Alternatives to CData Query Federation Drivers

Oracle Data Service Integrator empowers organizations to swiftly create and oversee federated data services, allowing for unified access to diverse datasets. This tool is entirely built on standards, is declarative in nature, and promotes the reusability of data services. It stands out as the sole data federation solution that facilitates the development of bidirectional (both read and write) data services across various data sources. Moreover, it introduces an innovative feature that removes the need for coding by enabling users to graphically design both straightforward and intricate modifications to different data sources. Users can easily install, verify, uninstall, upgrade, and initiate their experience with Data Service Integrator. Initially branded as Liquid Data and AquaLogic Data Services Platform (ALDSP), Oracle Data Service Integrator still retains some references to these earlier names within its product structure, installation paths, and components. This continuity ensures that users familiar with the legacy names can still navigate the system effectively.

AWS Glue is a fully managed data integration solution that simplifies the process of discovering, preparing, and merging data for purposes such as analytics, machine learning, and application development. By offering all the necessary tools for data integration, AWS Glue enables users to begin analyzing their data and leveraging it for insights within minutes rather than taking months. The concept of data integration encompasses various activities like identifying and extracting data from multiple sources, enhancing, cleaning, normalizing, and consolidating that data, as well as organizing and loading it into databases, data warehouses, and data lakes. Different users, each utilizing various tools, often manage these tasks. Operating within a serverless environment, AWS Glue eliminates the need for infrastructure management, automatically provisioning, configuring, and scaling the resources essential for executing data integration jobs. This efficiency allows organizations to focus more on data-driven decision-making without the overhead of manual resource management.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

Establish federated source data identifiers to allow users to connect to various data sources seamlessly. Utilize a web-based administrative console to streamline the management of user access, privileges, and authorizations for easier oversight. Incorporate data quality enhancements such as match-code generation and parsing functions within the view to ensure high-quality data. Enhance performance through the use of in-memory data caches and efficient scheduling methods. Protect sensitive information with robust data masking and encryption techniques. This approach keeps application queries up-to-date and readily accessible to users while alleviating the burden on operational systems. You can set access permissions at multiple levels, including catalog, schema, table, column, and row, allowing for tailored security measures. The advanced capabilities for data masking and encryption provide the ability to control not just who can see your data but also the specific details they can access, thereby significantly reducing the risk of sensitive information being compromised. Ultimately, these features work together to create a secure and efficient data management environment.

dbExpress, a database-independent layer, provides fast access to SQL database servers using C++Builder and Delphi on Windows and macOS (32/64-bit and 64/64-bit), and Linux (64/64-bit). dbExpress provides a driver for each supported server. This independent library implements the common interfaces of dbExpress for processing queries and stored procedure. dbExpress is simple and thin, and provides high-performance database connectivity. It is also easy to deploy. Our drivers can be used in the same manner as Embarcadero's.

Connect and centralize data. Transform your data landscape into a flexible powerhouse. Data Virtuality is a data integration platform that allows for instant data access, data centralization, and data governance. Logical Data Warehouse combines materialization and virtualization to provide the best performance. For high data quality, governance, and speed-to-market, create your single source data truth by adding a virtual layer to your existing data environment. Hosted on-premises or in the cloud. Data Virtuality offers three modules: Pipes Professional, Pipes Professional, or Logical Data Warehouse. You can cut down on development time up to 80% Access any data in seconds and automate data workflows with SQL. Rapid BI Prototyping allows for a significantly faster time to market. Data quality is essential for consistent, accurate, and complete data. Metadata repositories can be used to improve master data management.

A comprehensive enterprise data virtualization solution enables seamless access to a variety of data sources while establishing a robust foundation of datasets and IT-managed data services suitable for virtually any application. The TIBCO® Data Virtualization system, functioning as a contemporary data layer, meets the dynamic demands of organizations with evolving architectures. By eliminating bottlenecks, it fosters consistency and facilitates reuse by providing on-demand access to all data through a unified logical layer that is secure, governed, and accessible to a wide range of users. With immediate availability of all necessary data, organizations can derive actionable insights and respond swiftly in real-time. Users benefit from the ability to effortlessly search for and choose from a self-service directory of virtualized business data, utilizing their preferred analytics tools to achieve desired outcomes. This shift allows them to concentrate more on data analysis rather than on the time-consuming task of data retrieval. Furthermore, the streamlined process enhances productivity and enables teams to make informed decisions quickly and effectively.

Oracle Big Data SQL Cloud Service empowers companies to swiftly analyze information across various platforms such as Apache Hadoop, NoSQL, and Oracle Database, all while utilizing their existing SQL expertise, security frameworks, and applications, achieving remarkable performance levels. This solution streamlines data science initiatives and facilitates the unlocking of data lakes, making the advantages of Big Data accessible to a wider audience of end users. It provides a centralized platform for users to catalog and secure data across Hadoop, NoSQL systems, and Oracle Database. With seamless integration of metadata, users can execute queries that combine data from Oracle Database with that from Hadoop and NoSQL databases. Additionally, the service includes utilities and conversion routines that automate the mapping of metadata stored in HCatalog or the Hive Metastore to Oracle Tables. Enhanced access parameters offer administrators the ability to customize column mapping and govern data access behaviors effectively. Furthermore, the capability to support multiple clusters allows a single Oracle Database to query various Hadoop clusters and NoSQL systems simultaneously, thereby enhancing data accessibility and analytics efficiency. This comprehensive approach ensures that organizations can maximize their data insights without compromising on performance or security.

Toad® Data Point is a versatile self-service data integration solution designed to streamline the processes of data access, preparation, and provisioning across multiple platforms. With its extensive data connectivity options, users can easily integrate data from a variety of sources, such as SQL and NoSQL databases, ODBC, as well as business intelligence tools and Microsoft Excel or Access. The application features a user-friendly Workbook interface that allows business users to build visual queries and automate workflows with ease. Regardless of your technical expertise, you can create queries without the need to write or modify SQL code, although those familiar with SQL will appreciate the intuitive graphical interface that enhances the creation of relationships and the visualization of queries. Toad Data Point Professional accommodates different user preferences by offering two distinct interfaces: one that emphasizes traditional flexibility and a wide range of functionalities. Additionally, this powerful tool ensures that data profiling tasks are efficiently managed, allowing users to achieve consistent and reliable results across their projects.

Adaptive Data Virtualization™ technology empowers businesses to operate their current applications on contemporary cloud data warehouses without the need for extensive modifications or reconfiguration. With Datometry Hyper-Q™, organizations can swiftly embrace new cloud databases, effectively manage ongoing operational costs, and enhance their analytical capabilities to accelerate digital transformation efforts. This virtualization software from Datometry enables any existing application to function on any cloud database, thus facilitating interoperability between applications and databases. Consequently, enterprises can select their preferred cloud database without the necessity of dismantling, rewriting, or replacing their existing applications. Furthermore, it ensures runtime application compatibility by transforming and emulating legacy data warehouse functionalities. This solution can be deployed seamlessly on major cloud platforms like Azure, AWS, and GCP. Additionally, applications can leverage existing JDBC, ODBC, and native connectors without any alterations, ensuring a smooth transition. It also establishes connections with leading cloud data warehouses, including Azure Synapse Analytics, AWS Redshift, and Google BigQuery, broadening the scope for data integration and analysis.

Varada offers a cutting-edge big data indexing solution that adeptly balances performance and cost while eliminating the need for data operations. This distinct technology acts as an intelligent acceleration layer within your data lake, which remains the central source of truth and operates within the customer's cloud infrastructure (VPC). By empowering data teams to operationalize their entire data lake, Varada facilitates data democratization while ensuring fast, interactive performance, all without requiring data relocation, modeling, or manual optimization. The key advantage lies in Varada's capability to automatically and dynamically index pertinent data, maintaining the structure and granularity of the original source. Additionally, Varada ensures that any query can keep pace with the constantly changing performance and concurrency demands of users and analytics APIs, while also maintaining predictable cost management. The platform intelligently determines which queries to accelerate and which datasets to index, while also flexibly adjusting the cluster to match demand, thereby optimizing both performance and expenses. This holistic approach to data management not only enhances operational efficiency but also allows organizations to remain agile in an ever-evolving data landscape.

Conventional techniques for integrating mainframe data, such as ETL, data warehouses, and connector development, are increasingly inadequate in terms of speed, accuracy, and efficiency in today’s business landscape. As the amount of data generated and stored on mainframes continues to surge, these outdated methods fall further behind. Data virtualization emerges as the solution to bridge this growing divide, automating the accessibility of mainframe data for developers and applications alike. This approach allows organizations to discover and map their data just once, after which it can be easily virtualized and reused across various platforms. Ultimately, this capability enables your data to align with your business goals and aspirations. By leveraging data virtualization on z/OS, organizations can simplify the complexities associated with mainframe resources. Moreover, data virtualization facilitates the integration of data from numerous disparate sources into a cohesive logical repository, significantly enhancing the ability to connect mainframe information with distributed applications. This method also allows for the enrichment of mainframe data by incorporating insights from location, social media, and other external datasets, promoting a more comprehensive understanding of business dynamics.

The primary obstacle in expanding AI-driven decision-making lies in the underutilization of data. IBM Cloud Pak® for Data provides a cohesive platform that integrates a data fabric, enabling seamless connection and access to isolated data, whether it resides on-premises or in various cloud environments, without necessitating data relocation. It streamlines data accessibility by automatically identifying and organizing data to present actionable knowledge assets to users, while simultaneously implementing automated policy enforcement to ensure secure usage. To further enhance the speed of insights, this platform incorporates a modern cloud data warehouse that works in harmony with existing systems. It universally enforces data privacy and usage policies across all datasets, ensuring compliance is maintained. By leveraging a high-performance cloud data warehouse, organizations can obtain insights more rapidly. Additionally, the platform empowers data scientists, developers, and analysts with a comprehensive interface to construct, deploy, and manage reliable AI models across any cloud infrastructure. Moreover, enhance your analytics capabilities with Netezza, a robust data warehouse designed for high performance and efficiency. This comprehensive approach not only accelerates decision-making but also fosters innovation across various sectors.

CData Software stands out as a premier provider of solutions for data access and connectivity, focusing on creating drivers and technologies that allow for real-time access to both online and on-premise applications, databases, and Web APIs. The versatility of CData Drivers allows users to connect to data through widely recognized standards and application platforms, including ODBC, JDBC, ADO.NET, OData, SSIS, BizTalk, and Excel. With a portfolio that boasts over 130 data sources, CData caters to a wide range of applications, encompassing analytics and BI platforms, CRM systems, ERP tools, data warehousing, databases, as well as popular SaaS solutions in finance, accounting, and marketing. CData Drivers empower users to effectively link BI, Reporting, and ETL tools to a multitude of data sources, drastically reduce development time by offering pre-built solutions, and utilize SQL for data access, eliminating the need for intricate coding. Renowned for their reliability, CData Drivers are trusted by users across the globe and come with robust support options, ensuring that assistance is available whenever it's needed. Whether you're a small business or a large enterprise, CData's solutions can significantly enhance your data management experience.

Virtuoso Universal Server represents a cutting-edge platform that leverages established open standards and utilizes Hyperlinks as Super Keys to dismantle data silos that hinder both user engagement and enterprise efficiency. With Virtuoso, users can effortlessly create financial profile knowledge graphs based on near real-time financial activities, significantly lowering the costs and complexity involved in identifying fraudulent behavior patterns. Thanks to its robust, secure, and scalable database management system, it allows for intelligent reasoning and inference to unify fragmented identities through personally identifiable information such as email addresses, phone numbers, social security numbers, and driver's licenses, facilitating the development of effective fraud detection solutions. Additionally, Virtuoso empowers users to craft impactful applications powered by knowledge graphs sourced from diverse life sciences-related data sets, thereby enhancing the overall analytical capabilities in that field. This innovative approach not only streamlines the processes involved in fraud detection but also opens new avenues for data utilization across various sectors.

Reportql is an innovative data visualization tool that leverages SQL and artificial intelligence to simplify the creation of real-time reports and dashboards. By allowing users to seamlessly connect to their databases and query data in natural language, it removes the barriers of lengthy development cycles and lessens reliance on technical teams. Supporting various AI models like OpenAI, Google Gemini, and Mistral, it provides immediate data insights without the need for extensive AI model training or financial investment. Among its features are the ability to create dynamic dashboards that showcase critical metrics from multiple databases, automated email reporting based on scheduled or event-triggered actions, and alert systems that notify users about changes in key performance indicators, trends, anomalies, or metric summaries. With Reportql's user-friendly low-code interface, the pace of report generation is significantly accelerated, enabling developers to produce reports up to ten times quicker, while its AI-driven functionalities allow end-users to retrieve data in real-time without any hassle. Furthermore, this tool is particularly beneficial for organizations seeking to enhance their decision-making processes through timely and actionable data insights.

The fundamental technology that powers contemporary solutions for data integration and management is designed to swiftly link various structured and unstructured data sources. It allows for the comprehensive cataloging of your entire data environment, ensuring that data remains within its original sources and is retrieved as needed, eliminating the requirement for duplicate copies. Users can construct data models tailored to their needs, even when drawing from multiple data sources, while also concealing the intricacies of back-end systems from end users. The virtual model can be securely accessed and utilized through standard SQL alongside other formats such as REST, SOAP, and OData, promoting easy access to diverse data types. It features complete data integration and modeling capabilities, along with an Active Data Catalog that enables self-service for data and metadata exploration and preparation. Furthermore, it incorporates robust data security and governance measures, ensures rapid and intelligent execution of data queries, and provides real-time data delivery in various formats. The system also supports the establishment of data marketplaces and effectively decouples business applications from data systems, paving the way for more informed, data-driven decision-making strategies. This innovative approach enhances the overall agility and responsiveness of organizations in managing their data assets.

InDriver: The Multifunctional Automation engine powered by JavaScript allows for simultaneous task execution. InStudio: GUI application for remote InDriver Configuration across multiple computers. With minimal JS code, and a few mouse clicks, you can easily transform setups into tailored solution. Key Applications Data Automation and Integration Engine Conduct Extract-Transform-Load (ETL) operations effortlessly. Access to RESTful API Resources is streamlined, with simplified request definition, interval settings, JSON data processing and database logins. Industrial Automation Engine Interfacing seamless with PLCs and sensors. Create control algorithms, read/write data and process data to SCADA, MES and other systems. Database Automation Schedule queries to run at specific intervals or on specific events. This will ensure continuous automation.

Data engineers and scientists can be 95% better at their jobs with ready access to the most flexible semantic layer, explainable AI and reusable data modelling. They can create and expand semantic models, understand data interrelationships, and run federated query to speed up time to insight. Stardog's graph data virtualization and high performance graph database are the best available -- at a price that is up to 57x less than competitors -- to connect any data source, warehouse, or enterprise data lakehouse without copying or moving data. Scale users and use cases at a lower infrastructure cost. Stardog's intelligent inference engine applies expert knowledge dynamically at query times to uncover hidden patterns and unexpected insights in relationships that lead to better data-informed business decisions and outcomes.

If you're looking to establish a governed delta lake, create a data warehouse, or transition from a conventional database to a contemporary cloud data solution, Lyftrondata has you covered. You can effortlessly create and oversee all your data workloads within a single platform, automating the construction of your pipeline and warehouse. Instantly analyze your data using ANSI SQL and business intelligence or machine learning tools, and easily share your findings without the need for custom coding. This functionality enhances the efficiency of your data teams and accelerates the realization of value. You can define, categorize, and locate all data sets in one centralized location, enabling seamless sharing with peers without the complexity of coding, thus fostering insightful data-driven decisions. This capability is particularly advantageous for organizations wishing to store their data once, share it with various experts, and leverage it repeatedly for both current and future needs. In addition, you can define datasets, execute SQL transformations, or migrate your existing SQL data processing workflows to any cloud data warehouse of your choice, ensuring flexibility and scalability in your data management strategy.

StackQL serves as a SQL-oriented framework designed for managing multicloud environments, enabling the provisioning and querying of cloud resources while streamlining security and minimizing complexity. Its unique architecture allows for both "no code" and "low code" provider plugins, which makes it adaptable to any API-enabled cloud and SaaS provider, thereby enhancing its utility as a user-friendly solution for multicloud management and observability. This flexibility not only empowers users to efficiently handle diverse cloud resources but also elevates the overall management experience.

IBM Db2 Big SQL is a sophisticated hybrid SQL-on-Hadoop engine that facilitates secure and advanced data querying across a range of enterprise big data sources, such as Hadoop, object storage, and data warehouses. This enterprise-grade engine adheres to ANSI standards and provides massively parallel processing (MPP) capabilities, enhancing the efficiency of data queries. With Db2 Big SQL, users can execute a single database connection or query that spans diverse sources, including Hadoop HDFS, WebHDFS, relational databases, NoSQL databases, and object storage solutions. It offers numerous advantages, including low latency, high performance, robust data security, compatibility with SQL standards, and powerful federation features, enabling both ad hoc and complex queries. Currently, Db2 Big SQL is offered in two distinct variations: one that integrates seamlessly with Cloudera Data Platform and another as a cloud-native service on the IBM Cloud Pak® for Data platform. This versatility allows organizations to access and analyze data effectively, performing queries on both batch and real-time data across various sources, thus streamlining their data operations and decision-making processes. In essence, Db2 Big SQL provides a comprehensive solution for managing and querying extensive datasets in an increasingly complex data landscape.

Dot serves as an AI-driven data analyst, seamlessly linking to your data warehouse and empowering users to pose questions in natural language for immediate, reliable insights. It functions across platforms like Slack, Teams, or through a dedicated web application to facilitate on-demand data retrieval, visualizations, root-cause analyses, and weekly business summaries that come with actionable suggestions. By leveraging existing business intelligence tools, dbt metrics, LookML, SQL queries, and documentation, GetDot.ai guarantees consistent and governed responses supported by role-specific permissions and row-level security measures. The setup process is entirely code-free, featuring one-click integrations for popular SQL-based sources including Snowflake, BigQuery, Redshift, and PostgreSQL. Its continuous monitoring feature reveals previously unknown insights, while a specialized training and governance workspace allows you to fine-tune its functionality and uphold accuracy. Built for efficiency and ease of use, Dot eliminates the clutter of multiple dashboards by providing exact answers in mere seconds, transforming the way data is accessed and utilized. Moreover, this innovative tool not only enhances productivity but also empowers users to make data-driven decisions with confidence.

We make BI and Big Data analytics easier and more efficient. Our goal is to empower business users, make BI specialists and always-busy business more independent when solving data-driven business problems. Querona is a solution for those who have ever been frustrated by a lack in data, slow or tedious report generation, or a long queue to their BI specialist. Querona has a built-in Big Data engine that can handle increasing data volumes. Repeatable queries can be stored and calculated in advance. Querona automatically suggests improvements to queries, making optimization easier. Querona empowers data scientists and business analysts by giving them self-service. They can quickly create and prototype data models, add data sources, optimize queries, and dig into raw data. It is possible to use less IT. Users can now access live data regardless of where it is stored. Querona can cache data if databases are too busy to query live.

Apache Hive is a data warehouse solution that enables the efficient reading, writing, and management of substantial datasets stored across distributed systems using SQL. It allows users to apply structure to pre-existing data in storage. To facilitate user access, it comes equipped with a command line interface and a JDBC driver. As an open-source initiative, Apache Hive is maintained by dedicated volunteers at the Apache Software Foundation. Initially part of the Apache® Hadoop® ecosystem, it has since evolved into an independent top-level project. We invite you to explore the project further and share your knowledge to enhance its development. Users typically implement traditional SQL queries through the MapReduce Java API, which can complicate the execution of SQL applications on distributed data. However, Hive simplifies this process by offering a SQL abstraction that allows for the integration of SQL-like queries, known as HiveQL, into the underlying Java framework, eliminating the need to delve into the complexities of the low-level Java API. This makes working with large datasets more accessible and efficient for developers.

Quickly access data from all sources with a single search, including non-security data sources and unstructured data in cloud storage. Control where and how to store data, reducing storage costs and eliminating expensive data churn projects. Supercharge your security investigations with a single view of normalized and enriched search results from across your data sources.

Establish connectivity that adheres to recognized standards (ODBC, JDBC, ADO.NET) for the foundational data, ensuring compatibility with widely-used analytical and data integration platforms. Sensitive personal information can be concealed or made accessible strictly on a need-to-know basis. The OpenAccess SDK serves as a valuable resource for developers, facilitating the creation of a standards-compliant driver with SQL capabilities, covering approximately 99% of the necessary coding. This SDK enables the rapid development of an ODBC, JDBC, or ADO.NET driver that aligns with your specific needs while eliminating the need for extensive domain knowledge or rigorous coding efforts. You can swiftly generate a tailored driver using our ready-made add-ons or work directly with the SDK interfaces. It provides the quickest method to ensure your application integrates seamlessly with data-centric solutions, including BI and analytics tools, ETL processes, and beyond. Furthermore, it allows for the establishment of a unified interface for accessing data across all standards-compliant APIs, thereby enhancing overall efficiency and usability.

Our goal is to deliver software and services that empower safety operations and management teams to effectively recognize and oversee essential information about drivers, vehicles, and equipment. The CFDS cloud-based platform enables the tracking of time-sensitive items as outlined by the Federal Motor Carrier Safety Association. At CFDS, our web application is tailored to assist Motor Carriers in navigating and comprehending the FMCSA's CSA program. We offer cloud-based solutions for intermodal, TL, and local P&D operations, along with dispatching software. Moreover, our cloud-based driver application is integrated with features for qualifications assessment and tracking. It allows for the monitoring of driver reviews, vehicle status, and equipment expiration dates by keeping due dates updated for all related items. If you're looking to qualify a potential driver or owner-operator, CFDS provides advanced screening services that deliver swift and reliable results for all candidates. In doing so, we ensure that safety and compliance remain at the forefront of transportation operations.

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.

Enterprise Enabler brings together disparate information from various sources and isolated data sets, providing a cohesive view within a unified platform; this includes data housed in the cloud, distributed across isolated databases, stored on instruments, located in Big Data repositories, or found within different spreadsheets and documents. By seamlessly integrating all your data, it empowers you to make timely and well-informed business choices. The system creates logical representations of data sourced from its original locations, enabling you to effectively reuse, configure, test, deploy, and monitor everything within a single cohesive environment. This allows for the analysis of your business data as events unfold, helping to optimize asset utilization, reduce costs, and enhance your business processes. Remarkably, our deployment timeline is typically 50-90% quicker, ensuring that your data sources are connected and operational in record time, allowing for real-time decision-making based on the most current information available. With this solution, organizations can enhance collaboration and efficiency, leading to improved overall performance and strategic advantage in the market.

Red Hat JBoss Data Virtualization serves as an efficient solution for virtual data integration, effectively releasing data that is otherwise inaccessible and presenting it in a unified, user-friendly format that can be easily acted upon. It allows data from various, physically distinct sources, such as different databases, XML files, and Hadoop systems, to be viewed as a cohesive set of tables within a local database. This solution provides real-time, standards-based read and write access to a variety of heterogeneous data repositories. By streamlining the process of accessing distributed data, it accelerates both application development and integration. Users can integrate and adapt data semantics to meet the specific requirements of data consumers. Additionally, it offers central management for access control and robust auditing processes through a comprehensive security framework. As a result, fragmented data can be transformed into valuable insights swiftly, catering to the dynamic needs of businesses. Moreover, Red Hat provides ongoing support and maintenance for its JBoss products during specified periods, ensuring that users have access to the latest enhancements and assistance.

TSQL.APP, a web-based SQL Platform for building data-driven apps directly within SQL Server, is a platform that allows you to build applications using SQL. It features a built-in IDE that allows developers to create responsive web applications using SQL for backend code and UI interaction. Key features include: Dynamic UI Components : Cards represent database records on list and form views. Modals for inputs, alerts and tables can be customized. Action Scripting : SQL-based scripts define the app behavior and enable CRUD operations as well as complex workflows. Real-Time Interactivity - Reports auto-refresh and audio/visual notifications provide instant user feedback. Security and Accessibility: Supports secure log-ins, cloud access and file management via meta tables. Extensibility: Integrates.NET CLR to perform advanced operations. TSQL.APP allows you to build adaptable business applications with responsive data handling and workflows. It also allows for custom user interaction.

VeloDB, which utilizes Apache Doris, represents a cutting-edge data warehouse designed for rapid analytics on large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion that occurs in mere seconds, alongside a storage engine capable of real-time upserts, appends, and pre-aggregations. The platform delivers exceptional performance for real-time data serving and allows for dynamic interactive ad-hoc queries. VeloDB accommodates not only structured data but also semi-structured formats, supporting both real-time analytics and batch processing capabilities. Moreover, it functions as a federated query engine, enabling seamless access to external data lakes and databases in addition to internal data. The system is designed for distribution, ensuring linear scalability. Users can deploy it on-premises or as a cloud service, allowing for adaptable resource allocation based on workload demands, whether through separation or integration of storage and compute resources. Leveraging the strengths of open-source Apache Doris, VeloDB supports the MySQL protocol and various functions, allowing for straightforward integration with a wide range of data tools, ensuring flexibility and compatibility across different environments.

Many commonly used dashboards, reporting, and ETL tools often face challenges with connectivity to specific data sources, which can create significant integration hurdles for users. To address this issue, Simba provides ready-to-use, standards-compliant drivers that facilitate seamless connectivity, streamlining the integration process. Organizations that supply data to their customers frequently find it difficult to ensure smooth and straightforward data access for their users. By leveraging Simba’s SDK, developers are empowered to create custom, standards-based drivers, which makes access to data more user-friendly compared to traditional methods like CSV exports or API integrations. Moreover, unique backend requirements, such as those dictated by particular applications or internal workflows, can further complicate connectivity efforts. With the aid of Simba’s SDK or managed services, it becomes possible to develop drivers that are specifically tailored to address these unique needs. Furthermore, Simba offers extensive ODBC/JDBC compatibility that supports a diverse array of applications and data tools, enhancing their functionality. By incorporating Simba Drivers into these platforms, organizations can significantly improve their connectivity capabilities, opening the door to a broader range of data sources for their users. This ultimately leads to a more integrated and efficient data environment for businesses.

FleetDrive 360 offers a comprehensive, cloud-based solution to manage your FMCSA and DOT compliance. Hire and onboard drivers faster than ever before. Using your laptop or phone, you can access and maintain your driver qualification files, drug and alcohol consortium, Clearinghouse queries, vehicle maintenance documents, accident records, and more.

Enhance the performance of your system by updating drivers effortlessly with just a single click, ensuring a more stable and efficient operation. After reinstalling Windows, you can restore all drivers conveniently with another click. For added peace of mind, the software creates a system restore point alongside a driver rollback feature, allowing you to revert to a previous driver version whenever necessary. Additionally, it helps you eliminate obsolete driver packages to reclaim valuable disk space. You can also access your computer's hardware information and monitor its operational status in real-time, including temperature and voltage levels. In the event of hardware replacement, the previous hardware data will be preserved within the system. If you switch your operating system, the tool ensures that your network is quickly restored. Furthermore, the offline driver update utility allows for rapid driver installations and network recovery, even if your computer faces connectivity issues. This comprehensive approach not only simplifies driver management but also enhances overall system reliability and performance.

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.

Rapidly establish cloud environments tailored for spontaneous development, testing, and enhanced productivity for IT and business personnel. Mitigate the risks and expenses associated with managing your data lake by adopting robust data governance practices that include comprehensive end-to-end data lineage for business users. Achieve greater cost efficiency by providing clean, reliable, and timely data for your data lakes, data warehouses, or big data initiatives, while also consolidating applications and phasing out legacy databases. Benefit from automatic schema propagation to accelerate job creation, implement type-ahead search features, and maintain backward compatibility, all while following a design that allows for execution across varied platforms. Develop data integration workflows and enforce governance and quality standards through an intuitive design that identifies and recommends usage trends, thus enhancing user experience. Furthermore, boost visibility and information governance by facilitating complete and authoritative insights into data, backed by proof of lineage and quality, ensuring that stakeholders can make informed decisions based on accurate information. With these strategies in place, organizations can foster a more agile and data-driven culture.

Utilize Ignite as a conventional SQL database by employing JDBC drivers, ODBC drivers, or the dedicated SQL APIs that cater to Java, C#, C++, Python, and various other programming languages. Effortlessly perform operations such as joining, grouping, aggregating, and ordering your distributed data, whether it is stored in memory or on disk. By integrating Ignite as an in-memory cache or data grid across multiple external databases, you can enhance the performance of your existing applications by a factor of 100. Envision a cache that allows for SQL querying, transactional operations, and computational tasks. Develop contemporary applications capable of handling both transactional and analytical workloads by leveraging Ignite as a scalable database that exceeds the limits of available memory. Ignite smartly allocates memory for frequently accessed data and resorts to disk storage when dealing with less frequently accessed records. This allows for the execution of kilobyte-sized custom code across vast petabytes of data. Transform your Ignite database into a distributed supercomputer, optimized for rapid calculations, intricate analytics, and machine learning tasks, ensuring that your applications remain responsive and efficient even under heavy loads. Embrace the potential of Ignite to revolutionize your data processing capabilities and drive innovation within your projects.

XTDB is a unique SQL database that offers immutability to enhance application development and guarantee data compliance. Its automatic data history preservation feature allows for extensive time-travel queries, enabling users to execute as-of queries and audits seamlessly with SQL commands. This innovative tool is relied upon by a multitude of organizations to revolutionize their dynamic and temporal applications. Getting started with XTDB is straightforward, whether through HTTP, plain SQL, or various programming languages, as it only necessitates a client driver or Curl. Users can easily insert data in an immutable way, conduct time-based queries, and perform intricate joins. Furthermore, risk systems see direct advantages from its bitemporal modeling capabilities. By employing valid time, the database facilitates the correlation of out-of-sync trade data, simplifying compliance procedures. One of the significant challenges organizations face is the continuous change in data exposure; however, XTDB addresses this issue effectively by streamlining data exchange and enabling advanced temporal analysis. Additionally, modeling future changes in pricing, taxes, and discounts necessitates robust temporal query capabilities, which XTDB provides efficiently. Ultimately, XTDB stands out as a powerful solution for managing complex temporal data needs.

MindsDB is the only AGI data solution that connects and unifies petabyte scale enterprise data, enabling enterprise-wide informed decision-making in real-time.

Fernhill SCADA is a scalable SCADA offer based on a client server architecture. Easy to use and setup. Drivers for all commonly used PLCs and open protocols BACnet, DNP3, Modbus. Includes open data access interfaces OPC UA, OPC Classic, ODBC, MQTT. Historian and trending are built-in. Operators can use Fernhill SCADA on multiple platforms including Windows, Linux, macOS, Android and iOS. Free runtime - deploy any number of SCADA systems with one low cost developer license.

UniverseBridge enables the smooth integration of your data from SAP BusinessObjects into various data visualization applications, empowering you to utilize the corporate business logic that has already been established within your SAP BusinessObjects Universes. By building on what you’ve previously created in BusinessObjects, you can generate interactive visualizations and applications with ease. This allows for the effortless utilization of your existing corporate business logic while linking data from legacy Universes and other sources without the need for redevelopment. You can harness the capabilities of your current BusinessObjects Universes without the hassle of rewriting SQL Queries or incurring costs for new solutions. Front-end design can be performed using data visualization tools while allowing your business users to engage with their preferred tools. Additionally, you can federate data across legacy Universes without the need to transition to Multi Source Universes. Our assistance spans the entire data and analytics lifecycle, from strategic planning to execution, enabling you to derive insights from your data and address intricate business challenges effectively. We are dedicated to helping you maximize the potential of your data assets for informed decision-making.

Apache Doris serves as a cutting-edge data warehouse tailored for real-time analytics, enabling exceptionally rapid analysis of data at scale. It features both push-based micro-batch and pull-based streaming data ingestion that occurs within a second, alongside a storage engine capable of real-time upserts, appends, and pre-aggregation. With its columnar storage architecture, MPP design, cost-based query optimization, and vectorized execution engine, it is optimized for handling high-concurrency and high-throughput queries efficiently. Moreover, it allows for federated querying across various data lakes, including Hive, Iceberg, and Hudi, as well as relational databases such as MySQL and PostgreSQL. Doris supports complex data types like Array, Map, and JSON, and includes a Variant data type that facilitates automatic inference for JSON structures, along with advanced text search capabilities through NGram bloomfilters and inverted indexes. Its distributed architecture ensures linear scalability and incorporates workload isolation and tiered storage to enhance resource management. Additionally, it accommodates both shared-nothing clusters and the separation of storage from compute resources, providing flexibility in deployment and management.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.