The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

This table contains 39 series, with data for starting from 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Artificial Intelligence in Regtech Market Outlook

The global Artificial Intelligence (AI) in Regtech market size was valued at approximately USD 7.8 billion in 2023 and is projected to reach around USD 34.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 18.3% during the forecast period. This robust growth is attributed to increasing regulatory scrutiny and the subsequent need for efficient compliance solutions. The market's expansion is reinforced by technological advancements in AI, which are enhancing the capabilities of Regtech solutions to address the ever-evolving regulatory landscape.

One of the primary growth factors driving the AI in Regtech market is the increasing complexity of regulatory requirements across various industries. Companies are continually faced with the challenge of staying compliant with a multitude of regulations that differ from country to country. This complexity necessitates the adoption of advanced technologies like AI to automate and streamline compliance processes. AI-powered Regtech solutions can analyze vast amounts of regulatory data and provide actionable insights, helping organizations mitigate risks and avoid costly penalties.

Another significant growth driver is the rise in financial crimes such as money laundering, fraud, and identity theft. Traditional methods of combating these issues are often inadequate due to their manual nature and the sheer volume of data that needs to be processed. AI in Regtech offers sophisticated tools for real-time monitoring, predictive analytics, and anomaly detection, enabling organizations to proactively identify and address fraudulent activities. Consequently, the increasing demand for robust fraud detection and prevention solutions is propelling market growth.

The growing emphasis on operational efficiency and cost reduction is also contributing to the market's expansion. AI technologies can automate routine compliance tasks, reducing the need for extensive human intervention and thereby lowering operational costs. Moreover, AI-driven Regtech solutions can deliver faster and more accurate results, enhancing overall efficiency. Organizations are increasingly recognizing the value of these benefits, leading to higher adoption rates of AI in Regtech solutions.

From a regional perspective, North America holds a significant share of the AI in Regtech market, driven by stringent regulatory frameworks and a high level of technological adoption. Europe is also a major market, owing to rigorous compliance requirements and strong financial sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid digital transformation and increasing regulatory pressures in countries like China and India. Latin America and the Middle East & Africa are also emerging markets, with growing awareness and investment in Regtech solutions.

Component Analysis

In the AI in Regtech market, the component segment is categorized into software, hardware, and services. The software segment dominates the market due to the extensive adoption of AI-powered compliance and risk management solutions. These software solutions offer capabilities such as data analytics, machine learning, and natural language processing, which are crucial for automating regulatory processes. Companies are increasingly investing in AI-driven software to enhance their compliance frameworks and manage regulatory challenges more effectively.

Hardware, though a smaller segment compared to software, plays a critical role in supporting the deployment of AI in Regtech solutions. High-performance computing hardware, such as GPUs and servers, is essential for running complex AI algorithms and processing large datasets. Organizations are investing in advanced hardware to ensure that their AI systems operate efficiently and deliver accurate results. The growth in cloud computing and edge computing technologies is also driving the demand for specialized hardware in the Regtech market.

Services constitute a vital component of the AI in Regtech market, encompassing consulting, implementation, and support services. As organizations adopt AI-powered Regtech solutions, they often require expert guidance to integrate these technologies into their existing systems. Consulting services help companies understand their regulatory requirements and devise effective compliance strategies. Implementation services assist in deploying and customizing AI solutions, while support services ensure the ongoing maintenance and optimization of these sy

Using two different samples – one based on newspaper advertisements, the other Internet-based – we identify some price anomalies in the used car market in the Netherlands. First, prices of used cars depend on their age in calendar years rather than months. Second, there is some evidence that crossing 100,000 km induces a sudden additional price reduction. Third, a new license plate format, something with no intrinsic value whatsoever, increases a car’s price by about 4%. We discuss possible explanations for these results.

This table contains 38 series, with data starting from 1957 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Rates (38 items: Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate; Forward premium or discount (-), United States dollars in Canada: 1 month; ...).

Enterprise Database Software Market Outlook

The global enterprise database software market size is expected to grow from USD 86.5 billion in 2023 to USD 145.8 billion by 2032, at a compound annual growth rate (CAGR) of 5.8% during the forecast period. The escalating demand for data-driven decision-making and advanced data analytics is a key growth factor for this market. Organizations are increasingly leveraging enterprise database software to streamline operations, ensure data integrity, and gain competitive advantages through predictive analytics and business intelligence.

One of the primary growth drivers for the enterprise database software market is the exponential growth in data generation across various industries. With the advent of the Internet of Things (IoT), social media, and cloud computing, data is being produced at an unprecedented rate. Enterprises are seeking robust and scalable database solutions to manage this influx of data efficiently. Additionally, the increasing importance of data compliance and security regulations, such as GDPR and CCPA, is pushing organizations to adopt advanced database management systems that offer enhanced data governance and protection features.

Another significant growth factor is the proliferation of cloud computing and the shift towards cloud-based database solutions. Cloud databases offer numerous benefits, including reduced total cost of ownership, high scalability, flexibility, and ease of use. As businesses continue to embrace digital transformation strategies, the demand for cloud-based database solutions is expected to soar. The integration of artificial intelligence and machine learning capabilities within these databases is further driving their adoption, enabling organizations to extract actionable insights from their data more efficiently and accurately.

The rise of big data analytics and the need for real-time data processing is also fueling the demand for enterprise database software. Organizations are increasingly relying on big data analytics to uncover hidden patterns, correlations, and trends within their data. This requires robust database solutions that can handle large volumes of data and support complex queries in real-time. The advent of in-memory database technology and advancements in database architectures, such as NoSQL and NewSQL, are addressing these requirements, driving the growth of the enterprise database software market.

Regionally, North America holds a significant share of the enterprise database software market, attributed to the presence of major technology players and early adoption of advanced database solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digitization of economies, increasing investment in IT infrastructure, and the growing emphasis on data-driven decision-making are contributing to this growth. Countries like China and India are emerging as key markets for enterprise database software, driven by the expanding industrial base and the proliferation of small and medium enterprises.

Deployment Type Analysis

In the deployment type segment, the enterprise database software market is categorized into on-premises and cloud-based solutions. On-premises deployment refers to database solutions installed and operated within an organization's own data centers. This traditional deployment model offers higher control over data and security, making it a preferred choice for industries with stringent compliance requirements, such as BFSI and healthcare. However, this model also involves significant upfront costs for hardware, software, and maintenance, which can be a barrier for small and medium enterprises.

The cloud-based deployment model, on the other hand, is witnessing rapid adoption due to its numerous advantages. Cloud databases eliminate the need for substantial capital investment in infrastructure, as they are hosted on the service provider's servers. This model offers high scalability, allowing organizations to scale their database resources up or down based on demand. Additionally, cloud databases facilitate remote access, enabling employees to access data from anywhere, thus supporting the growing trend of remote work. The pay-as-you-go pricing model of cloud databases also makes them an attractive option for small and medium enterprises looking to optimize their IT budgets.

The integration of advanced technologies, such as artificial intelligence and machine learning, within cloud databases is further propelling their adoption. These techn

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

According to Cognitive Market Research, the global GPU Database market size will be USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. Market Dynamics of GPU Database Market Key Drivers for GPU Database Market Growing Demand for High-Performance Computing in Various Data-Intensive Industries- One of the main reasons the GPU Database market is growing demand for high-performance computing (HPC) across various data-intensive industries. These industries, including finance, healthcare, and telecommunications, require rapid data processing and real-time analytics, which GPU databases excel at providing. Unlike traditional CPU databases, GPU databases leverage the parallel processing power of GPUs to handle complex queries and large datasets more efficiently. This capability is crucial for applications such as machine learning, artificial intelligence, and big data analytics. The expansion of data and the increasing need for speed and scalability in processing are pushing enterprises to adopt GPU databases. Consequently, the market is poised for robust growth as organizations continue to seek solutions that offer enhanced performance, reduced latency, and greater computational power to meet their evolving data management needs. The increasing demand for gaining insights from large volumes of data generated across verticals to drive the GPU Database market's expansion in the years ahead. Key Restraints for GPU Database Market Lack of efficient training professionals poses a serious threat to the GPU Database industry. The market also faces significant difficulties related to insufficient security options. Introduction of the GPU Database Market The GPU database market is experiencing rapid growth due to the increasing demand for high-performance data processing and analytics. GPUs, or Graphics Processing Units, excel in parallel processing, making them ideal for handling large-scale, complex data sets with unprecedented speed and efficiency. This market is driven by the proliferation of big data, advancements in AI and machine learning, and the need for real-time analytics across industries such as finance, healthcare, and retail. Companies are increasingly adopting GPU-accelerated databases to enhance data visualization, predictive analytics, and computational workloads. Key players in this market include established tech giants and specialized startups, all contributing to a competitive landscape marked by innovation and strategic partnerships. As organizations continue to seek faster and more efficient ways to harness their data, the GPU database market is poised for substantial growth, reshaping the future of data management and analytics.< /p>

Database Migration Service Market Outlook

The global Database Migration Service market size was valued at approximately USD 4.5 billion in 2023 and is expected to reach around USD 14 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 13.5% during the forecast period. The primary growth driver for this market is the increasing need for efficient management and migration of data across different platforms and systems, spurred by the growing adoption of cloud-based solutions among enterprises.

One of the major growth factors for the database migration service market is the rising adoption of cloud computing worldwide. Enterprises are increasingly moving their applications and databases to the cloud to leverage the benefits of scalability, flexibility, and cost reduction. Cloud-based database migration services are thus in high demand as they facilitate seamless and efficient data transfer from on-premises systems to cloud environments. The increasing use of cloud technologies across various industries is therefore a significant driver for the market.

Another contributing factor to the growth of this market is the rapid digital transformation across industries. Companies are modernizing their IT infrastructure to stay competitive, which often involves migrating legacy systems and databases to more modern and efficient platforms. This trend is particularly evident in sectors such as BFSI, healthcare, and retail, where large volumes of data need to be managed and utilized effectively. Database migration services enable these organizations to perform this transition smoothly, ensuring data integrity and minimizing downtime.

Furthermore, the growing trend of mergers and acquisitions among organizations is also boosting the demand for database migration services. When companies merge or acquire other businesses, they often face the challenge of integrating disparate systems and databases. Database migration services provide the necessary tools and expertise to merge these systems seamlessly, ensuring continuity of operations and data consistency. This factor is expected to continue driving the market growth in the coming years.

In the context of digital transformation, Application Modernization and Migration Service has emerged as a crucial enabler for businesses seeking to update their legacy systems. This service involves re-architecting, re-coding, and migrating applications to modern platforms, often leveraging cloud technologies. By doing so, organizations can enhance their operational efficiency, reduce costs, and improve scalability. The demand for these services is growing as companies recognize the need to stay competitive in an increasingly digital world. Application Modernization and Migration Service not only facilitates the seamless transition of applications but also ensures they are optimized for future technological advancements, thereby providing a strategic advantage.

Regionally, North America holds the largest share of the database migration service market, 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 expected to witness the highest growth rate during the forecast period, at a CAGR of 15%, due to the rapid digital transformation, increasing adoption of cloud services, and growing number of SMEs in countries like China and India.

Type Analysis

The database migration service market by type is segmented into cloud-based and on-premises. Cloud-based database migration services are gaining substantial traction due to their scalability, flexibility, and cost-effectiveness. These services allow enterprises to migrate their databases without the need for extensive hardware investments, providing a more streamlined and efficient approach to data management. The cloud-based segment is expected to witness significant growth, driven by the increasing adoption of cloud technologies across various industries.

Cloud-based solutions also offer the advantage of reduced downtime during migration. Traditional on-premises migrations can be time-consuming and disruptive, but cloud-based services enable organizations to migrate their databases with minimal impact on their daily operations. This feature is particularly beneficial for enterprises that require high availability and cannot afford prolonged downtime. Furthermore, cloud-bas

Enterprise Database Market Outlook

The enterprise database market size is projected to see significant growth over the coming years, with a valuation of USD 91.5 billion in 2023, and is expected to reach USD 171.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This growth is driven by the increasing demand for efficient data management solutions across various industries and the rise in digital transformation initiatives that require robust database systems. The growth factors include advancements in cloud computing, the growing need for real-time data analytics, and the integration of artificial intelligence and machine learning in data management.

One of the primary growth factors in the enterprise database market is the increasing adoption of cloud-based solutions. Organizations are rapidly moving towards cloud environments due to their scalability, cost-effectiveness, and flexibility. Cloud databases offer better accessibility and reduced infrastructure costs, making them an attractive option for businesses of all sizes. Additionally, with the proliferation of data generated from various sources such as social media, IoT devices, and online transactions, the need for scalable and efficient data storage solutions is more critical than ever. Cloud-based databases provide the requisite infrastructure to handle this data surge efficiently, further propelling market growth.

Another significant driver for the enterprise database market is the rise of big data analytics. As businesses strive to harness the power of data for insights and decision-making, the demand for robust database systems capable of handling large volumes of data has intensified. Enterprises are looking for databases that not only store data but also enable advanced analytics to derive actionable insights. This trend is particularly prevalent in industries like retail, healthcare, and BFSI, where data-driven decisions can lead to improved customer experiences, better risk management, and optimized operations. The integration of artificial intelligence and machine learning with enterprise databases is further enhancing their capabilities, allowing for predictive analytics and automating data processing tasks.

The growing emphasis on data security and compliance is also contributing to the expansion of the enterprise database market. With the increasing incidences of data breaches and stringent regulatory requirements, organizations are prioritizing secure database solutions that offer robust data protection measures. Databases with built-in security features such as encryption, access control, and regular auditing are in high demand. Furthermore, industry-specific compliance standards like GDPR in Europe and HIPAA in the US are driving businesses to invest in databases that ensure compliance and mitigate the risk of penalties, thus fueling market growth.

Regionally, North America is expected to dominate the enterprise database market due to the presence of major technology companies and early adoption of advanced technologies. The Asia Pacific region, however, is anticipated to witness the fastest growth rate during the forecast period, driven by rapid industrialization, the proliferation of SMEs, and increasing investments in digital infrastructure by countries like China, India, and Japan. The growing focus on smart cities and digital transformation initiatives in these countries is further boosting the demand for enterprise databases. Europe also holds a significant share of the market, with widespread adoption of cloud technologies and heightened focus on data privacy and security driving market expansion.

Industrial Databases play a crucial role in the enterprise database market, particularly as industries undergo digital transformation. These databases are designed to manage and process large volumes of industrial data generated from various sources such as manufacturing processes, supply chain operations, and IoT devices. The ability to handle real-time data analytics and provide actionable insights is essential for industries aiming to optimize operations and enhance productivity. As industries continue to adopt smart manufacturing practices, the demand for industrial databases that offer scalability, reliability, and integration with advanced technologies like AI and machine learning is on the rise. This trend is expected to contribute significantly to the growth of the enterprise database market, as businesses seek to leverage data for competitive advantage and operational efficiency.

Dataset Description Title: Electricity Market Dataset for Long-Term Forecasting (2018–2024)

Overview: This dataset provides a comprehensive collection of electricity market data, focusing on long-term forecasting and strategic planning in the energy sector. The data is derived from real-world electricity market records and policy reports from Germany, specifically the Frankfurt region, a major European energy hub. It includes hourly observations spanning from January 1, 2018, to December 31, 2024, covering key economic, environmental, and operational factors that influence electricity market dynamics. This dataset is ideal for predictive modeling tasks such as electricity price forecasting, renewable energy integration planning, and market risk assessment.

Features Description Feature Name Description Type Timestamp The timestamp for each hourly observation. Datetime Historical_Electricity_Prices Hourly historical electricity prices in the Frankfurt market. Continuous (Float) Projected_Electricity_Prices Forecasted electricity prices (short, medium, long term). Continuous (Float) Inflation_Rates Hourly inflation rate trends impacting energy markets. Continuous (Float) GDP_Growth_Rate Hourly GDP growth rate trends for Germany. Continuous (Float) Energy_Market_Demand Hourly electricity demand across all sectors. Continuous (Float) Renewable_Investment_Costs Investment costs (capital and operational) for renewable energy projects. Continuous (Float) Fossil_Fuel_Costs Costs for fossil fuels like coal, oil, and natural gas. Continuous (Float) Electricity_Export_Prices Prices for electricity exports from Germany to neighboring regions. Continuous (Float) Market_Elasticity Sensitivity of electricity demand to price changes. Continuous (Float) Energy_Production_By_Solar Hourly solar energy production. Continuous (Float) Energy_Production_By_Wind Hourly wind energy production. Continuous (Float) Energy_Production_By_Coal Hourly coal-based energy production. Continuous (Float) Energy_Storage_Capacity Available storage capacity (e.g., batteries, pumped hydro). Continuous (Float) GHG_Emissions Hourly greenhouse gas emissions from energy production. Continuous (Float) Renewable_Penetration_Rate Percentage of renewable energy in total energy production. Continuous (Float) Regulatory_Policies Categorical representation of regulatory impact on electricity markets (e.g., Low, Medium, High). Categorical Energy_Access_Data Categorization of energy accessibility (Urban or Rural). Categorical LCOE Levelized Cost of Energy by source. Continuous (Float) ROI Return on investment for energy projects. Continuous (Float) Net_Present_Value Net present value of proposed energy projects. Continuous (Float) Population_Growth Population growth rate trends impacting energy demand. Continuous (Float) Optimal_Energy_Mix Suggested optimal mix of renewable, non-renewable, and nuclear energy. Continuous (Float) Electricity_Price_Forecast Predicted electricity prices based on various factors. Continuous (Float) Project_Risk_Analysis Categorical analysis of project risks (Low, Medium, High). Categorical Investment_Feasibility Indicator of the feasibility of energy investments. Continuous (Float) Use Cases Electricity Price Forecasting: Utilize historical and projected price trends to predict future electricity prices. Project Risk Classification: Categorize projects into risk levels for better decision-making. Optimal Energy Mix Analysis: Analyze the balance between renewable, non-renewable, and nuclear energy sources. Policy Impact Assessment: Study the effect of regulatory and market policies on energy planning. Long-Term Strategic Planning: Provide insights into investment feasibility, GHG emission reduction, and energy market dynamics. Acknowledgment This dataset is based on publicly available records and market data specific to the Frankfurt region, Germany. The dataset is designed for research and educational purposes in energy informatics, computational intelligence, and long-term forecasting.

Distributed Relational Database Market Outlook

The global distributed relational database market is projected to witness substantial growth, with its market size anticipated to expand from USD 8.5 billion in 2023 to approximately USD 24.7 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.5%. This growth is primarily driven by the increasing need for scalable and high-performance database solutions across various industries. The adoption of distributed relational databases is fueled by the growing volume of data and the need for real-time analytics, which are essential for intelligent decision-making processes in today's competitive business landscape.

One of the primary growth factors for the distributed relational database market is the escalating demand for big data and analytics. Organizations across sectors are increasingly relying on data-driven insights to enhance operational efficiency, improve customer experience, and gain a competitive edge. Distributed relational databases offer the scalability and flexibility required to handle vast amounts of data and perform complex queries efficiently, making them an indispensable tool for businesses looking to leverage big data. Moreover, the integration of artificial intelligence and machine learning algorithms with distributed databases is further enhancing their analytical capabilities, providing deeper and more actionable insights.

Another significant factor contributing to the market growth is the rising adoption of cloud computing. Cloud-based distributed relational databases provide numerous advantages, including cost-efficiency, scalability, and ease of access. Small and medium enterprises (SMEs), in particular, are increasingly opting for cloud solutions to avoid the high upfront costs associated with on-premises infrastructure. Additionally, the flexibility to scale resources up or down based on demand, coupled with the reduced need for in-house IT management, makes cloud deployment an attractive option for organizations of all sizes. The ongoing advancements in cloud technology and the increasing number of cloud service providers are expected to further drive the adoption of cloud-based distributed relational databases.

The market is also being propelled by the growing need for high availability and disaster recovery solutions. In today's digital era, downtime can lead to significant financial losses and damage to an organization's reputation. Distributed relational databases provide robust replication and failover mechanisms that ensure data availability and integrity, even in the event of hardware failures or other disruptions. This is particularly crucial for industries such as banking, financial services, and insurance (BFSI), healthcare, and retail, where uninterrupted access to data is critical for day-to-day operations. The emphasis on business continuity and the need to comply with stringent regulatory requirements are also driving the adoption of distributed relational databases.

The role of Enterprise DBMS in the distributed relational database market is becoming increasingly prominent. As organizations strive to manage vast amounts of data efficiently, the demand for robust database management systems that can support complex queries and ensure data integrity is on the rise. Enterprise DBMS solutions are designed to handle large-scale data processing and storage needs, providing businesses with the tools necessary to manage their data effectively. These systems offer advanced features such as automated backup, recovery, and security measures, which are crucial for maintaining data availability and compliance with regulatory standards. The integration of Enterprise DBMS with distributed relational databases enhances their scalability and performance, making them an indispensable component for organizations seeking to leverage big data and analytics.

Regionally, North America holds the largest share in the distributed relational database market, driven by the presence of major technology companies and a high level of digitalization across industries. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid adoption of advanced technologies, increasing investments in IT infrastructure, and the growing focus on data-driven decision-making. The expansion of cloud services and the rising number of startups and SMEs in countries like China and India are also contributing to the market growth in this region.

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

The benchmark interest rate in Indonesia was last recorded at 5.50 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Crypto Fear and Greed Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/adelsondias/crypto-fear-and-greed-index on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Crypto Fear and Greed Index

Each day, the website https://alternative.me/crypto/fear-and-greed-index/ publishes this index based on analysis of emotions and sentiments from different sources crunched into one simple number: The Fear & Greed Index for Bitcoin and other large cryptocurrencies.

Why Measure Fear and Greed?

The crypto market behaviour is very emotional. People tend to get greedy when the market is rising which results in FOMO (Fear of missing out). Also, people often sell their coins in irrational reaction of seeing red numbers. With our Fear and Greed Index, we try to save you from your own emotional overreactions. There are two simple assumptions:

• Extreme fear can be a sign that investors are too worried. That could be a buying opportunity.
• When Investors are getting too greedy, that means the market is due for a correction.

Therefore, we analyze the current sentiment of the Bitcoin market and crunch the numbers into a simple meter from 0 to 100. Zero means "Extreme Fear", while 100 means "Extreme Greed". See below for further information on our data sources.

Data Sources

We are gathering data from the five following sources. Each data point is valued the same as the day before in order to visualize a meaningful progress in sentiment change of the crypto market.

First of all, the current index is for bitcoin only (we offer separate indices for large alt coins soon), because a big part of it is the volatility of the coin price.

But let’s list all the different factors we’re including in the current index:

Volatility (25 %)

We’re measuring the current volatility and max. drawdowns of bitcoin and compare it with the corresponding average values of the last 30 days and 90 days. We argue that an unusual rise in volatility is a sign of a fearful market.

Market Momentum/Volume (25%)

Also, we’re measuring the current volume and market momentum (again in comparison with the last 30/90 day average values) and put those two values together. Generally, when we see high buying volumes in a positive market on a daily basis, we conclude that the market acts overly greedy / too bullish.

Social Media (15%)

While our reddit sentiment analysis is still not in the live index (we’re still experimenting some market-related key words in the text processing algorithm), our twitter analysis is running. There, we gather and count posts on various hashtags for each coin (publicly, we show only those for Bitcoin) and check how fast and how many interactions they receive in certain time frames). A unusual high interaction rate results in a grown public interest in the coin and in our eyes, corresponds to a greedy market behaviour.

Surveys (15%) currently paused

Together with strawpoll.com (disclaimer: we own this site, too), quite a large public polling platform, we’re conducting weekly crypto polls and ask people how they see the market. Usually, we’re seeing 2,000 - 3,000 votes on each poll, so we do get a picture of the sentiment of a group of crypto investors. We don’t give those results too much attention, but it was quite useful in the beginning of our studies. You can see some recent results here.

Dominance (10%)

The dominance of a coin resembles the market cap share of the whole crypto market. Especially for Bitcoin, we think that a rise in Bitcoin dominance is caused by a fear of (and thus a reduction of) too speculative alt-coin investments, since Bitcoin is becoming more and more the safe haven of crypto. On the other side, when Bitcoin dominance shrinks, people are getting more greedy by investing in more risky alt-coins, dreaming of their chance in next big bull run. Anyhow, analyzing the dominance for a coin other than Bitcoin, you could argue the other way round, since more interest in an alt-coin may conclude a bullish/greedy behaviour for that specific coin.

Trends (10%)

We pull Google Trends data for various Bitcoin related search queries and crunch those numbers, especially the change of search volumes as well as recommended other currently popular searches. For example, if you check Google Trends for "Bitcoin", you can’t get much information from the search volume. But currently, you can see that there is currently a +1,550% rise of the query „bitcoin price manipulation“ in the box of related search queries (as of 05/29/2018). This is clearly a sign of fear in the market, and we use that for our index.

There's a story behind every dataset and here's your opportunity to share yours.

Copyright disclaimer

This dataset is produced and maintained by the administrators of https://alternative.me/crypto/fear-and-greed-index/.

This published version is an unofficial copy of their data, which can be also collected using their API (e.g., GET https://api.alternative.me/fng/?limit=10&format=csv&date_format=us).

--- Original source retains full ownership of the source dataset ---

Explore a diverse range of fashion items, home goods, and more, with insights into pricing, availability, ratings, and reviews. Popular use cases include trend forecasting, pricing optimization, and inventory management in the fast-fashion market.

The Shein.com Products dataset provides a detailed overview of the extensive product range available on Shein, offering key insights into the fast-fashion market. This dataset includes essential details such as product names, prices, discounts, descriptions, materials, product images, SKUs (Stock Keeping Units), low-stock indicators, and more.

Ideal for eCommerce professionals, fashion analysts, and market strategists, this dataset supports trend analysis, pricing strategies, and inventory management. Whether you're benchmarking competitors, identifying emerging trends, or optimizing your product offerings, the Shein.com Products dataset delivers valuable insights to stay ahead in the dynamic fashion industry.

Dataset Features

• product_name: The name/title of the product listed.
• description: A brief description of the product, including features or materials.
• initial_price: The original price of the product before any discounts.
• final_price: The actual selling price after applying discounts.
• currency: The currency in which the price is listed (e.g., USD).
• in_stock: Availability status of the product (True if in stock, otherwise False).
• color: Available color(s) for the product.
• size: Size(s) available (e.g., S, M, L, or custom sizes).
• reviews_count: Number of user reviews the product has received.
• main_image: URL to the primary product image.
• category_url: Link to the category page the product belongs to.
• url: Direct link to the product page.
• category_tree: Hierarchical path of the product category.
• country_code: Country code indicating where the product is available.
• domain: The Shein domain where the product was found (e.g., shein.com, shein.uk).
• image_count: Total number of product images.
• image_urls: List/array of URLs for all images related to the product.
• model_number: The product’s model or SKU number.
• offers: Details of promotions or discounts available.
• other_attributes: Miscellaneous product features or labels (e.g., eco-friendly, plus-size).
• product_id: Unique identifier for the product.
• rating: Average user rating (typically on a 5-star scale).
• related_products: List of similar or related products.
• root_category: The broadest category classification (e.g., "Women", "Home").
• top_reviews: Highlighted customer reviews.
• category: Specific product category (e.g., "Bikinis", "T-Shirts").
• brand: Brand name (often "Shein" or sub-brands).
• all_available_sizes: List of all size options for the product.
• category_details: Additional metadata about the product category.
• initial_price_usd: Original price converted to USD.
• final_price_usd: Final price converted to USD.
• discount_price: Price discount amount (initial - final).
• discount_price_usd: Discount amount in USD.
• colors: All color variants of the product.
• store_details: Information about the store or seller.
• shipping_details: Information about shipping costs and delivery time.
• shipping_type: Type of shipping offered (e.g., standard, express).
• product_parent_id: ID representing a grouped product variant.
• tags: Keywords or tags associated with the product.
• model_data: Additional attributes from the product model (could include fit, cut, etc.).

Distribution

• Data Volume: 40 Columns and 42.35 M Rows
• Format: CSV

Usage

This dataset is ideal for a wide range of practical and analytical applications: - Trend Forecasting: Identify emerging fashion trends based on product popularity and review sentiment.
- Pricing Optimization: Analyze discount strategies and dynamic pricing patterns.
- Inventory Management: Monitor stock availability and detect low-stock patterns.
- Recommendation Systems: Build personalized fashion recommendations using product attributes and user ratings.
- Market Benchmarking: Compare Shein's offerings with competitors or across regions.
- Computer Vision: Use product images for training models in visual fashion recognition.

Coverage

• Geographic Coverage: Global
• Time Range: Varies by data collection; generally recent and can be updated periodically.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For training ML models like price predictors, review sentiment classifiers, or image-based search engines.
• Researchers: