Redis is a cloud-based data store that enables businesses to build fast and scalable applications. Founded in 2005 by Salvatore Sanfilippo, Redis is designed to be an in-memory NoSQL database that can be used as a document store, graph database, message broker, or as a distributed lock system. With its ability to handle high traffic and large datasets, Redis is trusted by companies such as Facebook, Instagram, and Niantic to power their applications.

Redis provides a range of features, including caching, sessions, and real-time analytics, making it an ideal solution for applications that require high performance and scalability. With its cloud-based infrastructure, Redis simplifies the process of building and deploying applications, allowing developers to focus on creating value rather than managing infrastructure.

Introduction This dataset shows an anonymised list of live, committed, import-related projects within UK Power Networks' licence areas. This includes demand-only projects that are 5,000 kilovolt-amperes (kVA) and above, as well as battery energy storage systems (BESS).

This list has been determined using internal systems UK Power Networks uses to manage all committed projects in the process of connecting to our network. To protect the identity of the sites, entries have been anonymised and only the licence area, the grid supply point the project is connecting at (or under), rounded requested import capacity, and application date have been provided.

Methodological Approach Live, committed demand projects are identified through desktop exercises using UK Power Networks' internal customer relationship management system and extracted.

The projects are then filtered to only show projects where The required import capacity is more than or equal to 5,000kVAThe required export capacity is 0MVA.

These project entries are then cross-referenced with other sources to verify its status. Any discrepancies are manually reviewed and kept/omitted as appropriate.To protect the identity of the demand projects the required import capacity is rounded, and the project names are anonymised by providing an arbitrary sequential number.

Quality Control Statement The dataset is primarily built upon internal data, relating to live demand projects in UK Power Networks' licence areas. Information about battery energy storage systems are taken from existing datasets relating to Appendix G information UK Power Networks manages.Data have been checked with both automatic and manual validation methods.

Assurance Statement The dataset is generated through a manual process, conducted by the Distribution System Operator's Regional Development Team. The dataset will be reviewed monthly to assess any changes, and to determine if any updates to the methodology are necessary. This process ensures that the dataset remains relevant and reflective of the live large demand projects UK Power Networks is working on. There are sufficient projects per licence area to assure anonymity of projects.While all reasonable efforts have been made to ensure the accuracy of the information provided in this dataset, neither the licensee nor any of its directors or employees is under any liability for any errors, or for any misstatement on which a user of the data seeks to rely. Please view our Terms and Conditions for more information.The data provided constitutes UK Power Networks’ provisional view of the status at this GSP at the date of publication and is for general information only.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/For prospective customers considering a connection to our network, we provide pre-application support on our website to make the connection journey as smooth as possible: Pre-application support and advice | UK Power NetworksWe also offer an "Ask the Expert" service, designed for some of your more complex connection questions that go beyond our FAQs. You can request an "Ask the Expert" surgery session, where our specialists can provide more specific technical guidance: Ask the Expert | UK Power NetworksTo view this data please register and login.

NoSQL Database Market Outlook

According to our latest research, the global NoSQL database market size reached USD 9.8 billion in 2024, reflecting robust industry momentum driven by the exponential growth of unstructured and semi-structured data across enterprises. The market is experiencing a remarkable compound annual growth rate (CAGR) of 20.7% and is forecasted to attain a value of USD 63.6 billion by 2033. This exceptional growth trajectory is primarily fueled by the surging demand for scalable, flexible, and high-performance database solutions that can support modern application requirements, especially in the era of big data, real-time analytics, and cloud computing.

A key growth factor in the NoSQL database market is the rapid proliferation of digital transformation initiatives across industries. Organizations are increasingly generating vast volumes of data from diverse sources such as social media, IoT devices, mobile applications, and e-commerce platforms. Traditional relational database management systems (RDBMS) often struggle to accommodate the scale, variety, and velocity of this data, which has led to a pronounced shift toward NoSQL solutions. NoSQL databases provide the flexibility to store, process, and analyze both structured and unstructured data without the rigid schema constraints of RDBMS, enabling businesses to derive actionable insights and enhance decision-making processes. This adaptability is particularly crucial for industries like retail, finance, and healthcare, where real-time customer engagement and data-driven services are key competitive differentiators.

Another significant driver propelling the NoSQL database market is the growing adoption of cloud computing and the increasing need for highly available, distributed database architectures. Cloud-based NoSQL solutions offer organizations the ability to scale resources dynamically, reduce infrastructure costs, and ensure high availability and disaster recovery capabilities. As enterprises embrace hybrid and multi-cloud strategies, NoSQL databases have become integral to supporting mission-critical workloads, global application deployments, and seamless data integration across disparate environments. The rise of microservices and containerized applications has further accelerated the demand for NoSQL databases, as these architectures require agile, horizontally scalable data storage solutions to meet the evolving needs of modern businesses.

The emergence of advanced analytics, artificial intelligence (AI), and machine learning (ML) applications is further amplifying the demand for NoSQL database market solutions. These technologies require the ability to ingest, process, and analyze massive datasets in real time, often with complex relationships and diverse data types. NoSQL databases, with their support for flexible data models and high-throughput operations, are uniquely positioned to power next-generation analytics and AI-driven applications. This trend is particularly evident in sectors such as BFSI, healthcare, and telecommunications, where organizations are leveraging NoSQL databases to enhance fraud detection, personalize customer experiences, and optimize operational efficiencies. The ongoing evolution of data privacy regulations and the need for secure, compliant data management practices further reinforce the strategic importance of NoSQL solutions in the global data ecosystem.

Managed Couchbase Services have emerged as a vital component for organizations seeking to leverage the power of NoSQL databases without the overhead of managing complex infrastructure. These services provide a fully managed environment where businesses can focus on application development and data-driven strategies while leaving the operational aspects to the service provider. With features such as automated scaling, high availability, and robust security, Managed Couchbase Services enable enterprises to efficiently handle large volumes of data and deliver real-time analytics. This approach is particularly beneficial for industries with fluctuating workloads and stringent performance requirements, allowing them to remain agile and competitive in a rapidly evolving market landscape.

From a regional perspective, North America continues to dominate the NoSQL database market, accounting for the largest revenue share in 2024, followed clos

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.

Vector Database as a Service Market Outlook

According to our latest research, the global Vector Database as a Service (DBaaS) market size reached USD 1.12 billion in 2024, driven by surging demand for AI-powered applications and data-intensive workloads. The market is expected to grow at a robust CAGR of 27.4% from 2025 to 2033, with the market size projected to reach USD 9.41 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of machine learning, generative AI, and advanced semantic search technologies across industries, as organizations seek scalable, real-time data solutions to power next-generation applications.

The primary growth factor for the Vector Database as a Service market is the exponential rise in unstructured and high-dimensional data generated by enterprises. Organizations across sectors such as BFSI, healthcare, e-commerce, and telecommunications are increasingly leveraging AI-driven applications that require rapid, accurate similarity search and retrieval from massive datasets. Traditional relational databases are ill-suited for these workloads, prompting a shift toward vector databases that can handle embeddings and facilitate real-time semantic search. This technological shift is further amplified by the proliferation of large language models (LLMs) and generative AI, both of which inherently depend on vector representations and require robust, scalable vector data infrastructure.

Another significant driver is the growing adoption of cloud-based solutions and managed services. Enterprises are rapidly moving away from on-premises database management due to the high costs, complexity, and lack of scalability associated with traditional systems. Vector Database as a Service enables organizations to deploy, scale, and manage high-performance vector databases with minimal operational overhead, allowing them to focus on core business and innovation. The pay-as-you-go pricing models and seamless integration with cloud-native AI/ML workflows further enhance the appeal of DBaaS offerings. This trend is particularly pronounced among small and medium enterprises (SMEs) that lack the resources for in-house data infrastructure but require advanced capabilities to stay competitive.

The increasing focus on personalized user experiences and intelligent automation is also propelling the Vector Database as a Service market. Recommendation engines, semantic search platforms, fraud detection systems, and advanced analytics all rely on the ability to process and analyze high-dimensional vectors in real time. As organizations strive to deliver hyper-personalized content and services, the need for scalable, low-latency vector search capabilities becomes paramount. This demand is further bolstered by the rise of hybrid and multi-cloud environments, where DBaaS solutions offer flexibility, reliability, and seamless integration across diverse IT landscapes. As a result, the market is witnessing heightened investment from both established technology vendors and innovative startups aiming to capture a share of this rapidly expanding landscape.

Regionally, North America remains at the forefront of the Vector Database as a Service market, accounting for the largest share in 2024 due to the early adoption of AI technologies, strong presence of leading cloud providers, and a mature digital ecosystem. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, burgeoning AI research, and increasing investments in cloud infrastructure. Europe is also witnessing significant growth, supported by stringent data regulations and a growing focus on enterprise AI adoption. Latin America and the Middle East & Africa are gradually catching up, with local enterprises and governments recognizing the value of advanced vector data solutions for economic modernization and digital competitiveness.

Offering Analysis

The Vector Database as a Service market is segmented by offering into Solutions and Services. Solutions encompass the core vector database platforms, APIs, and software tools that enable organizations to store, index, and search high-dimensional vectors at scale. These offerings are rapidly evolving to support advanced features such as hybrid search (combining vector and keyword search), real-time analytics, and integration with popular AI/ML frameworks. As enterprises increasingly demand seamless, end-to-end data pipelin

Synthetic Data Generation for Analytics Market Outlook

According to our latest research, the synthetic data generation for analytics market size reached USD 1.7 billion in 2024, with a robust year-on-year expansion reflecting the surging adoption of advanced analytics and AI-driven solutions. The market is projected to grow at a CAGR of 32.8% from 2025 to 2033, culminating in a forecasted market size of approximately USD 22.5 billion by 2033. This remarkable growth is primarily fueled by escalating data privacy concerns, the exponential rise of machine learning applications, and the growing need for high-quality, diverse datasets to power analytics in sectors such as BFSI, healthcare, and IT. As per our latest research, these factors are reshaping how organizations approach data-driven innovation, making synthetic data generation a cornerstone of modern analytics strategies.

A critical growth driver for the synthetic data generation for analytics market is the intensifying focus on data privacy and regulatory compliance. With the enforcement of stringent data protection laws such as GDPR in Europe, CCPA in California, and similar frameworks globally, organizations face mounting challenges in accessing and utilizing real-world data for analytics without risking privacy breaches or non-compliance. Synthetic data generation addresses this issue by creating artificial datasets that closely mimic the statistical properties of real data while stripping away personally identifiable information. This enables enterprises to continue innovating in analytics, machine learning, and AI development without compromising user privacy or running afoul of regulatory mandates. The increasing adoption of privacy-by-design principles across industries further propels the demand for synthetic data solutions, as organizations seek to future-proof their analytics pipelines against evolving legal landscapes.

Another significant factor accelerating market growth is the explosive demand for training data in machine learning and AI applications. As enterprises across sectors such as healthcare, finance, automotive, and retail harness AI to drive automation, personalization, and predictive analytics, the need for large, high-quality, and diverse datasets has never been greater. However, sourcing, labeling, and managing real-world data is often expensive, time-consuming, and fraught with ethical and logistical challenges. Synthetic data generation platforms offer a scalable and cost-effective alternative, enabling organizations to create virtually unlimited datasets tailored to specific use cases, edge scenarios, or rare events. This capability not only accelerates model development cycles but also enhances model robustness and generalizability, giving companies a decisive edge in the competitive analytics landscape.

Furthermore, the market is witnessing rapid technological advancements, including the integration of generative adversarial networks (GANs), advanced simulation techniques, and domain-specific synthetic data engines. These innovations have significantly improved the fidelity, realism, and utility of synthetic datasets across various data types, including tabular, image, text, video, and time series data. The rise of cloud-native synthetic data platforms and the proliferation of APIs and developer tools have democratized access to these technologies, making it easier for organizations of all sizes to experiment with and deploy synthetic data solutions. As a result, the synthetic data generation for analytics market is marked by increasing vendor activity, strategic partnerships, and venture capital investment, further fueling its expansion across regions and industry verticals.

Regionally, North America remains the largest and most mature market, driven by early technology adoption, robust R&D investments, and the presence of leading AI and analytics companies. However, Asia Pacific is emerging as the fastest-growing region, with countries like China, India, and Japan ramping up investments in digital transformation, smart manufacturing, and healthcare analytics. Europe follows closely, buoyed by strong regulatory frameworks and a vibrant ecosystem of AI startups. The Middle East & Africa and Latin America are also witnessing increased adoption, albeit at a more nascent stage, as governments and enterprises recognize the value of synthetic data in overcoming data scarcity and privacy chal