Alternatives to TerminusDB

Introducing DeepSeek-V3.2-Exp, our newest experimental model derived from V3.1-Terminus, featuring the innovative DeepSeek Sparse Attention (DSA) that enhances both training and inference speed for lengthy contexts. This DSA mechanism allows for precise sparse attention while maintaining output quality, leading to improved performance for tasks involving long contexts and a decrease in computational expenses. Benchmark tests reveal that V3.2-Exp matches the performance of V3.1-Terminus while achieving these efficiency improvements. The model is now fully operational across app, web, and API platforms. Additionally, to enhance accessibility, we have slashed DeepSeek API prices by over 50% effective immediately. During a transition period, users can still utilize V3.1-Terminus via a temporary API endpoint until October 15, 2025. DeepSeek encourages users to share their insights regarding DSA through our feedback portal. Complementing the launch, DeepSeek-V3.2-Exp has been made open-source, with model weights and essential technology—including crucial GPU kernels in TileLang and CUDA—accessible on Hugging Face. We look forward to seeing how the community engages with this advancement.

We empower exceptional Go-to-Market teams™ to revolutionize the way your organization drives revenue. Terminus is designed specifically for sales and marketing teams that prioritize revenue generation over mere lead acquisition. At the heart of Terminus lies our Data Studio, which enables you to easily identify and rank the most suitable targets using firmographic, intent, engagement, psychographic, and relationship insights. With Terminus, you can craft tailored messages for the appropriate audiences, regardless of their location. Create synchronized experiences across various platforms, including display and social media advertising, chat, email, and web personalization. By effortlessly integrating all of your account data, Terminus ensures that your go-to-market teams remain in sync from the initial contact through to renewal and expansion. It automatically notifies sales teams of significant engagements, centralizes account details, and enhances pipeline forecasting, thereby speeding up the opportunity for growth. As a result, your organization can achieve a more streamlined and efficient approach to revenue generation.

KgBase, short for Knowledge Graph Base, is a powerful collaborative database that features version control, analytics, and visualization tools. It enables individuals and communities to craft knowledge graphs that help derive insights from their data. Users can seamlessly import CSV files and spreadsheets or utilize the API for collaborative data work. With KgBase, you can create knowledge graphs without any coding, thanks to an intuitive user interface that allows for easy navigation of the graph and the display of results in tables, charts, and more. Engage with your graph data interactively; as you construct queries, the results are updated in real time, making the process much simpler than traditional query languages like Cypher or Gremlin. Additionally, your graph data can be represented in tabular form, so you can easily explore all results, regardless of the dataset size. KgBase is designed to handle both extensive graphs with millions of nodes and smaller projects effortlessly. Whether you prefer cloud hosting or self-hosting, it supports a diverse range of databases. You can introduce graph capabilities to your organization by starting with pre-existing templates. Moreover, any query results can be quickly transformed into visual chart representations, enhancing the interpretability of your data insights. This flexibility and ease of use make KgBase an ideal choice for anyone looking to leverage the power of knowledge graphs in their data analysis endeavors.

xtendr reveals comprehensive, privacy-focused insights from various independent data sources while ensuring user protection throughout the entire data lifecycle. This innovative platform provides access to previously unreachable data and instills confidence in both privacy and adherence to regulations. Beyond mere anonymity, xtendr addresses the critical gap in multi-party data sharing by offering robust privacy safeguards, effectively serving as a guardian of your data through advanced cryptography, enabling you to unlock your full potential. It represents the pinnacle of privacy-enhancing data collaboration technology, having successfully tackled the long-standing cryptographic challenges associated with data sharing among parties that may not fully trust one another. With xtendr, organizations can advance their operations through an enterprise-level data protection solution that facilitates the formation of data partnerships while safeguarding sensitive information. In today's digital landscape, data is increasingly recognized as a vital currency, with many asserting that it is surpassing oil as the most valuable asset, underscoring its ever-growing significance. As we continue to embrace this digital evolution, the importance of secure data sharing becomes ever more critical for businesses looking to thrive in an interconnected world.

DeepSeek has launched DeepSeek-V3.1-Terminus, an upgrade to the V3.1 architecture that integrates user suggestions to enhance output stability, consistency, and overall agent performance. This new version significantly decreases the occurrences of mixed Chinese and English characters as well as unintended distortions, leading to a cleaner and more uniform language generation experience. Additionally, the update revamps both the code agent and search agent subsystems to deliver improved and more dependable performance across various benchmarks. DeepSeek-V3.1-Terminus is available as an open-source model, with its weights accessible on Hugging Face, making it easier for the community to leverage its capabilities. The structure of the model remains consistent with DeepSeek-V3, ensuring it is compatible with existing deployment strategies, and updated inference demonstrations are provided for users to explore. Notably, the model operates at a substantial scale of 685B parameters and supports multiple tensor formats, including FP8, BF16, and F32, providing adaptability in different environments. This flexibility allows developers to choose the most suitable format based on their specific needs and resource constraints.

Designed specifically for handling super large-scale graphs with latency measured in milliseconds, this graph database continues to engage with the community for its preparation, promotion, and popularization. Nebula Graph ensures that access is secured through role-based access control, allowing only authenticated users. The database supports various types of storage engines and its query language is adaptable, enabling the integration of new algorithms. By providing low latency for both read and write operations, Nebula Graph maintains high throughput, effectively simplifying even the most intricate data sets. Its shared-nothing distributed architecture allows for linear scalability, making it an efficient choice for expanding businesses. The SQL-like query language is not only user-friendly but also sufficiently robust to address complex business requirements. With features like horizontal scalability and a snapshot capability, Nebula Graph assures high availability, even during failures. Notably, major internet companies such as JD, Meituan, and Xiaohongshu have successfully implemented Nebula Graph in their production environments, showcasing its reliability and performance in real-world applications. This widespread adoption highlights the database's effectiveness in meeting the demands of large-scale data management.

MLReef allows domain specialists and data scientists to collaborate securely through a blend of coding and no-coding methods. This results in a remarkable 75% boost in productivity, as teams can distribute workloads more effectively. Consequently, organizations are able to expedite the completion of numerous machine learning projects. By facilitating collaboration on a unified platform, MLReef eliminates all unnecessary back-and-forth communication. The system operates on your premises, ensuring complete reproducibility and continuity of work, allowing for easy rebuilding whenever needed. It also integrates with established git repositories, enabling the creation of AI modules that are not only explorative but also versioned and interoperable. The AI modules developed by your team can be transformed into user-friendly drag-and-drop components that are customizable and easily managed within your organization. Moreover, handling data often necessitates specialized expertise that a single data scientist might not possess, making MLReef an invaluable asset by empowering field experts to take on data processing tasks, which simplifies complexities and enhances overall workflow efficiency. This collaborative environment ensures that all team members can contribute to the process effectively, further amplifying the benefits of shared knowledge and skill sets.

Cayley is an open-source database tailored for Linked Data, drawing inspiration from the graph database that supports Google's Knowledge Graph, previously known as Freebase. This graph database is crafted for user-friendliness and adept at handling intricate data structures, featuring an integrated query editor, a visualizer, and a Read-Eval-Print Loop (REPL). It supports various query languages, including Gizmo, which is influenced by Gremlin, a GraphQL-like query language, and MQL, a streamlined version catering to Freebase enthusiasts. Cayley's modular architecture allows seamless integration with preferred programming languages and backend storage solutions, making it production-ready, thoroughly tested, and utilized by numerous companies for their operational tasks. Additionally, it is optimized for application use, demonstrating impressive performance metrics; for instance, testing has shown that it can effortlessly manage 134 million quads in LevelDB on consumer-grade hardware from 2014, with multi-hop intersection queries—such as finding films featuring both X and Y—executing in about 150 milliseconds. By default, Cayley is set up to operate in-memory, which is what the backend memstore refers to, thereby enhancing its speed and efficiency for data retrieval and manipulation. Overall, Cayley offers a powerful solution for those looking to leverage linked data in their applications.

Claravine is changing the way global enterprises view data integrity. The Data Standards Cloud allows teams to connect, standardize, and control data across their organization. Claravine is used by leading brands to increase their control and ownership of their data, allowing them to make better decisions, provide better customer experiences, and increase ROI.

Optable provides a comprehensive data clean room platform designed for seamless activation. This innovative technology empowers both publishers and advertisers to securely strategize, implement, and evaluate their advertising efforts. Representing a new era of data collaboration that prioritizes privacy, Optable enables clients to engage with both their own customers and partners, including those who may not use the platform. Utilizing the platform's Flash Nodes, users can invite external participants into a protected setting. Additionally, Optable features a decentralized identity infrastructure that facilitates the construction of private identity graphs. This setup allows for the creation of purpose-specific, permission-based data clean rooms that significantly reduce data transfer. Ensuring compatibility with data warehouses and other clean rooms is vital to its functionality. Furthermore, by leveraging open-source software, third-party platforms can effectively match their data with Optable's clients and implement secure clean room capabilities tailored to their needs, thereby enhancing the overall efficacy of data collaboration. This multi-faceted approach positions Optable as a leader in the evolving landscape of data privacy and collaboration.

Hex unites the finest features of notebooks, business intelligence, and documentation into a cohesive and collaborative user interface, establishing itself as a contemporary Data Workspace. It simplifies the process of connecting to various data sources and allows for collaborative analysis via SQL and Python-based notebooks, enabling users to share their findings as interactive data applications and narratives. Upon entering Hex, the Projects page serves as the default landing area, making it easy to access both your own projects and those shared within your workspace. The outline feature offers a streamlined overview of all cells contained in a project's Logic View, where each cell is annotated with the variables it defines. Furthermore, cells that produce visible outputs—such as chart cells, input parameters, and markdown cells—provide a preview of their results. By clicking on any cell within the outline, users can instantly navigate to that specific location in the logic, enhancing the overall efficiency of the workflow. This functionality ensures that collaboration and data exploration are both intuitive and effective.

Your online collaborative tool to automate data collection. RowShare is the new way to collaborate on spreadsheet data. Collect and centralize data. Real-time analysis and reports can be run in an easy, automated and secure way. Automate all data processing: collecting, centralizing, analyzing, sharing. All the repetitive and low-value tasks such as manual reminders, endless VLOOKUP( (), copy paste, replace or merge, etc. can be eliminated. Collaboration is possible without compromising confidentiality. Line by line, decide who can see what. You can either create your own spreadsheets, or you can use our pre-made templates. In just a few clicks, you can customize and adapt spredsheets to meet your specific needs. You can create seamless workflows yourself or with the assistance of our experts. You can monitor the progress of your data collection in real-time. Automatically generate the most current documents. Get accurate reports to make better and faster decisions.

HALCYON serves as a data collaboration platform that guarantees timely delivery of the appropriate data. It acts as a central hub for all stakeholders to provide, verify, and endorse engineering data, subsequently establishing an unchangeable record of that information. By automating the collection of detailed engineering data, HALCYON significantly enhances collaboration throughout the supply chain. The various activities that HALCYON monitors occur in present time, albeit in numerous disjointed formats and sources; we consolidate everything into a comprehensive central data environment that links teams and their respective data. Unique in its offering, HALCYON is the sole blockchain-based information management platform, crafting a lasting digital golden thread that encompasses the information, decisions, and queries generated during projects. Its configuration is user-friendly, allowing for effortless loading, sharing, and actioning of data from the supply chain. Consequently, all participants gain clarity on their responsibilities and deadlines, facilitating straightforward compliance and action. Additionally, this seamless integration fosters a more efficient workflow, ultimately benefiting the entire project lifecycle.

Coginiti is the AI-enabled enterprise Data Workspace that empowers everyone to get fast, consistent answers to any business questions. Coginiti helps you find and search for metrics that are approved for your use case, accelerating the lifecycle of analytic development from development to certification. Coginiti integrates the functionality needed to build, approve and curate analytics for reuse across all business domains, while adhering your data governance policies and standards. Coginiti’s collaborative data workspace is trusted by teams in the insurance, healthcare, financial services and retail/consumer packaged goods industries to deliver value to customers.

QEDIT stands as a robust, enterprise-focused platform designed for cross-organizational data collaboration, tailored for the evolving landscape of the data economy. By utilizing cutting-edge privacy-enhancing technologies, we enable businesses to effectively monetize their data assets, refine their analytics processes, and extract actionable insights from secondary sources while ensuring a risk-free environment. Our cloud-hosted solution is highly scalable and integrates smoothly with existing legacy database systems, allowing for quick implementation. With features such as a customizable dashboard, advanced reporting capabilities, and real-time notifications, QEDIT delivers essential, timely intelligence for your business needs. This platform not only supports regulatory-compliant data sharing but also fosters growth, reduces risks, and addresses intricate business challenges. Ultimately, QEDIT equips companies with the tools necessary to collaborate securely and leverage external data insights without compromising confidentiality. By facilitating these connections, QEDIT transforms the way organizations approach data sharing and utilization in a competitive market.

Einblick offers a swift and highly collaborative platform for data exploration, prediction generation, and application deployment. Our innovative canvases transform the data science process by simplifying the exploration, cleaning, and manipulation of data through a user-friendly interface. Unlike other platforms, we enable real-time collaboration among your entire team, emphasizing that collective decision-making is essential. Stop spending time on manual model adjustments; our AutoML feature is designed to facilitate the creation of transparent predictions and pinpoint crucial influencing factors effortlessly. Einblick also streamlines common analytics tasks into user-friendly operators, allowing you to minimize repetitive work and reach conclusions more quickly. Whether your data resides in Snowflake, S3 buckets, or CSV files, you can connect your data source and start drawing insights in no time. For instance, by analyzing a list of churned and active customers, you can integrate all relevant information about them, revealing the primary reasons for churn and assessing the risk level for each customer effectively. Moreover, our platform empowers teams to make data-driven decisions with confidence, ensuring that insights are accessible and actionable for everyone involved.

PuppyGraph allows you to effortlessly query one or multiple data sources through a cohesive graph model. Traditional graph databases can be costly, require extensive setup time, and necessitate a specialized team to maintain. They often take hours to execute multi-hop queries and encounter difficulties when managing datasets larger than 100GB. Having a separate graph database can complicate your overall architecture due to fragile ETL processes, ultimately leading to increased total cost of ownership (TCO). With PuppyGraph, you can connect to any data source, regardless of its location, enabling cross-cloud and cross-region graph analytics without the need for intricate ETLs or data duplication. By directly linking to your data warehouses and lakes, PuppyGraph allows you to query your data as a graph without the burden of constructing and maintaining lengthy ETL pipelines typical of conventional graph database configurations. There's no longer a need to deal with delays in data access or unreliable ETL operations. Additionally, PuppyGraph resolves scalability challenges associated with graphs by decoupling computation from storage, allowing for more efficient data handling. This innovative approach not only enhances performance but also simplifies your data management strategy.

Gain insights into user interactions with your product and pinpoint areas of opportunity to guide your product strategy. Mode enables a single Stitch analyst to accomplish what typically requires an entire data team by offering rapid, adaptable, and collaborative tools. Create dashboards that track annual revenue and utilize chart visualizations to quickly spot anomalies. Develop well-crafted reports suitable for investors or facilitate collaboration by sharing your analyses with different teams. Integrate your complete technology ecosystem with Mode to uncover upstream problems and enhance overall performance. Accelerate cross-team workflows using APIs and webhooks. By analyzing user engagement, you can discover opportunity areas that help refine your product decisions. Additionally, utilize insights from marketing and product data to address vulnerabilities in your sales funnel, optimize landing-page efficiency, and anticipate churn before it occurs, ensuring proactive measures are in place.

Bitfount serves as a collaborative platform for distributed data science, enabling deep collaborations without the need for data sharing. The innovative approach of distributed data science allows algorithms to be deployed directly to where the data resides, rather than moving the data itself. In just a few minutes, you can establish a federated network for privacy-preserving analytics and machine learning, freeing your team to concentrate on generating insights and fostering innovation rather than getting bogged down by bureaucratic processes. While your data team possesses the expertise needed to tackle significant challenges and drive innovation, they often face obstacles related to data accessibility. Are intricate data pipeline infrastructures disrupting your strategies? Is the compliance process taking an excessive amount of time? Bitfount offers a more effective solution to empower your data specialists. It enables the connection of disparate and multi-cloud datasets while maintaining privacy and honoring commercial confidentiality. Say goodbye to costly and time-consuming data migrations, as our platform provides usage-based access controls that guarantee teams can only conduct analyses on the data you permit. Moreover, the management of these access controls can be seamlessly transferred to the teams that actually manage the data, streamlining your operations and enhancing productivity. Ultimately, Bitfount aims to revolutionize the way organizations leverage their data assets for better outcomes.

Enjoy seamless remote access using SSH Client Terminal and Telnet! This app provides secure & easy to use terminal emulation on your device. This is the most advanced and intelligent SSH client app to connect and manage remote servers on your iOS and Mac devices. SSH Client: SSH Client is a mobile SSH-Telnet application with the best putty app, console app, and shell iOS app. It also includes powerful remote access & terminal emulator. Our enterprise-grade mobile application is designed to meet all server management requirements. Our features are not limited only to Bluetooth keyboard support, terminal keyboard and language support and multi-session capabilities. Our SSH client app can easily be integrated with AWS Azure and DigitalOcean.

The contemporary data workspace transforms the accessibility of your data assets, making everything from data tables to BI reports easily discoverable. With our robust search algorithms and user-friendly browsing experience, locating the right asset becomes effortless. Atlan simplifies the identification of poor-quality data through the automatic generation of data quality profiles. This includes features like variable type detection, frequency distribution analysis, missing value identification, and outlier detection, ensuring you have comprehensive support. By alleviating the challenges associated with governing and managing your data ecosystem, Atlan streamlines the entire process. Additionally, Atlan’s intelligent bots analyze SQL query history to automatically construct data lineage and identify PII data, enabling you to establish dynamic access policies and implement top-notch governance. Even those without technical expertise can easily perform queries across various data lakes, warehouses, and databases using our intuitive query builder that resembles Excel. Furthermore, seamless integrations with platforms such as Tableau and Jupyter enhance collaborative efforts around data, fostering a more connected analytical environment. Thus, Atlan not only simplifies data management but also empowers users to leverage data effectively in their decision-making processes.

Amazon Neptune is an efficient and dependable graph database service that is fully managed, facilitating the development and operation of applications that handle intricate, interconnected datasets. At its heart, Amazon Neptune features a specialized, high-performance database engine tailored for the storage of billions of relationships while enabling rapid querying with latency measured in milliseconds. It accommodates widely-used graph models, including Property Graph and W3C's RDF, along with their associated query languages, Apache TinkerPop Gremlin and SPARQL, which simplifies the process of crafting queries for navigating complex datasets. This service supports various graph-based applications, including recommendation systems, fraud detection mechanisms, knowledge graphs, drug discovery initiatives, and enhanced network security protocols. With a proactive approach, it enables the detection and analysis of IT infrastructure threats through a multi-layered security framework. Furthermore, it allows users to visualize their entire infrastructure to effectively plan, forecast, and address potential risks, while also enabling the creation of graph queries for the near-real-time identification of fraudulent patterns in financial and purchasing activities, thereby enhancing overall security and efficiency.

RushDB is an innovative, open-source graph database that requires no configuration and rapidly converts JSON and CSV files into a fully normalized, queryable Neo4j graph, all while avoiding the complexities associated with schema design, migrations, and manual indexing. Tailored for contemporary applications as well as AI and machine learning workflows, RushDB offers an effortless experience for developers, merging the adaptability of NoSQL with the organized capabilities of relational databases. By incorporating automatic data normalization, ensuring ACID compliance, and featuring a robust API, RushDB streamlines the often challenging processes of data ingestion, relationship management, and query optimization, allowing developers to direct their energies toward building applications rather than managing databases. Some notable features include: 1. Instantaneous data ingestion without the need for configuration 2. Storage and querying capabilities powered by graph technology 3. Support for ACID transactions and seamless schema evolution 4. A developer-friendly API that facilitates querying akin to an SDK 5. High-performance capabilities for search and analytics 6. Flexibility to be self-hosted or cloud-compatible. This combination of features positions RushDB as a transformative solution in the realm of data management.

With your own data shop, create new revenue streams from the data you already have. Narrative focuses on the fundamental principles that make buying or selling data simpler, safer, and more strategic. You must ensure that the data you have access to meets your standards. It is important to know who and how the data was collected. Access new supply and demand easily for a more agile, accessible data strategy. You can control your entire data strategy with full end-to-end access to all inputs and outputs. Our platform automates the most labor-intensive and time-consuming aspects of data acquisition so that you can access new data sources in days instead of months. You'll only ever have to pay for what you need with filters, budget controls and automatic deduplication.

GraphBase is a Graph Database Management System designed to streamline the development and upkeep of intricate data graphs. While Relational Database Management Systems often struggle with complex and interconnected structures, graph databases offer superior modeling capabilities, enhanced performance, and greater scalability. The existing range of graph database solutions, including triplestores and property graphs, has been available for almost twenty years; although they are effective tools with diverse applications, they still fall short in managing intricate data structures. With the introduction of GraphBase, we aimed to facilitate the handling of complex data architectures, allowing your information to evolve into something greater—Knowledge. We accomplished this by reimagining the management of graph data, ensuring that the graph is prioritized as a fundamental component. In GraphBase, users benefit from a graph equivalent of the familiar "rows and tables" framework, which contributes to the user-friendly nature of Relational Databases, making it easier to navigate and manipulate data. Ultimately, GraphBase transforms how organizations view and interact with their data, paving the way for innovative possibilities.

InfiniteGraph is a massively scalable graph database specifically designed to excel at high-speed ingest of massive volumes of data (billions of nodes and edges per hour) while supporting complex queries. InfiniteGraph can seamlessly distribute connected graph data across a global enterprise. InfiniteGraph is a schema-based graph database that supports highly complex data models. It also has an advanced schema evolution capability that allows you to modify and evolve the schema of an existing database. InfiniteGraph’s Placement Management Capability allows you to optimize the placement of data items resulting in tremendous performance improvements in both query and ingest. InfiniteGraph has client-side caching which caches frequently used node and edges. This can allow InfiniteGraph to perform like an in-memory graph database. InfiniteGraph's DO query language enables complex "beyond graph" queries not supported by other graph databases.

Graph databases, which are a key feature of Oracle's converged database solution, remove the necessity for establishing a distinct database and transferring data. This allows analysts and developers to conduct fraud detection in the banking sector, uncover relationships and links to data, and enhance traceability in smart manufacturing, all while benefiting from enterprise-level security, straightforward data ingestion, and robust support for various data workloads. The Oracle Autonomous Database incorporates Graph Studio, offering one-click setup, built-in tools, and advanced security measures. Graph Studio streamlines the management of graph data and facilitates the modeling, analysis, and visualization throughout the entire graph analytics lifecycle. Oracle supports both property and RDF knowledge graphs, making it easier to model relational data as graph structures. Additionally, interactive graph queries can be executed directly on the graph data or via a high-performance in-memory graph server, enabling efficient data processing and analysis. This integration of graph technology enhances the overall capabilities of data management within Oracle's ecosystem.

Transform your approach to business intelligence with AI-driven solutions that eliminate the complexities of data modeling. Quickly generate sales dashboards, gain valuable insights, and collaborate in real-time without the usual delays. With Zebra AI, you can create those dashboards in under 15 seconds, making it an ideal tool for anyone from business analysts to CEOs; simply upload your data and let our AI handle the rest to deliver your dashboard immediately. We prioritize the security of your business data, ensuring it remains private and separate from broader machine learning initiatives. Forget about traditional chart makers; Zebra AI acts as your reliable consultant, enabling you to identify trends, access information effortlessly, and create impactful presentations. You can easily share your dashboards and AI conversations, allowing your entire team to operate from the same reliable data source—all achievable with just a few clicks. This innovative platform goes beyond being a mere chatbot; it equips seasoned data analysts with the tools they need to tackle even the most intricate questions and analyses effectively, enhancing productivity across the board. Ultimately, Zebra AI is designed not just to simplify your workflow but to empower every team member to leverage data for informed decision-making.

RecallGraph is a versioned graph data store. It retains all changes its data (vertices, edges) have undergone to get to their current state. It supports point-in time graph traversals that allow the user to query any past state of a graph as well as the present. RecallGraph can be used in situations where data is best represented using a network of edges and vertices (i.e., as a graph). 1. Both edges and vertices can contain properties in the form attribute/value pairs (equivalent of JSON objects). 2. Documents (vertices/edges), can change throughout their lives (both in their individual attributes/values as well as in their relationships to each other). 3. Documents from the past are just as important as their current states, so it is essential to retain and queryable their change history. Also see this blog post for an intro - https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fblog.recallgraph.tech%2Fnever-lose-your-old-data-again.

VelocityDB stands out as a unique database engine, renowned for its ability to store data more quickly and efficiently than any competing solutions while being significantly more affordable. Unlike traditional databases that rely on mapping to tables, JSON, or XML, it retains .NET objects in their native format. Complementing this is VelocityGraph, an open-source property graph database that seamlessly integrates with the VelocityDB object database. As a C# .NET noSQL object database, VelocityDB is enhanced by VelocityGraph, making it one of the fastest, most scalable, and flexible database options available today. Bugs that are reported along with reproducible test cases are typically resolved within a week, showcasing the responsiveness of the development team. The primary advantage of using this database system lies in its remarkable flexibility, enabling developers to tailor their applications to meet even the most specific requirements. With VelocityDB, you have the freedom to select the most suitable data structures for your needs, granting you control over the persistent placement of data, as well as how it is indexed and accessed. This level of customization sets VelocityDB apart in the realm of database technology.

CompassABM is a user-friendly account-based marketing tool designed to seamlessly integrate with the interfaces and platforms you are already familiar with. Whether you are initiating a new pilot or enhancing your current ABM strategy, CompassABM facilitates straightforward account identification and scalable content personalization. Our optional services can expedite your journey towards achieving your goals. Offering essential ABM identification and content personalization features, CompassABM avoids unnecessary complexities and focuses on what truly matters. With a tiered subscription model, you only pay for what you actually use, with plans starting as low as $1,500 per month. The platform is compatible with your existing marketing technologies, allowing you to drive targeted traffic through channels like Google, LinkedIn, or Terminus. You can also integrate with engagement tools such as Drift and easily export prospect data to Salesforce and other contact enrichment services. By using CompassABM, you can create a powerful and all-encompassing ABM solution that not only targets valuable accounts but also maintains engagement with every visitor to your site. Ultimately, CompassABM equips you with the tools necessary to elevate your marketing efforts and achieve significant results.

The Datastreams Platform represents a cutting-edge solution aimed at streamlining the processes involved in capturing, sharing, and managing data both within organizations and across different entities. From the very first phase of gathering appropriate data to achieving a reliable, personalized view of that data, our Platform guides users throughout the entire experience. Thanks to its advanced streaming technology, optimized performance, and patented data capturing features, the process is not only efficient but also straightforward; coupled with our commitment to privacy by design, data collaboration has become accessible to all users! At its core, the Platform consists of the Data Operations Unit (known as Datastreams Operator), which can be customized with various modules to cater to specific needs, such as implementing data quality checks on web or mobile platforms, or compliant segmentation of target groups. With a focus on enhancing both IT and business flexibility, users can initiate data collaboration in just a matter of days by easily connecting to various data sources, thus ensuring a rapid and effective start to their data-driven initiatives.

Obtain valuable consumer insights without the risk of disclosing personal information. The Karlsgate Identity Exchange (KIE™) utilizes innovative, patent-pending Cryptoidentity technology to match data in an anonymous manner in real-time. This approach allows for market data collection while ensuring compliance with consumer privacy standards that exceed existing regulations. KIE empowers data providers with enhanced control and transparency over their data assets. The time required for data acquisition is significantly reduced from months to mere minutes. KIE also simplifies the evaluation, contracting, and data management processes, facilitating a swift operational setup. Its advanced input file-matching logic further streamlines data processing. By employing deterministic cascading match algorithms, KIE achieves high-confidence file matching. With Karlsgate, organizations can forge strategic partnerships for data collaboration without compromising their customers' personally identifiable information. Furthermore, the data appending process is instantaneous, guaranteeing that the attributes are both current and precise, alleviating concerns about outdated enhanced data sets. This ensures that businesses can rely on their data to make informed decisions quickly.

AnzoGraph DB boasts an extensive array of analytical features that can significantly improve your analytical framework. Check out this video to discover how AnzoGraph DB operates as a Massively Parallel Processing (MPP) native graph database specifically designed for data harmonization and analytics. This horizontally scalable graph database is optimized for online analytics and tackling data harmonization issues. Addressing challenges related to linked data, AnzoGraph DB stands out as a leading analytical graph database in the market. It offers robust online performance suitable for enterprise-scale graph applications, ensuring efficiency and speed. AnzoGraph DB employs familiar SPARQL*/OWL for semantic graphs, while also accommodating Labeled Property Graphs (LPGs). Its vast array of analytical, machine learning, and data science tools empowers users to uncover new insights at remarkable speed and scale. By prioritizing context and relationships among data, you can enhance your analysis significantly. Additionally, the database enables ultra-fast data loading and execution of analytical queries, making it an invaluable asset for any data-driven organization.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

GUN is a real-time, decentralized, and offline-first graph database engine that allows for seamless data storage, loading, and sharing within applications without the need to manage servers, network requests, or handle issues related to offline changes or concurrency conflicts. This lightweight, efficient data synchronization and storage system operates wherever JavaScript is supported, enabling developers to concentrate on their app's data needs rather than technical hurdles. By using GUN, you can rapidly create innovative applications while harnessing powerful internet capabilities such as decentralization and genuine privacy, ultimately contributing to a freer and more open web. GUN serves as a versatile database engine that operates across browsers, mobile devices, and servers, empowering you to configure your ideal data management system. With GUN, you can build applications with enhanced functionality while ensuring user privacy and eliminating dependencies on traditional infrastructures.

HugeGraph is a high-performance and scalable graph database capable of managing billions of vertices and edges efficiently due to its robust OLTP capabilities. This database allows for seamless storage and querying, making it an excellent choice for complex data relationships. It adheres to the Apache TinkerPop 3 framework, enabling users to execute sophisticated graph queries using Gremlin, a versatile graph traversal language. Key features include Schema Metadata Management, which encompasses VertexLabel, EdgeLabel, PropertyKey, and IndexLabel, providing comprehensive control over graph structures. Additionally, it supports Multi-type Indexes that facilitate exact queries, range queries, and complex conditional queries. The platform also boasts a Plug-in Backend Store Driver Framework that currently supports various databases like RocksDB, Cassandra, ScyllaDB, HBase, and MySQL, while also allowing for easy integration of additional backend drivers as necessary. Moreover, HugeGraph integrates smoothly with Hadoop and Spark, enhancing its data processing capabilities. By drawing on the storage structure of Titan and the schema definitions from DataStax, HugeGraph offers a solid foundation for effective graph database management. This combination of features positions HugeGraph as a versatile and powerful solution for handling complex graph data scenarios.

ZinkML is an open-source data science platform that does not require any coding. It was designed to help organizations leverage data more effectively. Its visual and intuitive interface eliminates the need for extensive programming expertise, making data sciences accessible to a wider range of users. ZinkML streamlines data science from data ingestion, model building, deployment and monitoring. Users can drag and drop components to create complex pipelines, explore the data visually, or build predictive models, all without writing a line of code. The platform offers automated model selection, feature engineering and hyperparameter optimization, which accelerates the model development process. ZinkML also offers robust collaboration features that allow teams to work seamlessly together on data science projects. By democratizing the data science, we empower businesses to get maximum value out of their data and make better decisions.

LiveRamp’s platform enables privacy-focused and customizable collaboration within your organization and with external partners, facilitating audience building, data activation, and gaining actionable insights. As cookies phase out, establishing a new marketing foundation for the coming decade becomes essential. Safe Haven not only safeguards your investment in audience data but also fosters significant collaborative opportunities, enabling a more precise and evolving understanding of your customers in a secure manner. By maximizing your marketing data's worth, Safe Haven empowers you with over 600 seamless integrations, allowing connections with top-tier services to enhance audience insights, obtain novel measurement reports, or initiate partnerships for developing innovative predictive models or micro-segments. Shift the conversation from mere impressions and clicks to metrics that resonate with the entire C-suite, such as incrementality, closed-loop measurement, journey analytics, and revenue impact, ensuring your marketing efforts are both relevant and effective. In doing so, you can transform how your organization approaches data-driven marketing strategies and foster a culture of informed decision-making.

Fluree is an immutable RDF graph database written in Clojure and adhering to W3C standards, supporting JSON and JSON-LD while accommodating various RDF ontologies. It operates with an immutable ledger that secures transactions with cryptographic integrity, alongside a rich RDF graph database capable of various queries. It employs SmartFunctions for enforcing data management rules, including identity and access management and data quality. Additionally, It boasts a scalable, cloud-native architecture utilizing a lightweight Java runtime, with individually scalable ledger and graph database components, embodying a "Data-Centric" ideology that treats data as a reusable asset independent of singular applications.

Hypertrade is a web-based Retail Collaboration platform built by retail experts. Our clients benefit from our ability to strengthen fundamentals, increase capabilities, drive organizational change, or develop new concepts, business models, and business models. We offer Category Management and Customer Behavior Intelligences through data collaboration. This allows us to optimize range, assortment, promotions.

The TigerGraph™, a graph platform based on its Native Parallel Graph™, technology, represents the next evolution in graph database evolution. It is a complete, distributed parallel graph computing platform that supports web-scale data analytics in real time. Combining the best ideas (MapReduce, Massively Parallel Processing, and fast data compression/decompression) with fresh development, TigerGraph delivers what you've been waiting for: the speed, scalability, and deep exploration/querying capability to extract more business value from your data.

Create rich, interactive dashboards to accelerate your analytical insights throughout your enterprise. Cloudera Data Visualization allows data engineers, data scientists, and business analysts to explore data, collaborate and share insights throughout the data lifecycle - from data ingest through to data insights. Data Visualization, a native Cloudera product, provides a consistent data visualization experience that is easy to use. It includes drag-and drop dashboards and custom applications. SDX provides full security for Data Visualization, enabling enhanced data workflows in all your data and analytics workflows. Cloudera Machine Learning can be used to build predictive applications, or you can leverage your data warehouse for fast intelligent reporting.

FalkorDB is an exceptionally rapid, multi-tenant graph database that is finely tuned for GraphRAG, ensuring accurate and relevant AI/ML outcomes while minimizing hallucinations and boosting efficiency. By utilizing sparse matrix representations alongside linear algebra, it adeptly processes intricate, interconnected datasets in real-time, leading to a reduction in hallucinations and an increase in the precision of responses generated by large language models. The database is compatible with the OpenCypher query language, enhanced by proprietary features that facilitate expressive and efficient graph data querying. Additionally, it incorporates built-in vector indexing and full-text search functions, which allow for intricate search operations and similarity assessments within a unified database framework. FalkorDB's architecture is designed to support multiple graphs, permitting the existence of several isolated graphs within a single instance, which enhances both security and performance for different tenants. Furthermore, it guarantees high availability through live replication, ensuring that data remains perpetually accessible, even in high-demand scenarios. This combination of features positions FalkorDB as a robust solution for organizations seeking to manage complex graph data effectively.

The foundation of creating intelligent systems lies in the database, and Grakn serves as a sophisticated knowledge graph database. It features an incredibly user-friendly and expressive data schema that allows for the definition of hierarchies, hyper-entities, hyper-relations, and rules to establish detailed knowledge models. With its intelligent language, Grakn executes logical inferences on data types, relationships, attributes, and intricate patterns in real-time across distributed and stored data. It also offers built-in distributed analytics algorithms, such as Pregel and MapReduce, which can be accessed using straightforward queries within the language. The system provides a high level of abstraction over low-level patterns, simplifying the expression of complex constructs while optimizing query execution automatically. By utilizing Grakn KGMS and Workbase, enterprises can effectively scale their knowledge graphs. Furthermore, this distributed database is engineered to function efficiently across a network of computers through techniques like partitioning and replication, ensuring seamless scalability and performance.