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 popularity of cloud database management systems (DBMSs) are on the rise, growing from *** percent in 2016 to *** percent in 2019, based on the ranking scores of DBSMs. Amazon DynamoDB is was the most popular cloud DBMS at the end of 2019, ranking 16th among all DBMSs.

As of June 2024, almost a ******* percent of the licenses for spatial database management systems (DBMSs) were open-source licenses. Over the years, open source DBMSs have become more and more popular. As of the evaluated period, open source DBMSs have become as popular as commercial ones.

Dataset

This dataset was created by IAmBen2

Released under Other (specified in description)

Contents

Databases with information on Latin American universities according to the webometrics ranking.

** Context**

The TMDB dataset is a csv file. This is a huge dataset of dimensions (244880 x 14). The first line contains the headers that describes what the column/ field contains. Addition to that headers also contain the description of the column.

Content

The available dataset is as follows: 1. Adult : This field says whether the movie is Adult Rated or not. 2. backdrop_path : This column provides the link to the backdrop path 3. genre_ids : This field contains the ID allotted to the specific genre. 4. id: This field labels the ID to the movie. 5. original_language : The original language in which the movie is released. 6. original_title : This field provides the original title of the movie. 7. overview : The overview column describes the movie in brief. 8. Popularity : This field tells us how popular the movie is till date. 9. Poster Path : Provides path to the poster of the movie.

Inspiration

1. You can build a recommendation system.
2. You can predict the popularity of the upcoming movie.

Dataset

This dataset was created by bin zhang

Contents

Distributed Relational Database Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Distributed Relational Database Market Drivers

The market drivers for the Distributed Relational Database Market can be influenced by various factors. These may include:

Growing Data Volume: Organizations require scalable and effective methods to handle and process massive amounts of data due to the exponential growth in data generation. Scalability and enhanced performance are two features that make distributed relational databases a good option for managing large amounts of data.

Cloud Adoption: The market for distributed relational databases has been greatly impacted by the emergence of cloud computing. Cloud platforms are encouraging the usage of distributed databases in cloud environments with their scalable infrastructure and managed database services. Distributed databases are also included by cloud providers into their services, increasing accessibility.

Global Distributed Relational Database Market Restraints

Several factors can act as restraints or challenges for the Distributed Relational Database Market. These may include:

Complexity in Management: Complex configurations and management are frequently associated with distributed relational databases. It can be difficult to ensure data consistency, manage distributed transactions, and deal with node failures; these tasks may call for specific knowledge and resources.

High Initial Costs: Including infrastructure investments and licensing fees, the implementation of distributed relational databases might come with a hefty upfront cost. These upfront expenses may prevent adoption in smaller businesses or those with tighter budgets.

This API is designed to find the rankings by geography within the state for a specific metric (population or household) and rank (any of the metrics from provider, demographic, technology or speed). The results are the top ten and bottom ten records within the state for the particular geography type and my area rankings. Additionally we include +/- 5 rankings from the 'my' area rank.

The Database Platform as a Service (DBPaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and flexible database solutions, and the rising demand for data-driven decision-making across various industries. Let's assume, for illustrative purposes, a 2025 market size of $50 billion and a Compound Annual Growth Rate (CAGR) of 18% for the forecast period 2025-2033. This signifies a significant expansion of the market, projected to reach approximately $180 billion by 2033. This growth is fueled by several key factors, including the migration of on-premise databases to the cloud, the increasing popularity of serverless computing architectures that seamlessly integrate with DBPaaS offerings, and the growing demand for real-time analytics and big data processing capabilities that cloud-based solutions readily provide. The market segmentation reveals a strong preference for cloud-based solutions over on-premise deployments, reflecting the advantages of scalability, cost-effectiveness, and accessibility offered by cloud platforms. Large enterprises are currently the largest consumers, but the growth among medium and small enterprises is accelerating, driven by declining entry barriers and the increasing availability of cost-effective cloud-based DBPaaS options suitable for their needs. The competitive landscape is highly dynamic, with established players like Amazon Web Services, Microsoft, and Google dominating the market share alongside emerging and specialized DBPaaS providers. The continuous innovation in database technologies, such as NoSQL and graph databases, and the emergence of advanced analytics and AI capabilities integrated within DBPaaS platforms, further contribute to market expansion. However, concerns around data security, vendor lock-in, and the complexity of migrating existing database infrastructure to the cloud represent significant challenges that need to be addressed to fully realize the market's potential. The regional analysis suggests that North America and Europe currently hold significant market shares, reflecting their higher levels of cloud adoption and technological advancement; however, rapid growth is expected from Asia-Pacific and other emerging economies as digital transformation efforts accelerate. Overall, the DBPaaS market is poised for continued expansion, driven by ongoing technological advancements and a growing reliance on data-driven strategies.

Embedded Database Management Systems Market size was valued at USD 10.8 Billion in 2024 and is projected to reach USD 18.70 Billion by 2031, growing at a CAGR of 7.1% during the forecasted period 2024 to 2031.

The Embedded Database Management Systems (DBMS) market is driven by the increasing demand for real-time data processing and management across various embedded systems, such as IoT devices, smartphones, automotive systems, and industrial equipment. The rise of connected devices and edge computing has amplified the need for lightweight, efficient, and scalable embedded databases that can operate within resource-constrained environments. Growing adoption of embedded systems in industries like healthcare, automotive, telecommunications, and consumer electronics is also boosting the demand for robust DBMS solutions. Additionally, advancements in AI, machine learning, and data analytics are driving the integration of more sophisticated embedded databases to enable real-time decision-making and enhance device performance.

The global cloud-based database market is projected to reach a colossal $872.12 billion by 2033, expanding at a remarkable CAGR of 52.3% during the forecast period of 2025-2033. This exponential growth is primarily driven by the surging adoption of cloud computing, the increasing popularity of big data and analytics, and the growing demand for scalable and flexible data management solutions. The SQL database and NoSQL database segments dominate the market, while small and medium businesses and large enterprises are key application areas. Cloud-based databases offer numerous advantages over traditional on-premise databases, including cost efficiency, scalability, reliability, and accessibility. These benefits are particularly appealing to organizations that require real-time data processing, complex data analysis, and remote access to data. Furthermore, the emergence of artificial intelligence (AI) and machine learning (ML) is further fueling the demand for cloud-based databases, as these technologies generate vast amounts of data that need to be processed and stored efficiently. Key players in the market include Amazon Web Services, Google, IBM, Microsoft, Oracle, Rackspace Hosting, Salesforce, Cassandra, Couchbase, MongoDB, SAP, and Teradata.

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. 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 size of the Graph Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 64282.28 million by 2032, with an expected CAGR of 18.20% during the forecast period. Recent developments include: June 2021: Neo4j has released its most recent graph database version, 4.3. Graph data analysis, relationship asset indexes, new smart 10 scheduling, and parallelized backup are some of the features included in the most recent version of the graph database., April 2021: The MarkLogic Data Hub Central low-code/no-code user interface was introduced by MarkLogic Corp. With the ease and agility of using the data infrastructure, MarkLogic's launch provides organizations with a clear roadmap for cloud modernization., October 2020: Microsoft Corporation unveiled a brand-new artificial intelligence platform that can caption and describe photos. Azure Cognitive Services offers the system..

The TMDb (The Movie Database) is a comprehensive movie database that provides information about movies, including details like titles, ratings, release dates, revenue, genres, and much more.

This dataset contains a collection of 1,000,000 movies from the TMDB database.

Dataset is updated daily. If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Interesting Task Ideas:

1. Predict movie ratings based on features such as revenue, popularity, genre, and runtime.
2. Identify trends in movie release dates and analyze their impact on revenue.
3. Analyze the relationship between budget, revenue, and popularity to determine factors that contribute to a movie's success.
4. Build a recommendation system that suggests similar movies based on genres, production companies, and language.
5. Perform sentiment analysis on movie reviews to understand audience reactions.
6. Explore the impact of movie genres on popularity and revenue.
7. Investigate the correlation between runtime and audience engagement.
8. Identify successful production companies and analyze their strategies.
9. Utilize natural language processing techniques to extract meaningful insights from movie overviews.
10. Visualize movie popularity over time and identify popular genres in different periods.

Checkout my other datasets

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Photo by Onur Binay on Unsplash

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |

The global NoSQL Software market is anticipated to expand at a CAGR of 22.5% from 2025 to 2033, with a projected market size of USD 325.0 million by 2033. The rising demand for cost-effective data storage solutions, growing popularity of cloud-based services, and increasing need for real-time data processing are key factors driving market growth. Furthermore, the proliferation of big data and the IoT sector is contributing to the demand for NoSQL databases. Among the application segments, data storage dominates the market with a significant share due to the need to store and manage vast amounts of unstructured data. E-commerce, social media, and data analytics are other key applications driving market growth. Geographically, North America is expected to maintain its dominant position throughout the forecast period, followed by Europe and the Asia Pacific region. The increasing adoption of cloud-based NoSQL services and the presence of major vendors in these regions are fueling market expansion. Asia Pacific is anticipated to experience the fastest growth, owing to the rising adoption of NoSQL databases by businesses in developing economies. Key players in the NoSQL software market include MongoDB, Amazon Web Services, Microsoft, and IBM, among others. These companies offer a wide range of NoSQL products, such as document, key-value, and graph databases, to cater to the diverse needs of customers. The global NoSQL software market is anticipated to grow from $7.3 billion in 2023 to $29.8 billion by 2030, exhibiting a CAGR of 22.4% during the forecast period. The increasing adoption of cloud computing, the growing need for data storage and management, and the rising popularity of data analytics are driving the growth of the market.

• The dataset consists of global companies' data sourced and enriched from LinkedIn, with multiple attributes specific to the company including current_technologies and total_funding
• The dataset can be useful for analysis of industry trends, company growth patterns, technology adoption, along with regression analysis to understand if any feature with continuous data can be expressed as a function of other independent variables.

Data and code for Friend-Based Ranking: These data and code are used in the simulations using real world networks in India and Indonesia to compute the likelihood of constructing a mechanism giving a complete ranking.

Universities, as agents of change, are expected to contribute to society’s most pressing challenges, particularly the 21st century’s central issue of sustainability. Amid growing expectations from governments, society, and an increasingly conscientious student body, universities have undergone significant institutional adjustments to incorporate sustainability into their core missions of education, research, and outreach. As universities worldwide increasingly engage in sustainability practices, the question arises: How do these sustainability endeavours correlate with academic performance on a global scale? This article, using data from the QS Sustainability Ranking and four prominent academic ranking (THEWUR, ARWU, QSWUR and USWUR), investigates this link. The study explores whether sustainability relates to the academic performance of universities, the validity of the relationship when academic scores of the four rankings are aggregated, and its dependence on country-level sustainability performance scores. Findings reveal that sustainability practices have a reflection on the university rankings, providing a global competitive advantage for universities. While this study incorporates aggregated scores as a methodological innovation addressing the lack of uniformity among ranking systems, it recommends the inclusion of university-level control variables (such as faculty expertise, university budget, infrastructure) and government and policy variables in future studies to ensure robustness of the results.