The global vector database market is anticipated to reach a value of 20.05 billion in 2033, exhibiting a CAGR of 23.7% from 2025 to 2033. The rising adoption of artificial intelligence (AI) and machine learning (ML) technologies, particularly in the BFSI, retail and e-commerce, healthcare and life sciences, and IT and ITeS sectors, is a major driver of market growth. Furthermore, the increasing need for efficient data storage and retrieval in a variety of applications, such as natural language processing (NLP), computer vision, and recommendation systems, is further boosting market expansion. The Asia Pacific region is expected to hold a significant share of the vector database market, with key countries such as China, India, and Japan contributing to its growth. The region's burgeoning IT and ITeS sector, as well as its rapidly growing e-commerce market, are driving the demand for vector databases. Additionally, government initiatives in various countries aimed at promoting AI adoption are creating favorable conditions for market growth. The presence of major technology companies in the region, such as Alibaba Cloud, Pinecone Systems, and Zilliz, is also contributing to the market's expansion. This report provides an in-depth analysis of the Vector Database Market, a rapidly growing segment of the database industry valued at USD 1.5 billion in 2023 and projected to reach USD 10.2 billion by 2028, exhibiting a CAGR of 36.1% during the forecast period. Recent developments include: In June 2024, Salesforce, Inc. announced the general availability of the Data Cloud Vector Database, designed to help businesses unify and leverage the 90% of customer data trapped in unstructured formats, such as PDFs, emails, and transcripts. This innovation enables businesses to cost-effectively deliver transformative and integrated customer experiences across service, sales, marketing, AI, automation, and analytics , In June 2024, Oracle launched HeatWave GenAI, the first in-database large language model, scale-out vector processing, automated in-database vector store, and contextual natural language conversations informed by unstructured content. These capabilities let customers apply generative AI to enterprise data without moving data to a separate vector database or needing AI expertise , In April 2024, Vultr partnered with Qdrant, an advanced vector database technology provider, through their Cloud Alliance program to enhance cloud infrastructure and support the growing AI ecosystem. This collaboration combines Qdrant's innovative technology with Vultr's global platform, offering seamless scalability and performance for vector search workloads .

The vector database market is experiencing rapid growth, driven by the increasing adoption of AI-powered applications across diverse sectors. The market's expansion is fueled by the need for efficient similarity search and retrieval in large-scale datasets, particularly within applications like natural language processing (NLP), computer vision, and recommender systems. The rising volume of unstructured data and the demand for real-time insights are further propelling market growth. Open-source databases are gaining traction due to their flexibility and cost-effectiveness, while commercial databases offer advanced features and robust support, catering to enterprise-level requirements. Key players are strategically investing in research and development to enhance performance, scalability, and integration capabilities, fostering competition and innovation within the ecosystem. Geographic expansion is also a significant factor, with North America and Asia Pacific currently leading the market, followed by Europe, and other regions experiencing increasing adoption. We estimate the 2025 market size at $500 million, with a Compound Annual Growth Rate (CAGR) of 25% projected through 2033. This growth is anticipated to be driven by continued advancements in AI technologies and the expanding application of vector databases across various industry verticals. The competitive landscape is highly dynamic, with a mix of established technology giants like Alibaba Cloud and Tencent Cloud alongside innovative startups such as Pinecone, Weaviate, and Qdrant. These companies are constantly striving to improve their offerings, focusing on areas such as query performance, ease of integration with existing systems, and the development of specialized features for specific application domains. The market is also witnessing a convergence of technologies, with vector databases increasingly integrating with other database types and cloud platforms. This trend simplifies deployment and management, further accelerating market adoption. Future growth will likely be shaped by the development of more efficient indexing techniques, advancements in hardware acceleration, and the expanding use of vector databases in emerging AI applications such as generative AI and large language models.

Vector Database Market Outlook

According to our latest research, the global vector database market size reached USD 1.12 billion in 2024, demonstrating robust momentum driven by the surging adoption of artificial intelligence and machine learning applications. The market is experiencing a remarkable expansion, registering a CAGR of 22.4% from 2025 to 2033. By 2033, the market is forecasted to reach USD 8.43 billion, underscoring the transformative role of vector databases in powering next-generation data-driven solutions. This extraordinary growth trajectory is fueled by the increasing need for high-performance search and analytics capabilities across industries, as organizations pivot towards leveraging unstructured and semi-structured data for strategic advantage.

A primary growth factor for the vector database market is the exponential increase in the volume and complexity of unstructured data generated by enterprises. As organizations accumulate vast amounts of images, videos, text, and other rich media, traditional relational databases struggle to provide the speed and scalability required for real-time analysis and retrieval. Vector databases, designed specifically to handle high-dimensional vector representations, have become essential for enabling advanced search and recommendation systems. The proliferation of AI-powered applications, such as semantic search, natural language processing, and image recognition, is amplifying the demand for vector databases, as these systems rely on vector embeddings to deliver accurate and contextually relevant results. Furthermore, the integration of vector databases with popular machine learning frameworks is streamlining the development and deployment of intelligent solutions, accelerating market adoption.

Another significant driver is the rapid digital transformation across key verticals, including BFSI, healthcare, retail and e-commerce, and IT and telecommunications. Enterprises in these sectors are increasingly leveraging vector databases to enhance customer experiences, improve operational efficiency, and unlock new revenue streams. For instance, in retail and e-commerce, vector databases power personalized recommendation engines and visual search capabilities, driving higher conversion rates and customer satisfaction. In healthcare, they enable advanced medical image analysis and patient data retrieval, supporting better diagnostics and treatment outcomes. The growing emphasis on data-driven decision-making and the need to derive actionable insights from complex datasets are compelling organizations to invest in vector database technologies, further propelling market growth.

The evolution of deployment models and the rise of cloud-native architectures have also contributed to the expansion of the vector database market. Organizations are increasingly opting for cloud-based vector database solutions to benefit from scalability, flexibility, and cost efficiency. Cloud deployment enables seamless integration with existing IT infrastructure and allows enterprises to scale resources dynamically based on workload demands. This shift is particularly pronounced among small and medium enterprises (SMEs), which often lack the capital and expertise to maintain on-premises infrastructure. The availability of managed vector database services from major cloud providers is lowering the barrier to entry, democratizing access to advanced data management capabilities, and fueling widespread adoption across diverse industry segments.

From a regional perspective, North America continues to dominate the vector database market, driven by the presence of leading technology companies, robust investment in AI research, and early adoption of advanced data management solutions. However, Asia Pacific is emerging as the fastest-growing region, with a burgeoning digital economy, rapid technological advancements, and increasing focus on innovation. Europe is also witnessing substantial growth, supported by strong regulatory frameworks and a thriving startup ecosystem. The global landscape is characterized by dynamic competition, with vendors innovating rapidly to address evolving customer needs and capitalize on new opportunities.

The global market for vector databases for generative AI applications is expected to grow from $600 million in 2025 to $2.5 billion by 2033, at a CAGR of 13.3%. The growth of this market is being driven by the increasing adoption of generative AI models, which require large amounts of data to train. Vector databases are well-suited for storing and processing this data because they can efficiently handle high-dimensional data and support complex queries. The key players in the vector database market for generative AI applications include Zilliz Cloud, Redis, Pinecone, Weaviate, Canonical, OpenSearch, MongoDB, Elastic, Marko, Milvus, Snorkel AI, Qdrant, Oracle, Microsoft, AWS, Deep Lake, Fauna, and Vespa. These companies offer a range of vector database solutions that can be tailored to the specific needs of generative AI applications. The market is also expected to see increased competition from open source vector database solutions, such as MILvus and Weaviate.

Market Size and Trends: The global vector database software market is expected to reach a value of USD XX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. This growth is driven by the increasing demand for efficient data management and analytics in various industries. The rise of IoT, big data, and machine learning applications has created a need for databases that can handle large volumes of complex, unstructured data. Vector databases, with their ability to store and process data in multiple dimensions, are becoming increasingly popular in these applications. Key Players and Segmentation: The market is fragmented with numerous players operating globally. Prominent vendors include Pinecone, Weaviate, Supabase, Zilliz Cloud, Milvus, DataStax, CrateDB, SingleStore, Qdrant, Faiss, Rockset, PG Vector, KX, Chroma Vector Database, and Nomic. The market is segmented by application (SMEs and large enterprises) and type (cloud-based and on-premises). Cloud-based deployments are gaining traction due to their scalability, flexibility, and cost-effectiveness. The market is also regionally segmented, with North America, Europe, and Asia Pacific being the key growth regions. This report on the vector database software market provides comprehensive insights into the industry's current state, trends, growth drivers, challenges, and opportunities. It offers valuable information for businesses seeking to gain a competitive edge in this rapidly evolving market.

The global vector database solution market is poised for substantial growth, projected to reach USD 10240 million by 2033, expanding at a remarkable CAGR of 19.6% from 2025 to 2033. This growth is driven by the increasing demand for real-time analytics and insights, particularly in industries such as e-commerce, finance, and healthcare. The need to manage and analyze large volumes of unstructured data, including text, images, and videos, has led to the adoption of vector databases as they offer fast and efficient search and retrieval capabilities. The market is segmented based on type (cloud-based and on-premises) and application (personal and enterprise). Cloud-based solutions are gaining traction owing to their scalability, flexibility, and cost-effectiveness. Enterprise applications dominate the market, driven by the increasing need for data-driven decision-making and operational efficiency in large organizations. Key market players include Milvus, Faiss, RedisAI, Tinybird, Scalyr, Vald, and others. Geographic analysis reveals that North America and Asia Pacific are prominent markets, with significant growth potential in emerging economies.

The global vector database solution market is valued at USD 3218.3 million in 2025 and is projected to grow at a CAGR of 22.6% from 2025 to 2033. The market growth is attributed to the increasing adoption of vector databases in various applications, such as fraud detection, risk management, recommendation systems, and natural language processing. Major drivers of the market include the rising demand for real-time data processing, increasing adoption of cloud-based solutions, and growing need for efficient and scalable data management solutions. Key trends include the integration of artificial intelligence (AI) and machine learning (ML) algorithms into vector databases, the development of new data structures and algorithms, and the increasing popularity of open-source vector databases. However, the market is restrained by the lack of skilled professionals and the high cost of implementation.

The vector database solution market is experiencing explosive growth, projected to reach $3218.3 million in 2025 and exhibiting a robust Compound Annual Growth Rate (CAGR) of 22.6% from 2025 to 2033. This rapid expansion is driven by the increasing adoption of AI and machine learning applications across various sectors, including e-commerce, finance, and healthcare. These applications rely heavily on efficient similarity search capabilities offered by vector databases, making them a crucial component of modern data infrastructure. The rising volume of unstructured data, such as images, videos, and text, further fuels the demand, as vector databases excel at handling and querying such data types effectively. Key market drivers include advancements in deep learning algorithms, the need for real-time search functionalities, and the growing emphasis on personalized user experiences. This market is characterized by a diverse range of players, including established tech giants like Redis and emerging specialized vendors like Zilliz (with its Milvus offering), Pinecone, Weaviate, and others. Competition is fierce, prompting continuous innovation in areas such as query performance, scalability, and ease of integration. While challenges remain, such as the complexity of managing and deploying vector databases, the overall market outlook remains positive. Future growth will likely be influenced by the continued development of AI/ML applications, the maturation of cloud-based vector database services, and the increased accessibility of these solutions for businesses of all sizes. The ongoing development of standardized interfaces and improved tooling will also play a significant role in broader adoption.

The global vector database market size was valued at USD XX million in 2023 and is projected to expand at a CAGR of XX% from 2023 to 2033. Vector databases are specialized database management systems (DBMS) designed to handle and process large volumes of vector data, which is characterized by its high dimensionality and sparsity. The growing adoption of machine learning (ML), natural language processing (NLP), and computer vision (CV) applications is driving the demand for vector databases as they offer efficient storage, indexing, and retrieval of vector data, which is essential for training and deploying these AI models. The market is segmented based on type (open source and commercial), application (NLP, CV, recommender systems, etc.), and region (North America, Europe, Asia Pacific, etc.). Key players in the market include Shanghai Yirui Information Technology, Qdrant, Milvus, Weaviate, Pinecone, Vespa, pgvector, opensearch, Alibaba Cloud, cVector, Vearch, Troy Information Technology, Actionsky, Facebook, Tencent Cloud, and others. The market is expected to witness significant growth in the coming years due to the increasing adoption of AI applications across various industries, including healthcare, finance, and manufacturing.

Graph Database Vector Search Market Outlook

According to our latest research, the global graph database vector search market size reached USD 2.1 billion in 2024, reflecting robust momentum driven by the convergence of graph database technology and advanced vector search capabilities. The market is experiencing a significant compound annual growth rate (CAGR) of 22.4% and is projected to reach USD 7.9 billion by 2033. This rapid expansion is primarily attributed to the increasing need for efficient data retrieval, real-time analytics, and the growing adoption of AI-driven applications across multiple industries. As per our latest research, this market is characterized by dynamic innovation, strong demand across sectors, and substantial investments in next-generation data management solutions.

The growth of the graph database vector search market is strongly fueled by the exponential rise in unstructured and semi-structured data across enterprises worldwide. Organizations are increasingly seeking advanced solutions to manage, search, and analyze complex relationships within their data, particularly as digital transformation initiatives accelerate. Graph databases, when combined with vector search, enable businesses to perform semantic searches, discover intricate patterns, and extract actionable insights from vast datasets. This capability is becoming indispensable in sectors such as BFSI, healthcare, and e-commerce, where understanding data relationships can lead to improved decision-making, personalized customer experiences, and enhanced operational efficiency. The integration of vector search further amplifies the value proposition by allowing for similarity-based queries, which are crucial in recommendation systems and fraud detection applications.

Another key driver propelling the graph database vector search market is the rapid advancement of artificial intelligence and machine learning technologies. As AI models become more sophisticated, there is a growing need for data architectures that can support complex queries and real-time analytics. Graph databases, with their inherent ability to model and traverse relationships, are uniquely positioned to meet these requirements. The incorporation of vector search techniques allows for high-dimensional similarity searches, which are essential for powering AI-driven applications such as natural language processing, semantic search, and knowledge graphs. This synergy between graph databases and vector search is unlocking new possibilities for enterprises to harness the full potential of their data assets, driving adoption across both large enterprises and SMEs.

The scalability and flexibility offered by cloud-based deployment models are also playing a pivotal role in the expansion of the graph database vector search market. Cloud platforms provide organizations with the ability to scale resources on demand, reduce infrastructure costs, and accelerate the deployment of graph-based applications. This has led to a surge in the adoption of cloud-native graph database solutions, particularly among businesses looking to leverage advanced analytics and AI capabilities without the burden of managing complex on-premises infrastructure. Furthermore, the growing ecosystem of managed graph database services and the increasing availability of APIs and developer tools are lowering barriers to entry and fostering innovation in the market.

From a regional perspective, North America continues to dominate the graph database vector search market due to the presence of leading technology providers, high levels of digital adoption, and substantial investments in AI and data analytics. However, Asia Pacific is emerging as a high-growth region, driven by rapid digitization, expanding IT infrastructure, and increasing adoption of advanced analytics solutions in countries like China, India, and Japan. Europe is also witnessing steady growth, supported by stringent data regulations and a strong focus on innovation. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with growing awareness of the benefits of graph database technologies and increasing investments in digital transformation initiatives.

Component Analysis

The graph database vector search market is segmented by component into software and services, with software constituting the largest share of the market in 2024. The software segment is experiencing robust growth as organizations increasi

The global vector databases market for generative AI applications is projected to grow from an estimated USD 276 million in 2025 to a value of USD 526 million by 2033, exhibiting a CAGR of 13.6% during the forecast period. The increasing adoption of generative AI applications in natural language processing (NLP), computer vision, and other domains is driving market growth. Key market drivers include the rising demand for real-time data processing and analysis, the proliferation of IoT devices, and the growing popularity of deep learning and artificial intelligence (AI) technologies. The market is also benefitting from the increasing awareness of the advantages of vector databases, such as their ability to handle large volumes of data and their efficient and scalable performance. The major market trends include the shift towards cloud-based vector databases, the development of new and innovative solutions by vendors, and the growing number of applications in the healthcare, finance, and retail sectors.

Graph Database Vector Search Market Outlook

According to our latest research, the global Graph Database Vector Search market size reached USD 2.35 billion in 2024, exhibiting robust growth driven by the increasing demand for advanced data analytics and AI-powered search capabilities. The market is expected to expand at a CAGR of 21.7% during the forecast period, propelling the market size to an anticipated USD 16.8 billion by 2033. This remarkable growth trajectory is primarily fueled by the proliferation of big data, the widespread adoption of AI and machine learning, and the growing necessity for real-time, context-aware search solutions across diverse industry verticals.

One of the primary growth factors for the Graph Database Vector Search market is the exponential increase in unstructured and semi-structured data generated by enterprises worldwide. Organizations are increasingly seeking efficient ways to extract meaningful insights from complex datasets, and graph databases paired with vector search capabilities are emerging as the preferred solution. These technologies enable organizations to model intricate relationships and perform semantic searches with unprecedented speed and accuracy. Additionally, the integration of AI and machine learning algorithms with graph databases is enhancing their ability to deliver context-rich, relevant results, thereby improving decision-making processes and business outcomes.

Another significant driver is the rising adoption of recommendation systems and fraud detection solutions across various sectors, particularly in BFSI, retail, and e-commerce. Graph database vector search platforms excel at identifying patterns, anomalies, and connections that traditional relational databases often miss. This capability is crucial for detecting fraudulent activities, building sophisticated recommendation engines, and powering knowledge graphs that underpin intelligent digital experiences. The growing need for personalized customer engagement and proactive risk mitigation is prompting organizations to invest heavily in these advanced technologies, further accelerating market growth.

Furthermore, the shift towards cloud-based deployment models is catalyzing the adoption of graph database vector search solutions. Cloud platforms offer scalability, flexibility, and cost-effectiveness, making it easier for organizations of all sizes to implement and scale graph-powered applications. The availability of managed services and API-driven architectures is reducing the complexity associated with deployment and maintenance, enabling faster time-to-value. As more enterprises migrate their data infrastructure to the cloud, the demand for cloud-native graph database vector search solutions is expected to surge, driving sustained market expansion.

Geographically, North America currently dominates the Graph Database Vector Search market, owing to its advanced IT infrastructure, high adoption rate of AI-driven technologies, and presence of leading technology vendors. However, rapid digital transformation initiatives across Europe and the Asia Pacific are positioning these regions as high-growth markets. The increasing focus on data-driven decision-making, coupled with supportive regulatory frameworks and government investments in AI and big data analytics, is expected to fuel robust growth in these regions over the forecast period.

Component Analysis

The Component segment of the Graph Database Vector Search market is broadly categorized into software and services. The software sub-segment commands the largest share, driven by the relentless innovation in graph database technologies and the integration of advanced vector search functionalities. Organizations are increasingly deploying graph database software to manage complex data relationships, power semantic search, and enhance the performance of AI and machine learning applications. The software market is characterized by the proliferation of both open-source and proprietary solutions, with vendors

This dataset provides a simulated water surface elevation product that resembles the Ka-band Interferometer (KaRIn) measurements by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. The simulated SWOT KaRIN swaths span 128 km in the cross-swath direction with a 20-km nadir gap. This product is complementary to the L2_HR_PIXC_V1 product. It provides a less noisy, height-constrained geolocation (latitude, longitude, and height) of the L2_HR_PIXC_V1 pixels. In addition, this product provides an identifier associated with each pixel. The identifier contains the information of the river and/or lake features pulled from the Prior River Database (PRD) or in the Prior Lake Database (PLD). Note that this is a simulated SWOT product and not suited for any scientific exploration.

The data fabric market is projected to reach USD 2.6521 billion by 2033, growing at a CAGR of 15.99% from 2025 to 2033. The increasing adoption of cloud computing, the need for data integration, and the growing volume of data are driving the growth of the market. The key market trends include the growing adoption of cloud-based data fabrics, the convergence of data management and data integration, and the increasing use of data fabrics for regulatory compliance. The major market segments include type, application, service, and vertical. The major companies operating in the market include SAP SE, Informatica, Splunk Inc., Denodo, and Syncsort Inc. Recent developments include: In November 2023, DataStax announced the development of its relationship with Amazon Web Services (AWS), focusing on new generative AI technologies and integrations across various aspects, such as go-to-market strategies, products, and technology. The partnership aims at providing generative artificial intelligence solutions to customers leading to enhanced efficiency and effective growth, improving precise generative artificial intelligence applications for a better user experience., In December 2023, Fortinet (FTNT) revealed that it had expanded its strategic partnership with Digital Realty (DLR), the largest global provider of cloud-agnostic carrier-neutral data center solutions. The company intends to accelerate the worldwide deployment of Fortinet’s Universal Secure Access Service Edge (SASE) solution which is already deployed in over one hundred locations worldwide. SASE refers to a security framework that combines network security capabilities with Wide Area Network (WAN) functions that satisfy evolving access demands securely for enterprises., In August 2022, Zilliz contributed significantly to the Milvus 2.1 release. Milvus is one of the most superior vector database systems globally capable of processing huge volumes, including ones from different origins, enabling next-generation information fabrics’ improvements., In March 2022, Vyasa released Cortex; this insightful data management tool represents the “blueprint” for information sources related to Vyasa Layar data fabrics so that users can build, manage or gain access from these links., In December 2021, SAP launched a fresh version of SAP Data Intelligence. This update contains deployment and delivery as well as metadata and governance, pipeline modeling, connectivity and integration and intelligent processing. SAP Data Intelligence is an on-premises version., In October 2021, NetApp Inc. released ONTAP data management software, a hybrid multi-cloud data management tool. It provides high-performance storage and enables public cloud integration., In July 2021, Teradata revealed a Teradata QueryGrid upgrade that included more cloud-native functionality to boost Vantage’s hybrid-multi-cloud capacity while allowing Teradata clients access to data and analytics across multiple platforms from different vendors. With new cloud-native features, it enables clients to access data and analytics spanning heterogeneous devices and public cloud providers.. Potential restraints include: Security And Data Privacy Concerns 26.

The OSNI Large-scale database consists of 1:1250 and 1:2500 scale vector mapping. This is the highest level of detail available. Data consists of over 100 useable levels, including categorised buildings, roads, water, field boundaries and administration boundaries. All features are recorded as cartographic elements – lines, symbols, text etc. – with related attributes, within a hierarchical framework to categorize them by type of feature. Information is held within a general-purpose digital topographic database. The Data is fully edgematched, multi-attributed, with full links and nodes structure and inclusive of associated textual information, which is fully maintained. The largescale data is a vital component in various applications such as planning and resource management.

This version of The Digital Chart of the World (DCW) is an Environmental Systems Research Institute, Inc. (ESRI) product originally developed for the US Defense Mapping Agency (DMA) using DMA data. This data was downloaded from the Penn State web site and then converted to Shapefile format using ArcMap.

This dataset is derived from the Vector Map (VMap) Level 0 database; the third edition of the Digital Chart of the World. The second edition was a limited release item published 1995 09. The product is dual named to show its lineage to the original DCW, published in 1992, while positioning the revised product within a broader emerging-family of VMap products.

VMap Level 0 is a comprehensive 1:1,000,000 scale vector basemap of the world. It consists of cartographic, attribute, and textual data stored on compact disc read only memory (CD-ROM). The primary source for the database is the National Imagery and Mapping Agency's (NIMA) Operational Navigation Chart (ONC) series. This is the largest scale unclassified map series in existence that provides consistent, continuous global coverage of essential basemap features. The database contains more than 1,900 megabytes of vector data and is organized into 10 thematic layers. The data includes major road and rail networks, major hydrological drainage systems, major utility networks (cross-country pipelines and communication lines), all major airports, elevation contours (1000 foot (ft), with 500ft and 250ft supplemental contours), coastlines, international boundaries and populated places.

Market Analysis: Data Observability Market The global data observability market is projected to reach USD 2.37 billion by 2033, exhibiting a CAGR of 12.2% during the forecast period (2025-2033). The increasing adoption of cloud-based technologies, big data analytics, and the Internet of Things (IoT) is driving the market growth. Enterprises are seeking comprehensive solutions to monitor and ensure the reliability, performance, and stability of their complex data pipelines and systems. Key Drivers and Trends The adoption of data observability solutions is driven by the increasing need for data-driven decision-making, compliance with data privacy regulations, and the desire to improve operational efficiency. The growing complexity of data ecosystems, including the use of multiple data sources and technologies, necessitates real-time and comprehensive data monitoring capabilities. Moreover, the trend towards agile data management and DevOps practices is fueling demand for data observability solutions that can provide continuous visibility and insights into the performance of data pipelines and systems. Recent developments include: In July 2024, International Business Machines Corporation, an American multinational technology company acquired StreamSets to expand its data integration capabilities, enabling real-time data pipelines and enhancing IBM Data Fabric's support for AI-driven insights. This acquisition aims to provide robust tools that empower organizations to utilize data effectively in hybrid multi-cloud environments. , In June 2024, Splunk Inc. introduced new data management innovations to provide customers with unified visibility across their enterprise. This includes preprocessing data through a centralized pipeline to optimize data economics and enhance digital resilience, addressing the complexities of managing data growth across diverse IT environments. , In April 2024, the Microsoft Azure Container Networking team introduced Retina, an open-source container networking observability platform designed to provide comprehensive visualization and analysis of workload traffic across diverse environments. Retina utilizes eBPF technology to achieve kernel-level monitoring without requiring container agents, ensuring efficient and scalable network monitoring across Linux and Windows environments. , In April 2024, Tech Mahindra Limited, an Indian multinational information technology services and consulting company, collaborated with Microsoft to launch a unified workbench on Microsoft Fabric. The collaboration focuses on streamlining data workflows, enhancing analytics capabilities, and accelerating business transformation. , In November 2023, Monte Carlo introduced integrations with vector databases and Apache Kafka, enhancing their data observability capabilities to ensure data quality and reliability across complex pipelines, including those supporting generative AI models. These advancements, showcased at Impact 2023, include Performance Monitoring and Data Product Dashboard tools to optimize data pipeline efficiency and monitor data product reliability. .

Google Patents Research Data contains the output of much of the data analysis work used in Google Patents (patents.google.com), including machine translations of titles and abstracts from Google Translate, embedding vectors, extracted top terms, similar documents, and forward references.

The "Gridded Soil Survey Geographic (gSSURGO) Database State-tile Package" product is derived from the Soil Survey Geographic (2.2) Database dated October 1, 2019. The gSSURGO data were prepared by merging SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table that contains “ready to map attributes”.

SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map” attributes. The gridded map layer is offered in an ArcGIS™ 10.0 file geodatabase raster format.

The raster and vector map data have a State-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key (MUKEY). A unique map unit key is used to link raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

Constraints:
Not to be used for navigation, for informational purposes only. See full disclaimer for more information

IntroductionLearning complex, detailed, and evolving knowledge is a challenge in multiple technical professions. Relevant source knowledge is contained within many large documents and information sources with frequent updates to these documents. Knowledge tests need to be generated on new material and existing tests revised, tracking knowledge base updates. Large Language Models (LLMs) provide a framework for artificial intelligence-assisted knowledge acquisition and continued learning. Retrieval-Augmented Generation (RAG) provides a framework to leverage available, trained LLMs combined with technical area-specific knowledge bases.MethodsHerein, two methods are introduced (DaaDy: document as a dictionary and SQAD: structured question answer dictionary), which together enable effective implementation of LLM-RAG question-answering on large documents. Additionally, the AI for knowledge intensive tasks (AIKIT) solution is presented for working with numerous documents for training and continuing education. AIKIT is provided as a containerized open source solution that deploys on standalone, high performance, and cloud systems. AIKIT includes LLM, RAG, vector stores, relational database, and a Ruby on Rails web interface.ResultsCoverage of source documents by LLM-RAG generated questions decreases as the length of documents increase. Segmenting source documents improve coverage of generated questions. The AIKIT solution enabled easy use of multiple LLM models with multimodal RAG source documents; AIKIT retains LLM-RAG responses for queries against one or multiple LLM models.DiscussionAIKIT provides an easy-to-use set of tools to enable users to work with complex information using LLM-RAG capabilities. AIKIT enables easy use of multiple LLM models with retention of LLM-RAG responses.