Alternatives to QuestDB

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

Warp 10 is a modular open source platform that collects, stores, and allows you to analyze time series and sensor data. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 offers both a time series database and a powerful analysis environment, which can be used together or independently. It will allow you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The Platform is GDPR compliant and secure by design using cryptographic tokens to manage authentication and authorization. The Analytics Engine can be implemented within a large number of existing tools and ecosystems such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. From small devices to distributed clusters, Warp 10 fits your needs at any scale, and can be used in many verticals: industry, transportation, health, monitoring, finance, energy, etc.

ClickHouse is an efficient, open-source OLAP database management system designed for high-speed data processing. Its column-oriented architecture facilitates the creation of analytical reports through real-time SQL queries. In terms of performance, ClickHouse outshines similar column-oriented database systems currently on the market. It has the capability to handle hundreds of millions to over a billion rows, as well as tens of gigabytes of data, on a single server per second. By maximizing the use of available hardware, ClickHouse ensures rapid query execution. The peak processing capacity for individual queries can exceed 2 terabytes per second, considering only the utilized columns after decompression. In a distributed environment, read operations are automatically optimized across available replicas to minimize latency. Additionally, ClickHouse features multi-master asynchronous replication, enabling deployment across various data centers. Each node operates equally, effectively eliminating potential single points of failure and enhancing overall reliability. This robust architecture allows organizations to maintain high availability and performance even under heavy workloads.

Hydra is an innovative, open-source solution that transforms Postgres into a column-oriented database, enabling instant queries over billions of rows without necessitating any alterations to your existing code. By employing advanced techniques such as parallelization and vectorization for aggregate functions like COUNT, SUM, and AVG, Hydra significantly enhances the speed and efficiency of data processing in Postgres. In just five minutes, you can set up Hydra without modifying your syntax, tools, data model, or extensions, ensuring a hassle-free integration. For those seeking a fully managed experience, Hydra Cloud offers seamless operations and optimal performance. Various industries can benefit from tailored analytics by leveraging powerful Postgres extensions and custom functions, allowing you to take charge of your data needs. Designed with user requirements in mind, Hydra stands out as the fastest Postgres solution available for analytical tasks, making it an essential tool for data-driven decision-making. With features like columnar storage, query parallelization, and vectorization, Hydra is poised to redefine the analytics landscape.

Tiger Data reimagines PostgreSQL for the modern era — powering everything from IoT and fintech to AI and Web3. As the creator of TimescaleDB, it brings native time-series, event, and analytical capabilities to the world’s most trusted database engine. Through Tiger Cloud, developers gain access to a fully managed, elastic infrastructure with auto-scaling, high availability, and point-in-time recovery. The platform introduces core innovations like Forks (copy-on-write storage branches for CI/CD and testing), Memory (durable agent context and recall), and Search (hybrid BM25 and vector retrieval). Combined with hypertables, continuous aggregates, and materialized views, Tiger delivers the speed of specialized analytical systems without sacrificing SQL simplicity. Teams use Tiger Data to unify real-time and historical analytics, build AI-driven workflows, and streamline data management at scale. It integrates seamlessly with the entire PostgreSQL ecosystem, supporting APIs, CLIs, and modern development frameworks. With over 20,000 GitHub stars and a thriving developer community, Tiger Data stands as the evolution of PostgreSQL for the intelligent data age.

TimescaleDB brings the power of PostgreSQL to time-series and event data at any scale. It extends standard Postgres with features like automatic time-based partitioning (hypertables), incremental materialized views, and native time-series functions, making it the most efficient way to handle analytical workloads. Designed for use cases like IoT, DevOps monitoring, crypto markets, and real-time analytics, it ingests millions of rows per second while maintaining sub-second query speeds. Developers can run complex time-based queries, joins, and aggregations using familiar SQL syntax — no new language or database model required. Built-in compression ensures long-term data retention without high storage costs, and automated data management handles rollups and retention policies effortlessly. Its hybrid storage architecture merges row-based performance for live data with columnar efficiency for historical queries. Open-source and 100% PostgreSQL compatible, TimescaleDB integrates with Kafka, S3, and the entire Postgres ecosystem. Trusted by global enterprises, it delivers the performance of a purpose-built time-series system without sacrificing Postgres reliability or flexibility.

ParadeDB enhances Postgres tables by introducing column-oriented storage alongside vectorized query execution capabilities. At the time of table creation, users can opt for either row-oriented or column-oriented storage. The data in column-oriented tables is stored as Parquet files and is efficiently managed through Delta Lake. It features keyword search powered by BM25 scoring, adjustable tokenizers, and support for multiple languages. Additionally, it allows semantic searches that utilize both sparse and dense vectors, enabling users to achieve improved result accuracy by merging full-text and similarity search techniques. Furthermore, ParadeDB adheres to ACID principles, ensuring robust concurrency controls for all transactions. It also seamlessly integrates with the broader Postgres ecosystem, including various clients, extensions, and libraries, making it a versatile option for developers. Overall, ParadeDB provides a powerful solution for those seeking optimized data handling and retrieval in Postgres.

QuasarDB, the core of Quasar's intelligence, is an advanced, distributed, column-oriented database management system specifically engineered for high-performance timeseries data handling, enabling real-time processing for massive petascale applications. It boasts up to 20 times less disk space requirement, making it exceptionally efficient. The unmatched ingestion and compression features of QuasarDB allow for up to 10,000 times quicker feature extraction. This database can perform real-time feature extraction directly from raw data via an integrated map/reduce query engine, a sophisticated aggregation engine that utilizes SIMD capabilities of contemporary CPUs, and stochastic indexes that consume minimal disk storage. Its ultra-efficient resource utilization, ability to integrate with object storage solutions like S3, innovative compression methods, and reasonable pricing structure make it the most economical timeseries solution available. Furthermore, QuasarDB is versatile enough to operate seamlessly across various platforms, from 32-bit ARM devices to high-performance Intel servers, accommodating both Edge Computing environments and traditional cloud or on-premises deployments. Its scalability and efficiency make it an ideal choice for businesses aiming to harness the full potential of their data in real-time.

Enhance your metrics and alerting capabilities using a top-tier open-source monitoring tool. Prometheus inherently organizes all data as time series, which consist of sequences of timestamped values associated with the same metric and a specific set of labeled dimensions. In addition to the stored time series, Prometheus has the capability to create temporary derived time series based on query outcomes. The tool features a powerful query language known as PromQL (Prometheus Query Language), allowing users to select and aggregate time series data in real time. The output from an expression can be displayed as a graph, viewed in tabular format through Prometheus’s expression browser, or accessed by external systems through the HTTP API. Configuration of Prometheus is achieved through a combination of command-line flags and a configuration file, where the flags are used to set immutable system parameters like storage locations and retention limits for both disk and memory. This dual method of configuration ensures a flexible and tailored monitoring setup that can adapt to various user needs. For those interested in exploring this robust tool, further details can be found at: https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fsourceforge.net%2Fprojects%2Fprometheus.mirror%2F

Riak®, TS is an enterprise-grade NoSQL Time Series Database that is specifically designed for IoT data and Time Series data. It can ingest, transform, store, and analyze massive amounts of time series information. Riak TS is designed to be faster than Cassandra. Riak TS masterless architecture can read and write data regardless of network partitions or hardware failures. Data is evenly distributed throughout the Riak ring. By default, there are three copies of your data. This ensures that at least one copy is available for reading operations. Riak TS is a distributed software system that does not have a central coordinator. It is simple to set up and use. It is easy to add or remove nodes from a cluster thanks to the masterless architecture. Riak TS's masterless architecture makes it easy for you to add or remove nodes from your cluster. Adding nodes made of commodity hardware to your cluster can help you achieve predictable and almost linear scale.

KairosDB allows data ingestion through various protocols including Telnet, Rest, and Graphite, in addition to supporting plugins for further flexibility. It utilizes Cassandra, a well-regarded NoSQL database, to manage time series data effectively. The database schema is organized into three column families, facilitating efficient data storage. The API offers a range of functionalities, such as listing existing metric names, retrieving tag names and their corresponding values, storing metric data points, and querying these points for analysis. Upon a standard installation, users can access a query page that enables them to extract data from the database easily. This tool is primarily tailored for development applications. Aggregators within the system can perform operations on data points, allowing for down sampling and analysis. A set of standard functions, including min, max, sum, count, and mean, among others, are readily available for users to utilize. Additionally, the KairosDB server supports import and export functionalities via the command line interface. Internal metrics related to the database not only provide insights into the stored data but also allow for monitoring the performance of the server itself, ensuring optimal operation and efficiency. This comprehensive approach makes KairosDB a powerful solution for managing time series data.

Infobright DB is an enterprise-grade database that utilizes a columnar storage architecture, enabling business analysts to efficiently analyze data and rapidly generate reports. This versatile database can be implemented both on-premise and in cloud environments. It is designed to store and analyze substantial amounts of big data, facilitating interactive business intelligence and handling complex queries with ease. By enhancing query performance and lowering storage costs, it significantly boosts overall efficiency in analytics and reporting processes. With capabilities to manage hundreds of terabytes of data, Infobright DB overcomes the limitations often faced by traditional databases. This solution supports big data applications while removing the need for indexing and partitioning, resulting in no administrative burden. In an era where machine data is growing exponentially, IgniteTech’s Infobright DB is purpose-built to deliver exceptional performance for large quantities of machine-generated information. Furthermore, it allows users to manage intricate ad hoc analytical environments without the heavy database administration demands seen in other solutions. This makes it an invaluable tool for organizations seeking to optimize their data handling and analysis.

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.

At the core of our innovative approach lies in-data computing, a cutting-edge technology aimed at efficiently processing substantial volumes of data. Our leading product, BigObject, is a prime example of this technology; it is a time series database purposefully created to enable rapid storage and management of vast data sets. Leveraging in-data computing, BigObject has the capability to swiftly and continuously address diverse data streams without interruption. This time series database excels in both high-speed storage and data analysis, showcasing remarkable performance alongside robust complex query functionalities. By transitioning from a traditional relational data structure to a time-series model, it harnesses in-data computing to enhance overall database efficiency. The foundation of our technology is an abstract model, wherein all data resides within an infinite and persistent memory space, facilitating seamless storage and computation. This unique architecture not only optimizes performance but also paves the way for future advancements in data processing capabilities.

JaguarDB facilitates the rapid ingestion of time series data while integrating location-based information. It possesses the capability to index data across both spatial and temporal dimensions effectively. Additionally, the system allows for swift back-filling of time series data, enabling the insertion of significant volumes of historical data points. Typically, time series refers to a collection of data points that are arranged in chronological order. However, in JaguarDB, time series encompasses both a sequence of data points and multiple tick tables that hold aggregated data values across designated time intervals. For instance, a time series table in JaguarDB may consist of a primary table that organizes data points in time sequence, along with tick tables that represent various time frames such as 5 minutes, 15 minutes, hourly, daily, weekly, and monthly, which store aggregated data for those intervals. The structure for RETENTION mirrors that of the TICK format but allows for a flexible number of retention periods, defining the duration for which data points in the base table are maintained. This approach ensures that users can efficiently manage and analyze historical data according to their specific needs.

Introducing a robust cross-platform columnar database designed for high-performance historical time-series data, which includes: - A compute engine optimized for in-memory operations - A streaming processor that functions in real time - A powerful query and programming language known as q Kdb+ drives the kdb Insights portfolio and KDB.AI, offering advanced time-focused data analysis and generative AI functionalities to many of the world's top enterprises. Recognized for its unparalleled speed, kdb+ has been independently benchmarked* as the leading in-memory columnar analytics database, providing exceptional benefits for organizations confronting complex data challenges. This innovative solution significantly enhances decision-making capabilities, enabling businesses to adeptly respond to the ever-evolving data landscape. By leveraging kdb+, companies can gain deeper insights that lead to more informed strategies.

Amazon Timestream is an efficient, scalable, and serverless time series database designed for IoT and operational applications, capable of storing and analyzing trillions of events daily with speeds up to 1,000 times faster and costs as low as 1/10th that of traditional relational databases. By efficiently managing the lifecycle of time series data, Amazon Timestream reduces both time and expenses by keeping current data in memory while systematically transferring historical data to a more cost-effective storage tier based on user-defined policies. Its specialized query engine allows users to seamlessly access and analyze both recent and historical data without the need to specify whether the data is in memory or in the cost-optimized tier. Additionally, Amazon Timestream features integrated time series analytics functions, enabling users to detect trends and patterns in their data almost in real-time, making it an invaluable tool for data-driven decision-making. Furthermore, this service is designed to scale effortlessly with your data needs while ensuring optimal performance and cost efficiency.

Azure Time Series Insights Gen2 is a robust and scalable IoT analytics service that provides an exceptional user experience along with comprehensive APIs for seamless integration into your current workflow or application. This platform enables the collection, processing, storage, querying, and visualization of data at an Internet of Things (IoT) scale, ensuring that the data is highly contextualized and specifically tailored for time series analysis. With a focus on ad hoc data exploration and operational analysis, it empowers users to identify hidden trends, detect anomalies, and perform root-cause investigations. Furthermore, Azure Time Series Insights Gen2 stands out as an open and adaptable solution that caters to the diverse needs of industrial IoT deployments, making it an invaluable tool for organizations looking to harness the power of their data. By leveraging its capabilities, businesses can gain deeper insights into their operations and make informed decisions to drive efficiency and innovation.

OpenTSDB comprises a Time Series Daemon (TSD) along with a suite of command line tools. Users primarily engage with OpenTSDB by operating one or more independent TSDs, as there is no centralized master or shared state, allowing for the scalability to run multiple TSDs as necessary to meet varying loads. Each TSD utilizes HBase, an open-source database, or the hosted Google Bigtable service for the storage and retrieval of time-series data. The schema designed for the data is highly efficient, enabling rapid aggregations of similar time series while minimizing storage requirements. Users interact with the TSD without needing direct access to the underlying storage system. Communication with the TSD can be accomplished through a straightforward telnet-style protocol, an HTTP API, or a user-friendly built-in graphical interface. To begin utilizing OpenTSDB, the initial task is to send time series data to the TSDs, and there are various tools available to facilitate the import of data from different sources into OpenTSDB. Overall, OpenTSDB's design emphasizes flexibility and efficiency for time series data management.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

CockroachDB: Cloud-native distributed SQL. Your cloud applications deserve a cloud-native database. Cloud-based apps and services need a database that can scale across clouds, reduces operational complexity, and improves reliability. CockroachDB provides resilient, distributed SQL with ACID transactions. Data partitioned by geography is also available. Combining CockroachDB and orchestration tools such as Mesosphere DC/OS and Kubernetes to automate mission-critical applications can speed up operations.

InfiniDB is a column-oriented database management system specifically designed for online analytical processing (OLAP) workloads, featuring a distributed architecture that facilitates Massive Parallel Processing (MPP). Its integration with MySQL allows users who are accustomed to MySQL to transition smoothly to InfiniDB, as they can connect using any MySQL-compatible connector. To manage concurrency, InfiniDB employs Multi-Version Concurrency Control (MVCC) and utilizes a System Change Number (SCN) to represent the system's versioning. In the Block Resolution Manager (BRM), it effectively organizes three key structures: the version buffer, the version substitution structure, and the version buffer block manager, which all work together to handle multiple data versions. Additionally, InfiniDB implements deadlock detection mechanisms to address conflicts that arise during data transactions. Notably, it supports all MySQL syntax, including features like foreign keys, making it versatile for users. Moreover, it employs range partitioning for each column, maintaining the minimum and maximum values of each partition in a compact structure known as the extent map, ensuring efficient data retrieval and organization. This unique approach to data management enhances both performance and scalability for complex analytical queries.

Codegym offers a comprehensive Java programming curriculum that is divided into four main quests: Java Syntax, Java Core, Java Collections, and Multithreading. Within these quests, you will find over 500 mini-lessons and more than 1,000 exercises designed to enhance your learning experience. The complexity of the tasks is structured to incrementally challenge you, similar to how gradual weight increases at the gym lead to significant strength gains over time. By the conclusion of the course, participants can expect to accumulate between 500 and 1,000 hours of hands-on programming experience. This program covers all key aspects of Java, such as Java syntax, standard types, arrays, lists, collections, generics, exceptions, and various methods for managing threads, files, networks, and internet functionalities. Additionally, you will delve into object-oriented programming (OOP), serialization, recursion, annotations, and popular design patterns among other crucial concepts. The course is designed with flexibility in mind, allowing you to revisit your lessons whenever necessary, as your progress is meticulously tracked throughout your journey. Should you encounter a challenging task and need to step away, you can do so without any pressure.

SiriDB is optimized for speed. Inserts and queries are answered quickly. You can speed up your development with the custom query language. SiriDB is flexible and can be scaled on the fly. There is no downtime when you update or expand your database. You can scale your database without losing speed. As we distribute your time series data across all pools, we make full use of all resources. SiriDB was designed to deliver unmatched performance with minimal downtime. A SiriDB cluster distributes time series across multiple pools. Each pool has active replicas that can be used for load balancing or redundancy. The database can still be accessed even if one of the replicas is unavailable.

KX Streaming Analytics offers a comprehensive solution for ingesting, storing, processing, and analyzing both historical and time series data, ensuring that analytics, insights, and visualizations are readily accessible. To facilitate rapid productivity for your applications and users, the platform encompasses the complete range of data services, which includes query processing, tiering, migration, archiving, data protection, and scalability. Our sophisticated analytics and visualization tools, which are extensively utilized in sectors such as finance and industry, empower you to define and execute queries, calculations, aggregations, as well as machine learning and artificial intelligence on any type of streaming and historical data. This platform can be deployed across various hardware environments, with the capability to source data from real-time business events and high-volume inputs such as sensors, clickstreams, radio-frequency identification, GPS systems, social media platforms, and mobile devices. Moreover, the versatility of KX Streaming Analytics ensures that organizations can adapt to evolving data needs and leverage real-time insights for informed decision-making.

VictoriaMetrics is a cost-effective, scalable monitoring solution that can also be used as a time series database. It can also be used to store Prometheus' long-term data. VictoriaMetrics is a single executable that does not have any external dependencies. All configuration is done using explicit command-line flags and reasonable defaults. It provides global query view. Multiple Prometheus instances, or other data sources, may insert data into VictoriaMetrics. Later this data may be queried via a single query. It can handle high cardinality and high churn rates issues by using a series limiter.

Blueflood is an advanced distributed metric processing system designed for high throughput and low latency, operating as a multi-tenant solution that supports Rackspace Metrics. It is actively utilized by both the Rackspace Monitoring team and the Rackspace public cloud team to effectively manage and store metrics produced by their infrastructure. Beyond its application within Rackspace, Blueflood also sees extensive use in large-scale deployments documented in community resources. The data collected through Blueflood is versatile, allowing users to create dashboards, generate reports, visualize data through graphs, or engage in any activities that involve analyzing time-series data. With a primary emphasis on near-real-time processing, data can be queried just milliseconds after it is ingested, ensuring timely access to information. Users send their metrics to the ingestion service and retrieve them from the Query service, while the system efficiently handles background rollups through offline batch processing, thus facilitating quick responses for queries covering extended time frames. This architecture not only enhances performance but also ensures that users can rely on rapid access to their critical metrics for effective decision-making.

Circonus IRONdb simplifies the management and storage of limitless telemetry data, effortlessly processing billions of metric streams. It empowers users to recognize both opportunities and challenges in real time, offering unmatched forensic, predictive, and automated analytics capabilities. With the help of machine learning, it automatically establishes a "new normal" as your operations and data evolve. Additionally, Circonus IRONdb seamlessly integrates with Grafana, which natively supports our analytics query language, and is also compatible with other visualization tools like Graphite-web. To ensure data security, Circonus IRONdb maintains multiple copies across a cluster of IRONdb nodes. While system administrators usually oversee clustering, they often dedicate considerable time to its upkeep and functionality. However, with Circonus IRONdb, operators can easily configure their clusters to run autonomously, allowing them to focus on more strategic tasks rather than the tedious management of their time series data storage. This streamlined approach not only enhances efficiency but also maximizes resource utilization.

FileQuest® offers a fast, web-based solution for data discovery, catering to the needs of litigation, investigations, regulatory requirements, and privacy safeguards. With FileQuest® Digital Mountain, users gain access to on-demand web solutions that streamline and enhance the entire electronic discovery process. This innovative web solution equips law firms, corporations, and government entities with the tools to independently manage data extraction, filtering, processing, reviewing, and producing documents with ease. By utilizing our comprehensive, fully integrated platform, FileQuest® significantly minimizes the duration of data discovery while removing the burden of maintaining software or hardware on your end. Essentially, FileQuest® delivers swift data discovery as a service, allowing for flexibility without any substantial initial investment. Additionally, FileQuest® ensures continual data importation as discoveries happen and maintains a thorough audit trail of all electronic evidence. This level of control and efficiency makes FileQuest® an invaluable asset for organizations navigating complex data landscapes.

Effortlessly create both one-time and recurring work orders! Tailored quality assurance inspections are conducted according to your client's precise requirements through our Mobile App. Our cloud-based platform enables you to manage and access your operations from anywhere across the globe. Use the eQuest Mobile Work Order App to oversee your work orders and carry out inspections right on-site. eQuest is compatible with iPhones, Android devices, and tablets. Explore the various features and advantages of eQuest Work Order Software. Embark on a guided tour to see how eQuest's software and Mobile App can enhance your profit margins and reduce costs. Labor expenses represent the highest overhead for any service-oriented business, and with eQuest, you can monitor the exact start and end times of each employee's workday. Don't hesitate to click for an online demonstration of eQuest Work Order Software; an eQuest representative will guide you through the system's impressive features and benefits, ensuring you make the most informed decision for your business. By leveraging our technology, you can streamline operations and boost efficiency like never before.

The Quantum exact simulation toolkit serves as a robust simulator for quantum circuits, state-vectors, and density matrices. QuEST harnesses the power of multithreading, GPU acceleration, and distributed computing to execute tasks rapidly on devices ranging from laptops to networked supercomputers. It operates seamlessly without requiring installation and can be easily compiled for immediate use. With no setup needed, users can download, compile, and launch QuEST in just seconds. Additionally, it has no external dependencies, allowing for native compilation on various operating systems including Windows, Linux, and MacOS. No matter if you are using a laptop, desktop, supercomputer, or even a microcontroller in the cloud, getting QuEST up and running typically requires only a handful of terminal commands. This accessibility makes QuEST a preferred choice for those delving into quantum simulations.

Today, managing content is more than just storing and retrieving documents. A mobile workforce demands efficient distribution. Decentralized decision-making requires routing for approvals of complex workflows. The document is not what matters, but the information it contains. ImageQuest ECM was designed to support small businesses to large enterprises. Information must be captured, stored and retrieved so that paper can be eliminated. ImageQuest ECM from Informa is a powerful content management software. It can be used as a standalone document management system or as part of an integrated workflow and process automation application.

When PostgreSQL serves as the foundation of your enterprise and is a key data source, PeerDB offers an efficient, straightforward, and economical solution for replicating data from PostgreSQL to data warehouses, queues, and storage systems. It is engineered to function seamlessly at any scale and is specifically adapted for various data repositories. By utilizing replication messages sourced from the PostgreSQL replication slot, PeerDB adeptly replays schema updates while providing alerts for slot growth and active connections. It also includes native support for PostgreSQL toast columns and large JSONB columns, making it particularly advantageous for IoT applications. The platform features an optimized query architecture aimed at minimizing warehouse expenditures, which is especially beneficial for users of Snowflake and BigQuery. Additionally, it accommodates partitioned tables through both publication mechanisms. PeerDB ensures rapid and reliable initial data loads via transaction snapshotting and CTID scanning techniques. With features such as high availability, in-place upgrades, autoscaling, advanced logging, comprehensive metrics, and monitoring dashboards, as well as burstable instance types, it is also well-suited for development environments. Overall, PeerDB stands out as a versatile tool that effectively meets the diverse needs of modern data management.

mQuest® Market Research stands out as a premier survey platform tailored for diverse modern market research needs, receiving widespread approval. Its adaptable order management, straightforward implementation, and accurate analysis render mQuest® POS an essential tool for point-of-sale research. Customized checklists, comprehensive visual documentation, and meticulous analysis contribute to the ongoing enhancement of quality standards. mQuest® Lead simplifies the automatic mobile gathering of leads via smartphones or tablets, alongside efficient follow-up processes. Whether conducted through interviews or individual submissions, mQuest® Car Clinic facilitates prototype testing with assurance and security. Furthermore, mQuest® empowers field workers to manage all orders and activities seamlessly while on the move through mQuest® service. Every step of the process can be systematically recorded using checklists and protocols, ensuring nothing is overlooked in the research journey. With its robust features, mQuest® not only enhances the effectiveness of research efforts but also fosters continuous improvement in quality assurance practices.

Proficy Historian stands out as a premier historian software solution designed to gather industrial time-series and A&E data at remarkable speeds, ensuring secure and efficient storage, distribution, and rapid access for analysis, ultimately enhancing business value. With a wealth of experience and a track record of thousands of successful implementations globally, Proficy Historian transforms how organizations operate and compete by making critical data accessible for analyzing asset and process performance. The latest version of Proficy Historian offers improved usability, configurability, and maintainability thanks to significant advancements in its architecture. Users can leverage the solution's powerful yet straightforward features to derive new insights from their equipment, process data, and business strategies. Additionally, the remote collector management feature enhances user experience, while horizontal scalability facilitates comprehensive data visibility across the enterprise, making it an essential tool for modern businesses. By adopting Proficy Historian, companies can unlock untapped potential and drive operational excellence.

FaceQuest® is an innovative facial recognition solution developed and managed by Ampyard, designed to cater to a diverse clientele ranging from students and developers to enterprise customers across the globe. This cutting-edge technology supports various sectors including banking, fintech, insurance, ride-hailing, delivery services, retail, subscription applications, educational institutions, and corporate environments. With FaceQuest, you can quickly dive into face recognition with minimal setup required, and if preferred, you can solely utilize API calls for seamless integration. This allows for automation of tasks by leveraging FaceQuest® APIs, enabling face recognition through any programming language simply by using your secret key. The flexible pay-as-you-grow pricing structure means you only pay for what you use – if your needs decrease, so does your cost. You don’t need advanced infrastructure or AI programming expertise to incorporate face recognition into your applications. By using FaceQuest®, you can focus on developing your software without the burden of hefty investments or ongoing maintenance for sophisticated infrastructure, ensuring a smooth and efficient integration process. Whether you're a startup or an established business, FaceQuest® can enhance your projects with ease and efficiency.

Fauna is a data API that supports rich clients with serverless backends. It provides a web-native interface that supports GraphQL, custom business logic, frictionless integration to the serverless ecosystem, and a multi-cloud architecture that you can trust and grow with.

Quest for Business offers a seamless launch experience and a dedicated in-headset interface, making it both user-friendly and ready for enterprise applications. Break down physical barriers and explore innovative methods for training, collaboration, and productivity enhancement through VR with Quest for Business. Built on a robust enterprise-grade infrastructure, this platform meets strict data security requirements while providing straightforward device management and comprehensive support, ensuring easy implementation and scalability within your organization. Our flagship VR headset, Meta Quest 2, allows for unrestricted movement and interaction in virtual spaces. With its lightweight design and improved ergonomics, the Quest 2 can be paired with the new Elite Strap with Battery to enhance comfort and prolong battery life during extended work periods. Featuring a superior display resolution and quicker processing capabilities than its predecessor, the Quest 2 ensures that text appears sharper and that intricate models and experiences are rendered swiftly, enhancing overall user experience and efficiency. Additionally, this advanced technology allows teams to collaborate in immersive environments like never before, paving the way for unprecedented productivity levels.

The ITTIA DB suite brings together advanced features for time series, real-time data streaming, and analytics tailored for embedded systems, ultimately streamlining development processes while minimizing expenses. With ITTIA DB IoT, users can access a compact embedded database designed for real-time operations on resource-limited 32-bit microcontrollers (MCUs), while ITTIA DB SQL serves as a robust time-series embedded database that operates efficiently on both single and multicore microprocessors (MPUs). These ITTIA DB offerings empower devices to effectively monitor, process, and retain real-time data. Additionally, the products are specifically engineered to meet the needs of Electronic Control Units (ECUs) within the automotive sector. To ensure data security, ITTIA DB incorporates comprehensive protection mechanisms against unauthorized access, leveraging encryption, authentication, and the DB SEAL feature. Furthermore, ITTIA SDL adheres to the standards set forth by IEC/ISO 62443, reinforcing its commitment to safety. By integrating ITTIA DB, developers can seamlessly collect, process, and enhance incoming real-time data streams through a specialized SDK designed for edge devices, allowing for efficient searching, filtering, joining, and aggregating of data right at the edge. This comprehensive approach not only optimizes performance but also supports the growing demand for real-time data handling in today's technology landscape.

Transform Your Classroom into a Thrilling RPG Journey TeachQuest is an innovative classroom management platform that gamifies education, creating an exhilarating RPG-like atmosphere for students. It captivates learners, monitors their development, and fosters an engaging educational space. Teach. Quest. Repeat. As the most comprehensive alternative to Classcraft, it serves as a teacher-led behavior management tool that revolutionizes classrooms with RPG elements, offering unmatched customization options. If you're on the lookout for alternatives to Classcraft, TeachQuest stands out by providing a robust solution that surpasses many other options available. Unlike other alternatives that may offer only basic gamification features, TeachQuest preserves the rich RPG experience that initially made the original platform so compelling and effective. Furthermore, it encourages collaboration and creativity among students, enhancing their overall learning journey.

Handling and storing tabular data, such as that found in CSV or Parquet formats, is essential for data management. Transferring large result sets to clients is a common requirement, especially in extensive client/server frameworks designed for centralized enterprise data warehousing. Additionally, writing to a single database from various simultaneous processes poses its own set of challenges. DuckDB serves as a relational database management system (RDBMS), which is a specialized system for overseeing data organized into relations. In this context, a relation refers to a table, characterized by a named collection of rows. Each row within a table maintains a consistent structure of named columns, with each column designated to hold a specific data type. Furthermore, tables are organized within schemas, and a complete database comprises a collection of these schemas, providing structured access to the stored data. This organization not only enhances data integrity but also facilitates efficient querying and reporting across diverse datasets.

IndexedDB serves as a fundamental API designed for the client-side storage of large volumes of structured data, including files and blobs. It utilizes indexing to facilitate efficient searches, making it suitable for extensive datasets. While traditional web storage can handle smaller data quantities well, it falls short when it comes to managing larger structured datasets, a gap that IndexedDB effectively fills. Functioning as a transactional database system akin to SQL-based Relational Database Management Systems (RDBMS), IndexedDB diverges from them by operating as a JavaScript-based object-oriented database. This distinction allows it to store and retrieve objects indexed by keys, with support for any objects that comply with the structured clone algorithm. Users must outline the database schema, establish a connection, and execute retrieval and updating of data through a series of transactions. Additionally, like other web storage solutions, IndexedDB adheres to the same-origin policy, ensuring data security and integrity across different domains. With its versatility and capability, IndexedDB has become an essential tool for developers dealing with complex data needs on the web.

NetQuest BI offers comprehensive visual representations and analyses of network data, including CDRs and outputs from NetAnalyzer reports, while incorporating critical information such as Country/City codes, ratings, LERG, and trunk group details. This platform is optimized for both short-term, 90-day online storage and extended periods, facilitating in-depth analysis and extensive drill-down features for use across multiple departments. With its user-friendly interface, NetQuest BI ensures that users can efficiently navigate and extract valuable insights from their data.

kdb Insights is an advanced analytics platform built for the cloud, enabling high-speed real-time analysis of both live and past data streams. It empowers users to make informed decisions efficiently, regardless of the scale or speed of the data, and boasts exceptional price-performance ratios, achieving analytics performance that is up to 100 times quicker while costing only 10% compared to alternative solutions. The platform provides interactive data visualization through dynamic dashboards, allowing for immediate insights that drive timely decision-making. Additionally, it incorporates machine learning models to enhance predictive capabilities, identify clusters, detect patterns, and evaluate structured data, thereby improving AI functionalities on time-series datasets. With remarkable scalability, kdb Insights can manage vast amounts of real-time and historical data, demonstrating effectiveness with loads of up to 110 terabytes daily. Its rapid deployment and straightforward data ingestion process significantly reduce the time needed to realize value, while it natively supports q, SQL, and Python, along with compatibility for other programming languages through RESTful APIs. This versatility ensures that users can seamlessly integrate kdb Insights into their existing workflows and leverage its full potential for a wide range of analytical tasks.

What makes eXtremeDB platform independent? - Hybrid storage of data. Unlike other IMDS databases, eXtremeDB databases are all-in-memory or all-persistent. They can also have a mix between persistent tables and in-memory table. eXtremeDB's Active Replication Fabric™, which is unique to eXtremeDB, offers bidirectional replication and multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more. - Row and columnar flexibility for time series data. eXtremeDB supports database designs which combine column-based and row-based layouts in order to maximize the CPU cache speed. - Client/Server and embedded. eXtremeDB provides data management that is fast and flexible wherever you need it. It can be deployed as an embedded system and/or as a clients/server database system. eXtremeDB was designed for use in resource-constrained, mission-critical embedded systems. Found in over 30,000,000 deployments, from routers to satellites and trains to stock market world-wide.