Alternatives to Warp 10

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times. RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.

Our namesake product, Minitab Statistical Software, leads the way in data analysis with the power to visualize, analyze and harness your data to gain insights and solve your toughest challenges. Access trusted, proven and modern analytics combined with dynamic visualizations to empower you and your decisions. The latest version of Minitab Statistical Software includes access to Minitab on the cloud so you can analyze from anywhere, and Graph Builder, our new interactive tool to instantly create multiple graph options at once. Minitab offers modules for Predictive Analytics and Healthcare to boost your analytics even further. Available in 8 languages: English, Chinese, French, German, Japanese, Korean, Spanish, and Portuguese. For 50 years, Minitab has helped thousands of companies and institutions spot trends, solve problems, and discover valuable insights in their data through our comprehensive, best-in-class suite of data analysis and process improvement tools.

A Revenue Operations Platform that increases revenue results. Automated CRM updates Check. Time series analysis? Check. Clari offers more than just innovative features. Clari solves the real problem by combining revenue intelligence, forecasting, and execution insights. It is efficient and predictable in hitting your targets every quarter. Clari's Revenue Operations Platform is purpose-built to increase revenue predictability. It takes previously untapped data, such as call logs, CRM, email, and CRM, and turns it into execution insights that you can use for your entire revenue team. Clari combines AI insights with human intuition to enable your team to forecast with greater accuracy and foresight. It uses a consistent, automated process that can be used to manage all businesses in your company. You can collect valuable activity data from prospects, customers, and reps so you are always up to date on what's happening in your deals, in your business, and with your team.

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.

Analyzing time-series data is crucial for the daily functions of numerous businesses. Common applications involve assessing consumer foot traffic and conversion rates for retailers, identifying anomalies in data, discovering real-time correlations within sensor information, and producing accurate recommendations. With the Cloud Inference API Alpha, businesses can derive real-time insights from their time-series datasets that they input. This tool provides comprehensive details about API query results, including the various groups of events analyzed, the total number of event groups, and the baseline probability associated with each event returned. It enables real-time streaming of data, facilitating the computation of correlations as events occur. Leveraging Google Cloud’s robust infrastructure and a comprehensive security strategy that has been fine-tuned over 15 years through various consumer applications ensures reliability. The Cloud Inference API is seamlessly integrated with Google Cloud Storage services, enhancing its functionality and user experience. This integration allows for more efficient data handling and analysis, positioning businesses to make informed decisions faster.

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.

QuestDB is an advanced relational database that focuses on column-oriented storage optimized for time series and event-driven data. It incorporates SQL with additional features tailored for time-based analytics to facilitate real-time data processing. This documentation encompasses essential aspects of QuestDB, including initial setup instructions, comprehensive usage manuals, and reference materials for syntax, APIs, and configuration settings. Furthermore, it elaborates on the underlying architecture of QuestDB, outlining its methods for storing and querying data, while also highlighting unique functionalities and advantages offered by the platform. A key feature is the designated timestamp, which empowers time-focused queries and efficient data partitioning. Additionally, the symbol type enhances the efficiency of managing and retrieving frequently used strings. The storage model explains how QuestDB organizes records and partitions within its tables, and the use of indexes can significantly accelerate read access for specific columns. Moreover, partitions provide substantial performance improvements for both calculations and queries. With its SQL extensions, users can achieve high-performance time series analysis using a streamlined syntax that simplifies complex operations. Overall, QuestDB stands out as a powerful tool for handling time-oriented data effectively.

Machbase is a leading time-series database designed for real-time storage and analysis of vast amounts of sensor data from various facilities. It stands out as the only database management system (DBMS) capable of processing and analyzing large datasets at remarkable speeds, showcasing its impressive capabilities. Experience the extraordinary processing speeds that Machbase offers! This innovative product allows for immediate handling, storage, and analysis of sensor information. It achieves rapid storage and querying of sensor data by integrating the DBMS directly into Edge devices. Additionally, it provides exceptional performance in data storage and extraction when operating on a single server. With the ability to configure multi-node clusters, Machbase offers enhanced availability and scalability. Furthermore, it serves as a comprehensive management solution for Edge computing, addressing device management, connectivity, and data handling needs effectively. In a fast-paced data-driven world, Machbase proves to be an essential tool for industries relying on real-time sensor data analysis.

Experience the power of advanced computing right at your fingertips. With the capabilities of parallel computing and innovative algorithms, there's no reason to hesitate any longer. Created specifically for data scientists in the business realm, this all-inclusive time-series platform delivers the fastest computing available. Shapelets offers a suite of analytical tools, including causality analysis, discord detection, motif discovery, forecasting, and clustering, among others. You can also run, expand, and incorporate your own algorithms into the Shapelets platform, maximizing the potential of Big Data analysis. Seamlessly integrating with various data collection and storage systems, Shapelets ensures compatibility with MS Office and other visualization tools, making it easy to share insights without requiring extensive technical knowledge. Our user interface collaborates with the server to provide interactive visualizations, allowing you to fully leverage your metadata and display it through a variety of modern graphical representations. Additionally, Shapelets equips professionals in the oil, gas, and energy sectors to conduct real-time analyses of their operational data, enhancing decision-making and operational efficiency. By utilizing Shapelets, you can transform complex data into actionable insights.

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.

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.

The Waylay platform is a modular IoT solution that offers top-tier OEM technology for backend development and operational processes, facilitating the rapid delivery of IoT solutions on a large scale. It features sophisticated rule logic modeling, execution capabilities, and comprehensive lifecycle management. This platform allows for the automation of various data workflows, regardless of their complexity. Designed to seamlessly handle the diverse data patterns found in IoT, OT, and IT, Waylay integrates both streaming and time series analytics into a single, collaborative intelligence environment. By providing non-developer teams with user-friendly, self-service apps focused on key performance indicators, it significantly shortens the time to market for IoT products. Users can identify the most effective automation tools for their specific IoT applications and evaluate them against established benchmarks. Additionally, developing IoT applications diverges significantly from traditional IT development, as it entails connecting the physical realm of Operations Technology, which involves sensors and actuators, with the digital landscape of Information Technology, encompassing databases and software systems. This bridging of worlds highlights the unique challenges and opportunities presented by IoT application development.

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.

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.

InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 250 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on-premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge. To get started, InfluxData offers free training through InfluxDB University.

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.

A Time Series Database (TSDB) is designed for rapid data input and output, allowing for swift reading and writing of information. It achieves impressive compression rates that lead to economical data storage solutions. Moreover, this service facilitates visualization techniques, such as precision reduction, interpolation, and multi-metric aggregation, alongside the processing of query results. By utilizing TSDB, businesses can significantly lower their storage expenses while enhancing the speed of data writing, querying, and analysis. This capability allows for the management of vast quantities of data points and enables more frequent data collection. Its applications span various sectors, including IoT monitoring, enterprise energy management systems (EMSs), production security oversight, and power supply monitoring. Additionally, TSDB is instrumental in optimizing database structures and algorithms, capable of processing millions of data points in mere seconds. By employing an advanced compression method, it can minimize each data point's size to just 2 bytes, leading to over 90% savings in storage costs. Consequently, this efficiency not only benefits businesses financially but also streamlines operational workflows across different industries.

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.

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.

Effortlessly handle intricate models with ArcadeDB while ensuring no compromises are made. Say goodbye to the concept of Polyglot Persistence; there's no need to juggle multiple databases. With ArcadeDB's Multi-Model database, you can seamlessly store graphs, documents, key values, and time series data in one unified solution. As each model is inherently compatible with the database engine, you can avoid the delays caused by translation processes. Powered by advanced Alien Technology, ArcadeDB's engine can process millions of records every second. Notably, the speed of data traversal remains constant regardless of the database's size, whether it houses a handful of records or billions. ArcadeDB is versatile enough to function as an embedded database on a single server and can easily scale across multiple servers using Kubernetes. Its compact design allows it to operate on any platform while maintaining a minimal footprint. Your data's security is paramount; our robust, fully transactional engine guarantees durability for mission-critical production databases. Additionally, ArcadeDB employs a Raft Consensus Algorithm to ensure consistency and reliability across multiple servers, making it a top choice for data management. In an era where efficiency and reliability are crucial, ArcadeDB stands out as a comprehensive solution for diverse data storage needs.

Kibana serves as a free and open user interface that enables the visualization of your Elasticsearch data while providing navigational capabilities within the Elastic Stack. You can monitor query loads or gain insights into how requests traverse your applications. This platform offers flexibility in how you choose to represent your data. With its dynamic visualizations, you can start with a single inquiry and discover new insights along the way. Kibana comes equipped with essential visual tools such as histograms, line graphs, pie charts, and sunbursts, among others. Additionally, it allows you to conduct searches across all your documents seamlessly. Utilize Elastic Maps to delve into geographic data or exercise creativity by visualizing custom layers and vector shapes. You can also conduct sophisticated time series analyses on your Elasticsearch data using our specially designed time series user interfaces. Furthermore, articulate queries, transformations, and visual representations with intuitive and powerful expressions that are easy to master. By employing these features, you can uncover deeper insights into your data, enhancing your overall analytical capabilities.

Detecting anomalies in time series data is critical for the daily functions of numerous organizations. The Timeseries Insights API Preview enables you to extract real-time insights from your time-series datasets effectively. It provides comprehensive information necessary for interpreting your API query results, including details on anomaly occurrences, projected value ranges, and segments of analyzed events. This capability allows for the real-time streaming of data, facilitating the identification of anomalies as they occur. With over 15 years of innovation in security through widely-used consumer applications like Gmail and Search, Google Cloud offers a robust end-to-end infrastructure and a layered security approach. The Timeseries Insights API is seamlessly integrated with other Google Cloud Storage services, ensuring a uniform access method across various storage solutions. You can analyze trends and anomalies across multiple event dimensions and manage datasets that encompass tens of billions of events. Additionally, the system is capable of executing thousands of queries every second, making it a powerful tool for real-time data analysis and decision-making. Such capabilities are invaluable for businesses aiming to enhance their operational efficiency and responsiveness.

Google Cloud Bigtable provides a fully managed, scalable NoSQL data service that can handle large operational and analytical workloads. Cloud Bigtable is fast and performant. It's the storage engine that grows with your data, from your first gigabyte up to a petabyte-scale for low latency applications and high-throughput data analysis. Seamless scaling and replicating: You can start with one cluster node and scale up to hundreds of nodes to support peak demand. Replication adds high availability and workload isolation to live-serving apps. Integrated and simple: Fully managed service that easily integrates with big data tools such as Dataflow, Hadoop, and Dataproc. Development teams will find it easy to get started with the support for the open-source HBase API standard.

TimescaleDB is the most popular open-source relational database that supports time-series data. Fully managed or self-hosted. You can rely on the same PostgreSQL that you love. It has full SQL, rock-solid reliability and a huge ecosystem. Write millions of data points per node. Horizontally scale up to petabytes. Don't worry too much about cardinality. Reduce complexity, ask more questions and build more powerful applications. You will save money with 94-97% compression rates using best-in-class algorithms, and other performance improvements. Modern cloud-native relational database platform that stores time-series data. It is based on PostgreSQL and TimescaleDB. This is the fastest, easiest, and most reliable way to store all of your time-series information. All observability data can be considered time-series data. Time-series problems are those that require efficient solutions to infrastructure and application problems.

The remarkable aspect of the Canary Historian is its versatility, functioning equally well on-site and across an entire organization. It allows for local data logging while simultaneously transmitting that data to your enterprise historian. Moreover, as your needs expand, the solution adapts seamlessly to accommodate growth. A single Canary Historian is capable of logging over two million tags, and by clustering multiple units, you can manage tens of millions of tags effortlessly. These enterprise historian solutions can be deployed in your own data centers or on cloud platforms like AWS and Azure. Additionally, contrary to many other enterprise historian options, Canary Historians do not necessitate large specialized teams for maintenance. Serving as a NoSQL time series database, the Canary Historian implements loss-less compression algorithms, delivering exceptional performance without the need for data interpolation, which is a significant advantage for users. This dual capability ensures that both speed and efficiency are maximized in data handling.

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.

IBM Informix® is a highly adaptable and efficient database that can effortlessly combine SQL, NoSQL/JSON, as well as time series and spatial data. Its flexibility and user-friendly design position Informix as a top choice for diverse settings, ranging from large-scale enterprise data warehouses to smaller individual application development projects. Moreover, due to its compact footprint and self-managing features, Informix is particularly advantageous for embedded data management applications. The rising demand for IoT data processing necessitates strong integration and processing capabilities, which Informix fulfills with its hybrid database architecture that requires minimal administrative effort and has a small memory footprint while delivering robust functionality. Notably, Informix is well-equipped for multi-tiered architectures that necessitate processing at various levels, including devices, gateway layers, and cloud environments. Furthermore, it incorporates native encryption to safeguard data both at rest and in transit. Additionally, Informix supports a flexible schema alongside multiple APIs and configurations, making it a versatile choice for modern data management challenges.

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.

Telegraf is an open-source server agent that helps you collect metrics from your sensors, stacks, and systems. Telegraf is a plugin-driven agent that collects and sends metrics and events from systems, databases, and IoT sensors. Telegraf is written in Go. It compiles to a single binary and has no external dependencies. It also requires very little memory. Telegraf can gather metrics from a wide variety of inputs and then write them into a wide range of outputs. It can be easily extended by being plugin-driven for both the collection and output data. It is written in Go and can be run on any system without external dependencies. It is easy to collect metrics from your endpoints with the 300+ plugins that have been created by data experts in the community.

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.

Cortex is an innovative open-source solution that enhances horizontal scalability. While Prometheus is capable of handling up to 1 million samples per second on a single machine, Cortex enables a virtually limitless level of horizontal scaling. In an ever-evolving landscape, it is essential to adopt alternative strategies for monitoring individual virtual machines or servers. Prometheus features a service-discovery-driven, pull-based metrics system that caters to the dynamic characteristics of microservices. This capability allows for seamless monitoring of your entire ecosystem, regardless of the number of components involved. You can instrument your application to generate tailored metrics using the standard Prometheus client libraries, or you can leverage the vast array of Prometheus Exporters that gather data from existing software like MySQL, Redis, Java, ElasticSearch, and many others. By adopting these tools, organizations can ensure they maintain visibility and control over their complex infrastructures. This flexibility is particularly valuable in today's fast-paced, continuously changing technological environments.

BangDB seamlessly incorporates AI, streaming capabilities, graph processing, and analytics directly within its database, empowering users to handle intricate data types like text, images, videos, and objects for immediate data processing and analysis. Users can ingest or stream various data types, process them, train models, make predictions, uncover patterns, and automate actions, facilitating applications such as IoT monitoring, fraud prevention, log analysis, lead generation, and personalized experiences. Modern applications necessitate the simultaneous ingestion, processing, and querying of diverse data types to address specific challenges effectively. BangDB accommodates a wide array of valuable data formats, simplifying problem-solving for users. The increasing demand for real-time data is driving the need for concurrent streaming and predictive analytics, which are essential for enhancing and optimizing business operations. As organizations continue to evolve, the ability to rapidly adapt to new data sources and insights will become increasingly vital for maintaining a competitive edge.

Seeq is the first app that focuses on process data analytics. Search your data, add context, model, cleanse, find patterns, establish boundaries and collaborate in real-time with time series data. No matter what your operational data system or process historian - the OSIsoft®, PI System®, Honeywell’s Uniformance®, Emerson DeltaV and Ovation or Inductive Automation's Ignition - Seeq can connect and get to work in minutes. What's missing in the current hype about predictive analytics, machine learning and data science is solutions to the real problems that an analytics-driven company faces. Your current employees' expertise. Collaboration and knowledge capture are key to fostering sharing and reuse of analytics efforts. The ability to quickly distribute insights to those who need them to improve outcomes.

Amazon Forecast is an entirely managed service that employs machine learning techniques to provide exceptionally precise predictions. In the contemporary business landscape, organizations utilize a range of tools, from basic spreadsheets to intricate financial planning applications, in their quest to accurately project future outcomes such as product demand, resource allocation, and overall financial results. These forecasting tools generate predictions by analyzing historical data known as time series data. For instance, they might estimate future demand for raincoats based solely on past sales figures, operating under the premise that future performance will mirror historical trends. However, this methodology can falter when tasked with managing extensive datasets that exhibit irregular patterns. Moreover, it often struggles to seamlessly integrate evolving data streams—like pricing, discounts, web traffic, and workforce numbers—with pertinent independent variables, such as product specifications and retail locations. As a result, businesses seeking reliable forecasts may find themselves facing significant challenges in adapting to the complexities of their data.

A NEW WAY TO BUY ENERGY AND RAW MATERIALS Datapred is an online integrated software program for energy and raw materials buyers. It assists them with market awareness and reporting, and provides powerful decision support. Connectivity to both internal and external data sources, along with powerful analysis, forecasting, and optimization models, ensures that buying decisions are compatible with both market conditions and operational conditions. Datapred is used in the energy industry by energy advisors and industrial companies.

Autobox is the most user-friendly solution for forecasting available today. Tailored for both beginners and seasoned professionals, it allows users to input their data and generate forecasts with expert-level accuracy. Regardless of your current forecasting technique, Autobox enhances your precision in predictions significantly. This innovative tool has been recognized as the “best-dedicated forecasting program” in the esteemed Principles of Forecasting textbook and has transitioned into an online platform. The unique methodology employed by AFS does not confine data to a rigid model or a small selection of models, enabling Autobox to optimally integrate historical data and causal factors while addressing level shifts, local time trends, pulses, and seasonal variations as needed. The Autobox engine is adept at uncovering new causal variables by analyzing patterns within historical forecast errors and outliers, often revealing causal factors that users may have been unaware of, such as promotions, holidays, and day-of-the-week influences. This capability allows users to harness a broader range of insights, ultimately leading to more refined and actionable forecasts.

Time-sensitive decisions require immediate attention. With TRND, machine learning projects can be accelerated from lengthy timelines to mere minutes, similar to the efficiency of Google's AI search engine that delivers insights in real time. Financial losses can stem from inaccuracies, and unlike standard KPIs, TRND identifies patterns that uncover critical information hidden from traditional metrics. This tool empowers decision-makers by highlighting trends that signal emerging threats or opportunities. In today's digital landscape, knowledge equates to financial advantage. TRND allows for the creation of shareable pattern libraries, promoting rapid learning and implementation for enhancing business operations. Without comprehensive monitoring, potential profits remain untapped. TRND excels at not only identifying rare insights but also continuously tracking relevant information across all indicators. The myth that scaling efforts must be excessively costly is shattered by our innovative search-based method, which makes extensive micro-monitoring both feasible and economical. In essence, TRND revolutionizes the approach to data utilization, ensuring that businesses stay ahead in a competitive environment.

TrendMiner is an advanced industrial analytics platform that is fast, powerful, and intuitive. It was designed to monitor and troubleshoot industrial processes in real-time. It allows for robust data collection, analysis and visualization, allowing everyone in industrial operations to make smarter data-driven decision efficiently. TrendMiner is a Proemion Company founded in 2008. Our global headquarters are located in Belgium and we have offices in the U.S.A., Germany, Spain, and the Netherlands. TrendMiner has strategic alliances with major players like Amazon, Microsoft and SAP. It also offers standard integrations for a variety of historians, including Honeywell PHD and GE Proficy Historian.

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

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.

Real-time analytics on raw data. Live ingest from S3, DynamoDB, DynamoDB and more. Raw data can be accessed as SQL tables. In minutes, you can create amazing data-driven apps and live dashboards. Rockset is a serverless analytics and search engine that powers real-time applications and live dashboards. You can directly work with raw data such as JSON, XML and CSV. Rockset can import data from real-time streams and data lakes, data warehouses, and databases. You can import real-time data without the need to build pipelines. Rockset syncs all new data as it arrives in your data sources, without the need to create a fixed schema. You can use familiar SQL, including filters, joins, and aggregations. Rockset automatically indexes every field in your data, making it lightning fast. Fast queries are used to power your apps, microservices and live dashboards. Scale without worrying too much about servers, shards or pagers.

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.

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.

OneTick Database has gained widespread acceptance among top banks, brokerages, data vendors, exchanges, hedge funds, market makers, and mutual funds due to its exceptional performance, advanced features, and unparalleled functionality. Recognized as the foremost enterprise solution for capturing tick data, conducting streaming analytics, managing data, and facilitating research, OneTick stands out in the financial sector. Its unique capabilities have captivated numerous hedge funds and mutual funds, alongside traditional financial institutions, enhancing their operational efficiency. The proprietary time series database offered by OneTick serves as a comprehensive multi-asset class platform, integrating a streaming analytics engine and embedded business logic that obviates the necessity for various separate systems. Furthermore, this robust system is designed to deliver the lowest total cost of ownership, making it an attractive option for organizations aiming to optimize their data management processes. With its innovative approach and cost-effectiveness, OneTick continues to redefine industry standards.