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Upsolver makes it easy to create a governed data lake, manage, integrate, and prepare streaming data for analysis. Only use auto-generated schema on-read SQL to create pipelines. A visual IDE that makes it easy to build pipelines. Add Upserts to data lake tables. Mix streaming and large-scale batch data. Automated schema evolution and reprocessing of previous state. Automated orchestration of pipelines (no Dags). Fully-managed execution at scale Strong consistency guarantee over object storage Nearly zero maintenance overhead for analytics-ready information. Integral hygiene for data lake tables, including columnar formats, partitioning and compaction, as well as vacuuming. Low cost, 100,000 events per second (billions every day) Continuous lock-free compaction to eliminate the "small file" problem. Parquet-based tables are ideal for quick queries.

Tenzir is a specialized data pipeline engine tailored for security teams, streamlining the processes of collecting, transforming, enriching, and routing security data throughout its entire lifecycle. It allows users to efficiently aggregate information from multiple sources, convert unstructured data into structured formats, and adjust it as necessary. By optimizing data volume and lowering costs, Tenzir also supports alignment with standardized schemas such as OCSF, ASIM, and ECS. Additionally, it guarantees compliance through features like data anonymization and enhances data by incorporating context from threats, assets, and vulnerabilities. With capabilities for real-time detection, it stores data in an efficient Parquet format within object storage systems. Users are empowered to quickly search for and retrieve essential data, as well as to reactivate dormant data into operational status. The design of Tenzir emphasizes flexibility, enabling deployment as code and seamless integration into pre-existing workflows, ultimately seeking to cut SIEM expenses while providing comprehensive control over data management. This approach not only enhances the effectiveness of security operations but also fosters a more streamlined workflow for teams dealing with complex security data.

ATA is an intelligent API management platform driven by AI that consolidates design, testing, governance, documentation, and lifecycle workflows into a cohesive workspace, enabling teams to efficiently design, develop, test, maintain, and oversee APIs with enhanced quality and collaboration. This platform ensures that API code, design documentation, specifications, test cases, and release notes are consistently synchronized, minimizing manual tasks and discrepancies while accommodating OpenAPI specifications, facilitating frontend progress with mock servers even before the backend is ready, and enabling scheduled API monitoring to catch slow responses, timeouts, or failures at an early stage. Additionally, it features a Developer Studio that supports design-first OpenAPI creation and version control, offers end-to-end test automation with AI-generated robustness and security assessments, includes mock servers, and enables chained API workflows along with UI automation testing, all backed by a Documentation Portal that centralizes API documentation with support for multiple editors, version management, secure access controls, and automatically linked live endpoints. Ultimately, ATA streamlines the entire API lifecycle, fostering better collaboration and efficiency among development teams.

Consolidate offline data, import various files, and meticulously process and refine the information. With just a single click, you can export everything to a live database, thereby optimizing your data workflows. Centralize offline information, ensuring that your team can easily access it. Transfer offline files to Dropbase in multiple formats, accommodating any preferences you may have. Process and format your data seamlessly, allowing for additions, edits, reordering, and deletions of processing steps as needed. Enjoy the convenience of 1-click exports, whether to a database, endpoints, or downloadable code. Gain instant REST API access to securely query your Dropbase data using REST API access keys. Onboard your data wherever necessary, and combine multiple datasets to fit your required format or data model without needing to write any code. Manage your data pipelines effortlessly through a user-friendly spreadsheet interface, tracking every step of the process. Benefit from flexibility by utilizing a library of pre-built processing functions or by creating your own as you see fit. With 1-click exports, you can easily manage databases and credentials, ensuring a smooth and efficient data management experience. This system empowers teams to work more collaboratively and efficiently, transforming how they handle data.

GitCode serves as a worldwide open-source community and code-hosting platform that aggregates and mirrors repositories, enabling rapid code exploration and effortless project collaboration through a single, cohesive interface. At its foundation lies an advanced code search engine that allows users to query various open-source projects, models, datasets, issues, pull requests, users, and organizations, offering keyword filtering by language, stars, forks, and update time, along with highlighted results and customizable sorting to quickly find exactly what they need. In addition to the search functionality, GitCode provides online project browsing featuring automatic folding of empty directories, a Markdown editor that supports emojis, and visual representations like table and Kanban board views for effective issue and task management. The comprehensive permission matrix empowers teams to establish interdependent, role-based access controls while mitigating the risk of configuration errors, and the natural-language OpenAPI endpoint allows for seamless integration of repository metadata into personalized workflows. Furthermore, the platform continuously evolves to incorporate user feedback, ensuring that it meets the changing needs of developers and teams worldwide.

Gentoro is a comprehensive platform designed to enable enterprises to effectively harness agentic automation by seamlessly integrating AI agents with existing real-world systems in a secure and scalable manner. It operates on the Model Context Protocol (MCP), which empowers developers to effortlessly transform OpenAPI specifications or backend endpoints into production-ready MCP Tools, eliminating the need for manual integration coding. The platform efficiently addresses runtime challenges such as logging, retries, monitoring, and cost management, while simultaneously ensuring secure access, audit trails, and governance policies, including OAuth support and policy enforcement, regardless of whether it is deployed in a private cloud or an on-premises environment. Notably, Gentoro is model- and framework-agnostic, allowing for flexibility in integrating various large language models (LLMs) and agent architectures. This versatility aids in preventing vendor lock-in and streamlines the orchestration of tools within enterprise settings, as it manages tool generation, runtime operations, security measures, and ongoing maintenance all within a single integrated stack. By providing a unified solution, Gentoro enhances operational efficiency and simplifies the journey toward automation for businesses.

Crow is an innovative language user interface tailored for contemporary software, designed specifically for developers to seamlessly integrate a fully functional AI copilot into their applications with minimal effort. Rather than starting from scratch to create a chatbot or copilot—complete with backend endpoint wiring, user interface design, user state management, contextual handling, and tool call capabilities—Crow simplifies the entire process. By incorporating just a small script into your frontend, Crow takes charge of everything else; it interfaces with your backend endpoints, transforms registered APIs (through OpenAPI specifications or endpoint URLs) into actionable tools, and oversees authentication to ensure that AI-driven tasks align with your current user permissions. To enhance the copilot's contextual awareness, Crow allows the ingestion of website content, documentation, or various files, enabling the AI to accurately answer questions specific to your domain. Once set up, the copilot is capable of not only engaging in conversational exchanges but also performing actions like reading and writing data. This streamlined integration ultimately empowers developers to enhance user experiences with advanced AI functionalities.

The API Agent in IBM API Connect serves as a powerful assistant driven by watsonx.ai, streamlining essential functions throughout the entire API lifecycle through an intuitive conversational interface. It operates on an agentic framework, enabling teams to quickly create OpenAPI specifications, generate mocked responses, and produce comprehensive documentation for design-oriented projects. Additionally, it facilitates seamless connections to backend data sources, aids in application code development, and allows for automatic deployment to Code Engine for code-focused workflows, all while requiring no manual configuration. To address the challenges of API sprawl, the API Agent effectively searches through your existing API catalog using straightforward description prompts, recommending reusable endpoints and minimizing redundancy. It also strengthens governance by ensuring that specifications comply with organizational guidelines, providing suggestions or making necessary corrections autonomously. Furthermore, it enhances quality assurance with an integrated testing suite that generates and executes semantic test cases, enabling the early detection of potential issues and contributing to a more robust API management process. Overall, the API Agent is a comprehensive solution that significantly improves efficiency and quality in API development and management.

Store data in its native format for graph, document, and search purposes. Leverage a comprehensive query language that allows for rich access to this data. Map the data directly to the database and interact with it through optimal methods tailored for specific tasks, such as traversals, joins, searches, rankings, geospatial queries, and aggregations. Experience the benefits of polyglot persistence without incurring additional costs. Design, scale, and modify your architectures with ease to accommodate evolving requirements, all while minimizing effort. Merge the adaptability of JSON with advanced semantic search and graph technologies, enabling the extraction of features even from extensive datasets, thereby enhancing data analysis capabilities. This combination opens up new possibilities for handling complex data scenarios efficiently.

The Globessey Data Server (GDS) serves as a comprehensive centralized solution for collecting, managing, and visualizing vast amounts of traffic data from various sources. It offers smooth integration with Adaptive Recognition's ANPR/LPR cameras alongside other third-party devices, which enhances the efficiency of data aggregation and analysis. With its foundation on the powerful ELK stack, GDS ensures secure data storage and features user-friendly dashboards that include advanced visualization options like heatmaps and geofencing filters. The platform's intuitive interface makes it easy to deploy and register devices, while the provided OpenAPI documentation and SDK samples cater to flexible development needs. Additionally, GDS's compatibility with both Windows and Linux operating systems contributes to its adaptability for traffic management, smart city infrastructure, and security applications. As cities increasingly rely on data-driven solutions, GDS stands out as a pivotal tool for enhancing urban mobility and safety.

Sliq is an innovative platform powered by artificial intelligence that swiftly cleans up disorganized raw datasets, making them ready for analysis within minutes by automatically identifying and resolving prevalent quality concerns such as format discrepancies, absent values, schema variations, and formatting mistakes. This efficiency allows analysts and engineers to minimize time spent on tedious maintenance tasks and focus more on deriving insights and building models. By utilizing context-sensitive intelligence, Sliq comprehends the semantic context of the uploaded datasets—whether they pertain to finance, e-commerce, or healthcare—and devises a customized cleaning strategy tailored specifically for each dataset instead of relying on generic solutions. Users have the flexibility to either upload files directly or connect programmatically with existing workflows, and Sliq is compatible with popular data formats like CSV, JSON, and Parquet, ensuring smooth integration into current data environments. Additionally, this platform enhances productivity by streamlining the data preparation process, allowing teams to drive more impactful decision-making through improved data quality.

Konfig is an innovative developer tool designed to streamline the creation of SDKs, documentation, demos, and tutorials for REST APIs, making the onboarding process for external developers more efficient. By simply importing an OpenAPI Specification or Postman Collection, Konfig can automatically generate SDKs in widely-used programming languages such as TypeScript, Python, Java, C#, PHP, Ruby, Go, Swift, and Dart. The platform enhances the quality of these SDKs by utilizing a linter to detect and correct errors in the OpenAPI Specification and by generating test cases to ensure that updates to the API do not disrupt existing SDK functionality. Furthermore, Konfig produces intuitive, branded documentation that updates automatically in response to any modifications in the API specification, thereby ensuring consistency between the documentation and the generated SDKs. Users can also create interactive demos and tutorials using familiar Markdown, which includes the capability to execute code directly in the browser for a more immersive learning experience. Overall, Konfig not only simplifies the technical aspects of API integration but also enriches the educational resources available to developers.

Apache DataFusion is a versatile and efficient query engine crafted in Rust, leveraging Apache Arrow for its in-memory data representation. It caters to developers engaged in creating data-focused systems, including databases, data frames, machine learning models, and real-time streaming applications. With its SQL and DataFrame APIs, DataFusion features a vectorized, multi-threaded execution engine that processes data streams efficiently and supports various partitioned data sources. It is compatible with several native formats such as CSV, Parquet, JSON, and Avro, and facilitates smooth integration with popular object storage solutions like AWS S3, Azure Blob Storage, and Google Cloud Storage. The architecture includes a robust query planner and an advanced optimizer that boasts capabilities such as expression coercion, simplification, and optimizations that consider distribution and sorting, along with automatic reordering of joins. Furthermore, DataFusion allows for extensive customization, enabling developers to incorporate user-defined scalar, aggregate, and window functions along with custom data sources and query languages, making it a powerful tool for diverse data processing needs. This adaptability ensures that developers can tailor the engine to fit their unique use cases effectively.

Mobula offers curated datasets: market data from Octopus, wallet data from Metacore, metadata from Metacore, along with REST, GraphSQL & SQL query interfaces.

Cloud Endpoints allows you to create, launch, safeguard, and oversee your APIs effectively. With a robust NGINX-based proxy and a distributed framework, it ensures exceptional performance and scalability. By utilizing either an OpenAPI Specification or one of the provided API frameworks, Cloud Endpoints equips you with essential tools for every stage of API development while offering valuable insights via Cloud Logging, Cloud Monitoring, and Cloud Trace. You can manage access to your API and authenticate each request using JSON Web Tokens and Google API keys. Additionally, seamless integration with Auth0 and Firebase Authentication enables user identification for your web or mobile applications. The Extensible Service Proxy guarantees security and analytics in under 1 millisecond per request. You can effortlessly deploy your API using App Engine and Google Kubernetes Engine or incorporate our proxy container into your Kubernetes setup. Furthermore, you can track essential operational metrics through the Google Cloud Console and obtain deeper insights into user behavior and usage patterns utilizing Cloud Trace, Cloud Logging, and BigQuery. This comprehensive approach ensures that your APIs are not only secure but also optimized for performance and usability.

A parallel query engine designed for efficient access to time- and symbol-indexed data. It incorporates an extended SQL syntax that allows for sophisticated filtering and aggregation capabilities. Users can unify quotes, trades, snapshots, and reference data within a single environment. The platform supports strategy backtesting using high-frequency data for enhanced analysis. It facilitates quantitative research and insights into market microstructure. Additionally, it offers detailed transaction cost analysis and comprehensive rollup reporting features. Market surveillance mechanisms and anomaly detection capabilities are also integrated into the system. The decomposition of non-transparent ETF/ETN instruments is supported, along with the utilization of FAST, SBE, and proprietary communication protocols. A plain text protocol is available alongside consolidated and direct data feeds. The system includes built-in tools for monitoring latency and provides end-of-day archival options. It can perform ETL processes from both institutional and retail financial data sources. Designed with a parallel SQL engine that features syntax extensions, it allows advanced filtering by trading session, auction stage, and index composition for precise analysis. Optimizations for aggregates related to OHLCV and VWAP calculations enhance performance. An interactive SQL console with auto-completion improves user experience, while an API endpoint facilitates seamless programmatic integration. Scheduled SQL reporting options are available, allowing delivery via email, file, or web. JDBC and ODBC drivers ensure compatibility with various applications, making this system a versatile tool for financial data handling.

Create-API.dev is an innovative tool powered by AI, developed by Kong, that enables users to swiftly create high-quality OpenAPI specifications through an intuitive chat interface. By simply communicating your intended endpoints or a rough concept, the service utilizes a sophisticated Google LLM to produce comprehensive, standards-compliant OpenAPI definitions that are ready for sharing, testing, and deployment. This web-based application is lightweight and requires no installation, allowing for easy access from anywhere. The outputted specifications can be seamlessly exported in standard YAML or JSON formats, facilitating integration with existing API gateways and documentation processes. Furthermore, Create-API.dev adheres to Google’s Generative AI Prohibited Use Policy, emphasizing the importance of exercising caution before using or publishing any content generated by AI, highlighting the necessity for human oversight in the final implementation. Users can confidently leverage this tool while remaining mindful of the guidelines to ensure optimal results.

The true asset in your intelligence framework lies not in AI, but in the expertise of your developers. Equip them with the necessary tools to take charge of API security, ensuring consistent and exceptional protection throughout the entire API lifecycle. Integrate your OpenAPI definition seamlessly into your CI/CD pipeline to enable automatic auditing, scanning, and safeguarding of your API. By evaluating your OpenAPI/Swagger file against over 300 security vulnerabilities, we will prioritize them according to severity and provide precise remediation instructions, thus embedding security into your development processes effortlessly. Implement a zero-trust architecture by verifying that all APIs adhere to a defined security standard prior to production, actively scanning live API endpoints for potential risks and automating redeployment as needed. Maintain the integrity of your APIs from the design phase to deployment, gaining comprehensive insights into attacks targeting APIs in production, while also defending against threats without compromising performance. This proactive approach to security not only strengthens your defenses but also fosters a culture of vigilance within your development team.

Y42 is the first fully managed Modern DataOps Cloud for production-ready data pipelines on top of Google BigQuery and Snowflake.

Kiota serves as a versatile client, plugin, and manifest generator tailored for HTTP REST APIs that are defined by OpenAPI specifications. It is accessible both as a command-line utility and a Visual Studio Code (VS Code) extension, providing developers with the ability to efficiently search through API descriptions, filter and choose specific endpoints, and create models along with a methodically chained API surface in multiple programming languages. This innovative solution reduces reliance on varied API clients for different APIs, allowing for the precise crafting of the necessary API surface area. Furthermore, Kiota contributes to the Microsoft Copilot ecosystem by supporting the generation of API plugins, thus enhancing its utility. The VS Code extension enriches the Kiota experience with an intuitive user interface that includes features such as API description searches, endpoint filtering, and client generation. Users can easily pick the endpoints they need and generate not just clients but also plugins and other outputs, all while receiving completion notifications and having straightforward access to generated outputs right within their workspace. This comprehensive functionality makes Kiota a powerful tool for streamlining API interactions and development processes.

OpenObserve is a robust open-source observability platform designed for managing logs, metrics, and traces, focusing on exceptional performance, scalability, and significantly reduced costs. It enables observability at a petabyte scale by incorporating features like columnar storage data compression and the flexibility of “bring your own bucket” storage options, including local disks and cloud services such as S3, GCS, and Azure Blob. Developed in Rust, it utilizes the DataFusion query engine for direct querying of Parquet files, and it boasts a stateless, horizontally scalable framework that employs caching strategies for both results and disk to ensure rapid performance even during peak loads. By adhering to open standards, including compatibility with OpenTelemetry and vendor-neutral APIs, OpenObserve seamlessly integrates into pre-existing monitoring and logging ecosystems. Its essential components encompass logs, metrics, traces, frontend monitoring, pipelines, alerts, and comprehensive dashboards for visualizations. Ultimately, OpenObserve empowers organizations to achieve efficient and cost-effective observability solutions in their operations.

Data is essential to improve the efficiency, profitability, and experience of streaming video. Datazoom allows video publishers to manage distributed architectures more efficiently by centralizing, standardizing and integrating data in real time. This creates a more powerful data pipeline, improves observability and adaptability, as well as optimizing solutions. Datazoom is a video data platform which continuously gathers data from endpoints such as a CDN or video player through an ecosystem of collectors. Once the data has been gathered, it is normalized with standardized data definitions. The data is then sent via available connectors to analytics platforms such as Google BigQuery, Google Analytics and Splunk. It can be visualized using tools like Looker or Superset. Datazoom is your key for a more efficient and effective data pipeline. Get the data you need right away. Do not wait to get your data if you have an urgent issue.

Google Earth Engine serves as a cloud-centric platform designed for the scientific examination and visualization of geospatial data, granting users access to an extensive public archive containing over 90 petabytes of analysis-ready satellite imagery alongside more than 1,000 carefully curated geospatial datasets. This rich collection boasts over five decades of historical imagery that is refreshed daily, with pixel resolutions reaching as fine as one meter, showcasing datasets from sources such as Landsat, MODIS, Sentinel, and the National Agriculture Imagery Program (NAIP). Through its web-based JavaScript Code Editor and Python API, Earth Engine empowers users to perform analyses on Earth observation data while employing machine learning techniques, thereby enabling the creation of sophisticated geospatial workflows. The platform's seamless integration with Google Cloud facilitates large-scale parallel processing, allowing for thorough analyses and efficient visualization of Earth data. Furthermore, Earth Engine's compatibility with BigQuery enhances its capabilities, making it a versatile tool for users in various fields. This unique combination of features positions Google Earth Engine as an essential resource for researchers and professionals working with geospatial information.

RxDB is a NoSQL database designed for local-first use, particularly suited for contemporary web and mobile applications. It facilitates offline-first capabilities by saving data directly on the client side through various storage engines such as IndexedDB, OPFS, and SQLite. With real-time reactivity, RxDB allows developers to monitor changes in documents, fields, or queries, which means that UI components can reflect data updates instantly. Its adaptable replication engine offers synchronization with multiple backends and custom endpoints, providing flexibility for various use cases. Furthermore, RxDB easily integrates with different frameworks and environments, enhancing its usability. Key features include field-level encryption, schema validation, conflict resolution, backup and restoration options, as well as support for attachments and CRDTs. By minimizing server demands and enabling rapid local queries, RxDB significantly boosts performance and scalability, making it a prime choice for applications that need real-time updates, offline functionality, and uniformity across platforms. The combination of these capabilities positions RxDB as a forward-thinking solution for developers aiming to build efficient and responsive applications.

CharityBase serves as a free and open-source database along with a GraphQL API, consolidating disparate data sources from organizations such as the Charity Commission, Companies House, 360 Giving, various charity websites, the ONS, and social media into a unified, cleaned, normalized, and easily searchable dataset. This platform facilitates a public portal where users can access comprehensive profiles of UK charities, which include financial information, governance structures, and activity details, while also providing a single GraphQL endpoint that generates structured JSON responses for custom queries regarding counts, aggregates, and detailed listings. Aimed at simplifying the burdensome tasks of data collection and cleaning, CharityBase empowers startups, grantmakers, and researchers to create digital tools like dashboards, reports, and applications for grant finding, all without the need to maintain their own data management systems. Additionally, its API is built to accommodate both GET and POST requests, supports variable-driven queries and pagination, and features live interactive playgrounds for efficient prototyping, all supported by consistently updated records that maintain an audit trail. Furthermore, this streamlined approach not only enhances accessibility to vital data but also fosters a collaborative environment for innovation within the charity sector.

One Auto API serves as a cohesive platform for automotive data, enabling streamlined access to a diverse array of vehicle and market insights by merging multiple top-tier automotive data providers into a single, developer-centric interface. Instead of the cumbersome task of integrating various data sources that come with distinct formats and agreements, users can simply query one endpoint to obtain extensive automotive datasets, which encompass vehicle specifications, retail pricing, valuations, technical details, market metrics, registration checks, tyre fitments, and historical data sourced from partners such as Auto Trader, Experian, Brego, DriveRightData, among others. Additionally, it provides detailed SDKs across more than 30 programming languages, offers sandbox environments for testing, and includes OpenAPI/Swagger documentation to facilitate swift integration of data into applications, websites, dealer portals, and software systems, ultimately minimizing engineering burdens and expediting the product development process. As a result, developers can focus more on innovation rather than the complexities of data integration.

Utilize Pipes to query and manipulate your data seamlessly, a novel method for linking SQL queries that draws inspiration from Python Notebooks. This approach aims to streamline complexity while maintaining optimal performance. By dividing your query into various nodes, you enhance both development and maintenance processes. With just a single click, you can activate your API endpoints that are ready for production use. Transformations happen instantly, ensuring you always have access to the most current data. You can securely share access to your data with just one click, providing quick and reliable results. In addition to offering monitoring capabilities, Tinybird is designed to scale effortlessly, so you need not be concerned about unexpected traffic surges. Visualize transforming any Data Stream or CSV file into a fully secured real-time analytics API endpoint in mere minutes. We advocate for high-frequency decision-making across every sector, including retail, manufacturing, telecommunications, government, advertising, entertainment, healthcare, and financial services, making data-driven insights accessible to all types of organizations. Our commitment is to empower businesses to make informed decisions swiftly, ensuring they stay ahead in an ever-evolving landscape.

Enhance your business operations by utilizing our flow builder to automate your pipeline efficiently. Select and implement the operations that are essential for your needs. You can integrate your preferred service providers for email, SMS, and HTTP services seamlessly. Leverage our sophisticated query builder to analyze CSV files containing any number of fields and rows. All uploaded CSV files are safely stored in our secure vault, alongside comprehensive account activity logs. Rest assured, we do not retain any data records that you request for processing, ensuring your information remains private. Our platform is designed to prioritize your security and operational efficiency.

Dataform provides a platform for data analysts and engineers to create and manage scalable data transformation pipelines in BigQuery using solely SQL from a single, integrated interface. The open-source core language allows teams to outline table structures, manage dependencies, include column descriptions, and establish data quality checks within a collective code repository, all while adhering to best practices in software development, such as version control, various environments, testing protocols, and comprehensive documentation. A fully managed, serverless orchestration layer seamlessly oversees workflow dependencies, monitors data lineage, and executes SQL pipelines either on demand or on a schedule through tools like Cloud Composer, Workflows, BigQuery Studio, or external services. Within the browser-based development interface, users can receive immediate error notifications, visualize their dependency graphs, link their projects to GitHub or GitLab for version control and code reviews, and initiate high-quality production pipelines in just minutes without exiting BigQuery Studio. This efficiency not only accelerates the development process but also enhances collaboration among team members.

Leverage Reactive Programming on the back-end without the hassle of using specialized languages or complex frameworks. With the help of Triggers, Actors, and Event Collections, it becomes straightforward to create data pipelines and manage data streams through an intuitive SQL-like syntax, effectively simplifying the challenges associated with distributed system development. A standout aspect is the Extensible Data Modeling feature, which enables rich semantics and schema definitions to accurately represent real-world entities. The implementation of on-the-fly validation and data shaping rules accommodates various formats, including XML and JSON, making it effortless to articulate and adapt your schema in line with evolving business needs. If you can articulate it, we have the capability to query it. If you're familiar with SQL and JavaScript, you're already equipped to navigate the data engine. No matter the format, a robust query language allows for immediate testing of logic expressions and functions, which accelerates development and streamlines deployment, resulting in exceptional data agility and responsiveness to changing circumstances. This adaptability not only enhances productivity but also fosters innovation within teams.

Pinot is built to efficiently handle OLAP queries on static data with minimal latency. It incorporates various pluggable indexing methods, including Sorted Index, Bitmap Index, and Inverted Index. While it currently lacks support for joins, this limitation can be mitigated by utilizing Trino or PrestoDB for querying purposes. The system offers an SQL-like language that enables selection, aggregation, filtering, grouping, ordering, and distinct queries on datasets. It comprises both offline and real-time tables, with real-time tables being utilized to address segments lacking offline data. Additionally, users can tailor the anomaly detection process and notification mechanisms to accurately identify anomalies. This flexibility ensures that users can maintain data integrity and respond proactively to potential issues.

Tad is an open-source desktop application available under the MIT License, designed specifically for the visualization and analysis of tabular data. This application serves as a swift viewer for various file types, including CSV and Parquet, as well as databases like SQLite and DuckDb, making it capable of handling large datasets efficiently. Acting as a Pivot Table tool, it facilitates in-depth data exploration and analysis. For its internal processing, Tad relies on DuckDb, ensuring rapid and precise data handling. It has been crafted to seamlessly integrate into the workflows of data engineers and scientists alike. Recent updates to Tad include enhancements to DuckDb 1.0, the functionality to export filtered tables in both Parquet and CSV formats, improvements in handling scientific notation for numbers, along with various minor bug fixes and upgrades to dependent packages. Additionally, a convenient packaged installer for Tad is accessible for users on macOS (supporting both x86 and Apple Silicon), Linux, and Windows platforms, broadening its accessibility for a diverse range of users. This comprehensive set of features makes Tad an invaluable tool for anyone working with data analysis.

OpenAPI Generator is a versatile open-source utility that facilitates the automatic generation of client libraries, server stubs, API documentation, and configuration files based on an OpenAPI Specification (OAS) document. With support for numerous programming languages and frameworks, it streamlines the process for developers looking to incorporate APIs into their applications efficiently. By automating the generation of boilerplate code, OpenAPI Generator not only shortens development time but also promotes uniformity in API interactions. This empowers teams to concentrate on executing business logic instead of getting bogged down by repetitive tasks such as data serialization, deserialization, and managing HTTP requests. The tool is particularly valuable in API-driven development scenarios, enhancing the integration of third-party services and making it easier to maintain alignment between API consumers and providers. Ultimately, OpenAPI Generator serves as a crucial resource for development teams aiming to boost productivity and ensure high-quality API integrations.

AutoRest is a powerful tool that facilitates the generation of client libraries for interacting with RESTful web services. It requires a specification that outlines the REST API using the OpenAPI format, enabling the streamlined production of client code in various programming languages like C#, Java, Python, TypeScript, and Go. By automating this process, AutoRest significantly improves consistency and efficiency in API usage, thereby minimizing the manual labor involved in creating and maintaining client libraries. The tool utilizes a versatile pipeline to process OpenAPI input files, converting them into a code model that language-specific generators then use to create client code that conforms to the design principles of each language. Supporting both OpenAPI 2.0 and 3.0 specifications, AutoRest ensures broad compatibility with numerous APIs. Additionally, developers can easily install AutoRest on any major operating system, including Windows, macOS, or Linux, using the Node.js package manager for a straightforward setup experience. This accessibility makes it an invaluable resource for developers looking to streamline their API integration processes.

Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.

Experience serverless and interactive data querying with IBM Cloud Object Storage, enabling you to analyze your data directly at its source without the need for ETL processes, databases, or infrastructure management. IBM Cloud SQL Query leverages Apache Spark, a high-performance, open-source data processing engine designed for quick and flexible analysis, allowing SQL queries without requiring ETL or schema definitions. You can easily perform data analysis on your IBM Cloud Object Storage via our intuitive query editor and REST API. With a pay-per-query pricing model, you only incur costs for the data that is scanned, providing a cost-effective solution that allows for unlimited queries. To enhance both savings and performance, consider compressing or partitioning your data. Furthermore, IBM Cloud SQL Query ensures high availability by executing queries across compute resources located in various facilities. Supporting multiple data formats, including CSV, JSON, and Parquet, it also accommodates standard ANSI SQL for your querying needs, making it a versatile tool for data analysis. This capability empowers organizations to make data-driven decisions more efficiently than ever before.

Transform your geospatial information into meaningful maps in a matter of minutes. Leverage our comprehensive layer catalog and sophisticated timeline animation features. Manipulate your data effortlessly with our user-friendly geospatial analytics tools. Gain insights rapidly through seamless exploration in your browser, complete with instant visual feedback. Share your maps effortlessly with your team at the click of a button. Craft your own narratives and disseminate compelling data stories to a global audience. Enjoy a user-friendly experience that simplifies the complexities of geospatial data science. Integrate Shapefiles, Vector Tiles, and Cloud-Optimized GeoTIFFs alongside conventional data formats such as CSV and GeoJSON. Conduct thorough analyses by merging tables and aggregating rows. Utilize cross-filtering capabilities to correlate columns through personalized metrics. Create sleek web applications built on your published maps, allowing for rapid iteration with our well-documented, user-friendly API. Execute geospatial joins across various data types, enhancing the depth of your analysis and storytelling. The possibilities for exploration and insight generation are truly limitless.

BigLake serves as a storage engine that merges the functionalities of data warehouses and lakes, allowing BigQuery and open-source frameworks like Spark to efficiently access data while enforcing detailed access controls. It enhances query performance across various multi-cloud storage systems and supports open formats, including Apache Iceberg. Users can maintain a single version of data, ensuring consistent features across both data warehouses and lakes. With its capacity for fine-grained access management and comprehensive governance over distributed data, BigLake seamlessly integrates with open-source analytics tools and embraces open data formats. This solution empowers users to conduct analytics on distributed data, regardless of its storage location or method, while selecting the most suitable analytics tools, whether they be open-source or cloud-native, all based on a singular data copy. Additionally, it offers fine-grained access control for open-source engines such as Apache Spark, Presto, and Trino, along with formats like Parquet. As a result, users can execute high-performing queries on data lakes driven by BigQuery. Furthermore, BigLake collaborates with Dataplex, facilitating scalable management and logical organization of data assets. This integration not only enhances operational efficiency but also simplifies the complexities of data governance in large-scale environments.

EraDB is an innovative database architecture that emphasizes the separation of storage and computing, offers true zero-schema data storage, and incorporates machine learning for adaptable indexing, enabling users to minimize data size, cost, and complexity while ensuring rapid queries over extensive datasets. With automatic indexing on all dimensions, you don't need to determine your query requirements in advance, while its schemaless nature allows for the storage of data with varying structures. Designed for adaptability, EraDB allows for interchangeable front-end and back-end systems, catering to diverse user needs. In contrast to traditional storage engines that struggle with complex data, leading to crashes or significant slowdowns, EraDB ensures efficient handling of intricate datasets without compromising performance. This makes it a robust solution for modern data management challenges.

Redis Enterprise offers a robust real-time indexing, querying, and full-text search engine that is accessible both on-premises and as a cloud-managed service. This real-time search capability is optimized for rapid indexing and data ingestion, utilizing high-performance in-memory data structures developed in C. You can expand and partition indexes across multiple shards and nodes, enhancing both speed and memory capacity. With an impressive five-nines availability and Active-Active failover, uninterrupted operations are ensured in any circumstance. The real-time search feature of Redis Enterprise enables users to swiftly establish primary and secondary indexes on Hash and JSON datasets through an incremental indexing method, which facilitates quick index creation and removal. These indexes empower users to perform queries at remarkable speeds, execute complex aggregations, and filter data based on properties, numeric ranges, and geographical distances, thus enhancing overall data accessibility. By leveraging these capabilities, organizations can significantly improve their data management and retrieval processes.

Microsoft Graph serves as the essential link for organizations to access Microsoft 365 data, focusing on productivity, identity, and security. The Microsoft Graph Data Connect feature allows developers to securely and efficiently transfer selected Microsoft 365 datasets into Azure data stores. This functionality is particularly beneficial for training machine learning and AI models that can derive valuable insights for enhanced analytics solutions. Developers can easily copy large volumes of data from a Microsoft 365 tenant directly into Azure Data Factory without needing to write any code. This streamlined process ensures that organizations can obtain the required data, delivered to their applications on a regular schedule, with just a few straightforward steps. Furthermore, the Microsoft Graph Data Connect includes a granular consent model that empowers organizations to manage how their data is accessed. This model mandates that developers clearly define the specific data types or content filters their applications will utilize. Additionally, administrators are required to provide explicit consent before any access to Microsoft 365 data is permitted, ensuring a secure and controlled environment for data handling. In this way, organizations can effectively leverage their data while maintaining strict oversight and compliance.

Enhance the efficiency of API development for individuals, teams, and large organizations using the Swagger open-source and professional suite of tools. Discover how Swagger can assist in designing and documenting APIs effectively on a large scale. The strength of Swagger tools is rooted in the OpenAPI Specification, which serves as the industry standard for designing RESTful APIs. There are various tools available to create, modify, and share OpenAPI definitions with different stakeholders. For comprehensive support of OpenAPI workflows, SwaggerHub serves as the platform solution that scales effectively. Millions of API developers, teams, and enterprises have benefited from Swagger's tools to create exceptional APIs. With Swagger, you gain access to the most robust and user-friendly tools that leverage the full potential of the OpenAPI Specification, ensuring a seamless development process that can adapt to evolving needs.

Inflectiv is a robust data platform that transforms raw files into organized datasets tailored for AI agents and automation tasks. Users are able to upload various file types, including PDFs, documents, spreadsheets, JSON files, and even website content. Inflectiv seamlessly structures this data, enabling it to be queried through APIs, SDKs, or integrated chat agents. Rather than dealing with unstructured documents, AI agents can interact directly with well-organized datasets that facilitate filtering, querying, and delivering consistent responses. This platform enables the development of Q&A chatbots, bots for Discord and Telegram, internal knowledge assistants, and applications powered by datasets. Users have the flexibility to keep their datasets private, share them with team members, or publish them on the marketplace for broader access. Importantly, creators maintain complete ownership of their data, along with the ability to manage access, permissions, and monetization options. Inflectiv is designed to cater to both technical experts and non-technical individuals who wish to convert their existing knowledge into reusable AI-ready intelligence without the need for custom ingestion processes, fostering innovation and collaboration across various fields.

Open core technology facilitates the integration of hybrid and multi-cloud environments. Built on the open-source initiative CDAP, Data Fusion guarantees portability of data pipelines for its users. The extensive compatibility of CDAP with both on-premises and public cloud services enables Cloud Data Fusion users to eliminate data silos and access previously unreachable insights. Additionally, its seamless integration with Google’s top-tier big data tools enhances the user experience. By leveraging Google Cloud, Data Fusion not only streamlines data security but also ensures that data is readily available for thorough analysis. Whether you are constructing a data lake utilizing Cloud Storage and Dataproc, transferring data into BigQuery for robust data warehousing, or transforming data for placement into a relational database like Cloud Spanner, the integration capabilities of Cloud Data Fusion promote swift and efficient development while allowing for rapid iteration. This comprehensive approach ultimately empowers businesses to derive greater value from their data assets.

Bazze is a cutting-edge platform that leverages artificial intelligence to provide intelligence targeting and early warnings by converting extensive unclassified commercial data into actionable insights as needed. Its Commercial Data Infrastructure (CDI) marketplace offers both real-time and historical datasets, which include information such as device locations, satellite imagery, and open-source intelligence, all accessible through a “query in place” API model that removes the necessity for bulk buying. Users have the ability to explore and integrate data from a growing variety of sources, utilize sophisticated filtering techniques and unique intent scoring, and present their findings through customizable dashboards or export them for further analysis. Among its specialized features are tools for reverse DNS mapping, the detection of geospatial events, tracking of trends, scoring of threats, and conducting similarity searches to uncover related entities. Continuous updates ensure that the information remains current, and the delivery is based on consumption to enhance resource management. Additionally, Bazze’s innovative approach makes it a valuable asset for organizations seeking to enhance their intelligence capabilities.