.

One of the primary growth factors driving the open source database software market is the significant cost savings associated with open source solutions compared to proprietary alternatives. Businesses are continually seeking ways to reduce their IT expenses without compromising on performance and security. Open source database software offers a compelling alternative by eliminating licensing fees and enabling organizations to allocate resources more efficiently. Additionally, the collaborative nature of open source communities fosters continuous improvement and innovation, further enhancing the software's capabilities and reliability.

Another critical growth factor is the accelerating adoption of cloud computing. As more organizations migrate their workloads to the cloud, the demand for cloud-compatible database solutions has surged. Open source database software can be easily integrated with various cloud platforms, providing businesses with the flexibility to scale their operations seamlessly. The cloud-based deployment model also offers benefits such as improved accessibility, reduced infrastructure costs, and enhanced disaster recovery capabilities, making it an attractive option for enterprises of all sizes.

The proliferation of big data and the Internet of Things (IoT) is also contributing significantly to the market's growth. The massive volumes of data generated by IoT devices and other sources require advanced database solutions capable of handling real-time data processing and analytics. Open source database software, with its robust performance and scalability, is well-suited to meet these demands. The ability to customize and extend open source solutions allows organizations to tailor their database infrastructure to specific use cases, further driving adoption across various industries.

Regional outlook for the open source database software market indicates that North America holds the largest market share, driven by the presence of major technology companies and early adoption of advanced IT infrastructure. Europe and Asia Pacific are also significant markets, with the latter expected to witness the highest growth rate during the forecast period. The rapid digitalization of businesses in countries like China and India, coupled with increasing investments in IT infrastructure, is bolstering the market's expansion in the Asia Pacific region.

The emergence of SQL In Memory Database technology is revolutionizing the way organizations handle data-intensive applications. By storing data in the main memory rather than on traditional disk storage, these databases offer significantly faster data retrieval speeds and improved performance. This technology is particularly beneficial for applications requiring real-time analytics and rapid transaction processing, such as financial services, online gaming, and e-commerce. The ability to process large volumes of data with minimal latency is a key advantage, enabling businesses to make quicker and more informed decisions. As the demand for high-performance data solutions grows, SQL In Memory Databases are becoming an integral part of the database landscape, providing the speed and efficiency needed to meet modern business demands.

Database Type Analysis

The open source database software market is segmented into SQL, NoSQL, and NewSQL databases. SQL databases, despite being the oldest form of database management systems, continue to dominate the market due to their robustness, reliability, and widespread adoption. SQL databases are favored for transaction-oriented applications and are commonly used in industries such as banking, finance, and retail. Their ability to handle complex queries, maintain data integrity, and support ACID (Atomicity, Consistency, Isolation, Durability) properties makes them indispensable for criti

This dataset has SQL injection attacks 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.

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

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..

The General Offense Crime Report Dataset includes criminal and city code violation offenses which document the scope and nature of each offense or information gathering activity. It is used to computate the Uniform Crime Report Index as reported to the Federal Bureau of Investigation and for local crime reporting purposes.Contact E-mailLink: N/AData Source: Versaterm Informix RMS \Data Source Type: Informix and/or SQL ServerPreparation Method: Preparation Method: Automated View pulled from SQL Server and published as hosted resource onto ArcGIS OnlinePublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

ckanext-sql Due to the absence of a README file in the provided GitHub repository for ckanext-sql, a comprehensive understanding of its features, integration, and benefits is unfortunately not available. Typically, an extension named 'sql' would likely bridge CKAN with SQL databases, potentially enabling users to query and interact with datasets stored in SQL-compatible databases directly from within CKAN. However, lacking specific documentation, definitive claims about its capabilities cannot be accurately made. Potential Key Features (based on the name and typical use cases): * SQL Query Interface: Hypothetically, this extension might offer an interface within CKAN to run SQL queries against linked datasets. * Data Visualization from SQL: Potentially, it could allow generating visualizations directly from data retrieved via SQL queries. * SQL Data Import: It is possible that the extension could provide functionality to import data from SQL databases into CKAN datasets. * Federated Queries: Maybe, the extension implements capability of running federated queries across datasets store as CKAN resources and external databases. * SQL Data Export: Possibility of offering the ability to export CKAN data to a SQL database. * SQL based resource views: Speculatively add different views for resource showing data from SQL Potential Use Cases (based on the name): 1. Direct Data Analysis: Data analysts might use this to directly query and analyze data stored in SQL databases via CKAN, skipping manually importing the data. 2. Database Integration: Organizations that already have large databases of data could use this extension to provide easier access to this data through a CKAN portal. Technical Integration (Hypothetical): Given the name, the 'sql' extension likely integrates with CKAN by adding new API endpoints or UI elements that allow users to specify SQL connections and queries. It would probably require configuration settings to define database connection parameters. It might also integrate with CKAN's resource view system, enabling custom visualizations. Potential Benefits & Impact (Speculative): If the extension functions as expected by the name, it would offer direct access to SQL data within the CKAN environment, reduce the need for data duplication (by querying directly rather than importing), and potentially enhance data analysis and visualization capabilities. The extension could become an indispensable part of data analytic workflows involving CKAN. However, due to a lack of a README.md, this analysis remains at theoretical level.

A SQLite database containing mass absorption coefficient (both discrete and continuous), atomic sub-shell binding energy, X-ray energy, jump ratio, ground-state occupancy, atomic relaxation rate following core shell ionization and X-ray linewidth data. The data is in the common SQLite format and also available in SQL format. SQLite is an open-source database which is supported on many different platforms. This database represents a compilation of data from other sources. Each datum is labeled with a literature reference which represents the source. The references are listed in the LIT_REFERENCES table with associated BIBTEX reference data. The two exceptions to this rule are the FFAST and FFAST_EXTRA tables which are associated with the Chantler2005 reference.

NoSQL Database Market Outlook 2032

The global NoSQL database market size was USD 5.9 Billion in 2023 and is likely to reach USD 36.6 Billion by 2032, expanding at a CAGR of 30% during 2024–2032. The market growth is attributed to the rising adoption of NoSQL databases by industries to manage large amounts of data efficiently.



Increasing adoption of digital solutions by businesses is augmenting the NoSQL database industry. Businesses continue using the unique capabilities that NoSQL databases bring to their data management strategies. The NoSQL solutions work without any predefined schemas, thus, offering more flexibility to businesses that need to handle and manage ever-evolving data types and formats.

The factors behind the accelerating growth of the NoSQL database market include the omnipresence of internet-related activities, a surge in big data, and others. NoSQL database solutions present exceptional scalability and offer superior performance while managing extensive datasets. Moreover, the shift from conventional SQL databases to NoSQL databases to handle big-data and real-time web application data augmented the market.

Impact of Artificial Intelligence (AI) on the NoSQL Database Market

Artificial Intelligence (AI) has a significant impact on the NoSQL databases market by creating a surge in data volume and variety. AI technologies, including machine learning and deep learning, generate and process vast amounts of data, necessitating efficient data management solutions. The integration of AI with NoSQL databases further enhances data analysis capabilities and enables businesses to acquire valuable insights and make informed decisions. Therefore, the rise of AI technologies is propelling the market.

Non-Relational Databases, commonly referred to as NoSQL databases, have gained significant traction in recent years due to their ability to handle diverse data types and structures. Unlike traditional relational databases, non-relational databases do not rely on a fixed schema, which allows for greater flexibility and scalability. This adaptability is particularly beneficial for businesses dealing with large volumes of unstructured data, such as social media content, customer reviews, and multimedia files. As organizations continue to embrace digital transformation, the demand for non-relational databases is expected to rise, further driving the growth of the NoSQL database market.

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WunDeeDB.jl (v1.0.0) is a package written in Julia Lang that provides a SQLite disk-backed system for storing, searching, and managing embedding vectors at scale, influenced by disk oriented graph-based ANN techniques and the broader insights from hierarchical small-world graphs. By maintaining embeddings in an SQLite database, WunDeeDB.jl reduces in-memory overhead while supporting efficient similarity searches on commodity hardware. Its design also facilitates integration with common vector-database or ML pipelines that rely on embedding retrieval. In contrast to fully in-memory approaches, WunDeeDB.jl leverages disk-based storage and user-configurable adjacency (e.g., HNSW, LM-DiskANN, or fallback linear search), allowing large-scale data to be handled without saturating RAM. It supports incremental insertions and deletions, ensuring the index remains up-to-date as datasets evolve. By combining these disk-native strategies with tunable BFS expansions and adjacency pruning, WunDeeDB.jl enables robust nearest neighbor searches for high-dimensional embeddings

Dataset Information

This data is from Open Source Mental Illness (OSMI) using survey data from years 2014, 2016, 2017, 2018 and 2019. Each survey measures and attitudes towards mental health and frequency of mental health disorders in the tech workplace.

The raw data was processed using Python, SQL and Excel for cleaning and manipulation.

Steps involved in cleaning were - Similar questions were group together - Values for answers were made consistent (ie 1 == 1.0) - Fixing spelling errors

Content

The SQLite database contains 3 tables. Survey, Question, and Answer.

Survey (PRIMARY KEY INT SurveyID, TEXT Description) Question (PRIMARY KEY QuestionID, TEXT QuestionText) Answer (PRIMARY/FOREIGN KEY SurveyID, PRIMARY KEY UserID, PRIMARY/FOREIGN KEY QuestionID, TEXT AnswerText)

SuveyID are simply survey year ie 2014, 2016, 2017, 2018, 2019. The same question can be used for multiple surveys Answer table is a composite table with multiple primary keys. SurveyID and QuestionID are FOREIGN KEYS. Some questions can contain multiple answers, thus the same user can appear more than once for that questionid.

Common SQL queries

query text information for Questionid

SELECT * FROM Question where QuestionID = 13;

query all answers for specified Questionid

SELECT AnswerText FROM Answer where QuestionID = 13;

query distribution of answer given questionid

SELECT AnswerText, COUNT(AnswerText) from Answer where QuestionID = 13 group by AnswerText;

query distribution of answer given questionid and survey year

SELECT AnswerText, COUNT(AnswerText) from Answer where QuestionID = 1 and surveyid = 2016 group by AnswerText;

query number of participants for each survey

SELECT surveyid, COUNT(DISTINCT(userid)) FROM answer GROUP BY surveyid;

Acknowledgements

The original data set can be found Open Source Mental Illness (OSMI) and can be downloaded and viewed here. This project was inspired here.

Database Management Systems (DBMS) Market Outlook

The global market size for Database Management Systems (DBMS) was valued at approximately USD 70 billion in 2023 and is projected to reach around USD 150 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. Several factors such as the exponential growth of data across various industries, the need for efficient data handling, and advancements in technology are driving this remarkable growth.

One significant growth factor for the DBMS market is the surge in data generation from various sources such as social media, e-commerce, IoT devices, and enterprise applications. The volume of data being generated is unprecedented, and organizations need robust systems to store, manage, and analyze this data efficiently. Database Management Systems offer solutions to handle large volumes of data, ensuring data integrity, security, and accessibility, which are critical for business operations and decision-making processes. Furthermore, the increasing adoption of cloud-based solutions is driving the demand for DBMS, as cloud technologies offer scalability, flexibility, and cost-efficiency.

Another growth factor is the rising emphasis on data-driven decision-making across industries. Organizations are increasingly leveraging data analytics to gain insights that can drive business strategies, enhance customer experiences, and improve operational efficiencies. DBMS play a pivotal role in facilitating data analysis by providing a structured and organized framework for data storage and retrieval. Additionally, regulatory requirements for data storage and protection in various industries such as BFSI, healthcare, and government are propelling the demand for secure and compliant DBMS solutions. Compliance with regulations such as GDPR and HIPAA necessitates robust data management systems that can ensure data privacy and security.

Technological advancements in DBMS are also contributing to market growth. Innovations such as in-memory databases, database-as-a-service (DBaaS), and advancements in big data and artificial intelligence are enhancing the capabilities of DBMS. These technologies enable faster data processing, real-time analytics, and improved scalability, making them attractive solutions for modern enterprises. Furthermore, the integration of DBMS with emerging technologies such as blockchain and IoT is creating new opportunities for market expansion. As organizations continue to invest in digital transformation initiatives, the demand for advanced DBMS solutions is expected to increase.

Regionally, North America is expected to dominate the DBMS market during the forecast period, driven by the presence of major technology companies, high adoption of advanced technologies, and significant investments in IT infrastructure. However, the Asia Pacific region is anticipated to witness the highest growth rate, owing to the rapid digitalization, increasing adoption of cloud technologies, and the growing emphasis on data-driven decision-making in emerging economies such as China and India. Europe is also expected to contribute significantly to the market growth, supported by stringent data protection regulations and the increasing adoption of cloud-based DBMS solutions.

Type Analysis

The DBMS market can be segmented by type into Relational, NoSQL, NewSQL, and Others. Relational Database Management Systems (RDBMS) have traditionally been the most widely used type, thanks to their structured data storage model and support for SQL queries. RDBMS such as Oracle, MySQL, and Microsoft SQL Server provide robust data integrity and security features, making them suitable for a wide range of applications from enterprise resource planning (ERP) to customer relationship management (CRM). Despite the emergence of new types of DBMS, RDBMS continues to hold a significant market share due to its established presence and reliability.

NoSQL databases have gained traction in recent years, especially with the rise of big data and the need for handling unstructured data. Unlike RDBMS, NoSQL databases are designed to scale horizontally and can handle large volumes of diverse data types such as documents, graphs, and key-value pairs. This makes them ideal for applications requiring high scalability and flexibility, such as social media platforms, e-commerce sites, and IoT applications. Popular NoSQL databases include MongoDB, Cassandra, and Couchbase, each off

This dataset is a merged collection of multiple text-to-SQL datasets, designed to provide a comprehensive resource for training and evaluating text-to-SQL models. It combines data from several popular benchmarks, including Spider, CoSQL, SparC, and others, to create a diverse and robust dataset for natural language to SQL query generation tasks. Dataset Details Dataset Description Curated by: Mudasir Ahmad Mir Language(s) (NLP): English License: Apache 2.0 This dataset is ideal for researchers… See the full description on the dataset page: https://huggingface.co/datasets/Mudasir692/text-to-sql.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SQL SeqLL Inc. Common stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data