Warp 10 Alternatives

Azure Time Series Insights Gen2 is an open and scalable end-to-end IoT analytics service featuring best-in-class user experiences and rich APIs to integrate its powerful capabilities into your existing workflow or application. You can use it to collect, process, store, query and visualize data at Internet of Things (IoT) scale--data that's highly contextualized and optimized for time series. Azure Time Series Insights Gen2 is designed for ad hoc data exploration and operational analysis allowing you to uncover hidden trends, spotting anomalies, and conduct root-cause analysis. It's an open and flexible offering that meets the broad needs of industrial IoT deployments.

Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day up to 1,000 times faster and at as little as 1/10th the cost of relational databases. Amazon Timestream saves you time and cost in managing the lifecycle of time series data by keeping recent data in memory and moving historical data to a cost optimized storage tier based upon user defined policies. Amazon Timestream’s purpose-built query engine lets you access and analyze recent and historical data together, without needing to specify explicitly in the query whether the data resides in the in-memory or cost-optimized tier. Amazon Timestream has built-in time series analytics functions, helping you identify trends and patterns in your data in near real-time.

VictoriaMetrics is a fast and scalable open source time series database and monitoring solution. It's designed to be user-friendly, allowing users to build a monitoring platform without scalability issues and with minimal operational burden. VictoriaMetrics is ideal for solving use cases with large amounts of time series data for IT infrastructure, APM, Kubernetes, IoT sensors, automotive vehicles, industrial telemetry, financial data, and other enterprise-level workloads. VictoriaMetrics is powered by several components, making it the perfect solution for collecting metrics (both push and pull models), running queries, and generating alerts. With VictoriaMetrics, you can store millions of data points per second on a single instance or scale to a high-load monitoring system across multiple data centers. Plus, it's designed to store 10x more data using the same compute and storage resources as existing solutions, making it a highly efficient choice.

Time-series analysis is essential for the day-to-day operation of many companies. Most popular use cases include analyzing foot traffic and conversion for retailers, detecting data anomalies, identifying correlations in real-time over sensor data, or generating high-quality recommendations. With Cloud Inference API Alpha, you can gather insights in real-time from your typed time-series datasets. Get everything you need to understand your API queries results, such as groups of events that were examined, the number of groups of events, and the background probability of each returned event. Stream data in real-time, making it possible to compute correlations for real-time events. Rely on Google Cloud’s end-to-end infrastructure and defense-in-depth approach to security that’s been innovated on for over 15 years through consumer apps. At its core, Cloud Inference API is fully integrated with other Google Cloud Storage services.

Modular IoT platform providing best-of-breed OEM technology for back-end development and operations, enabling accelerated IoT solution delivery at scale. Advanced rule logic modeling, execution and lifecycle management. Automate any data workflow, from the simple to the complex. The Waylay platform is built from the ground up to natively cope with the multiple data patterns of IoT, OT and IT. Leverage streaming and time series analytics within the same collaborative intelligence platform. Accelerate the time to market of your IoT solutions by easily delivering self-service and KPI-centric apps to non-developer teams. Find out what automation tools are best suited to your IoT use case, then test them against the benchmark. IoT application development is fundamentally different from “normal” IT development. It requires bridging the physical world of Operations Technology (OT) with sensors, actuators and gateways to the digital world of Information Technology (IT) with databases.

Machbase, a time-series database that stores and analyzes a lot of sensor data from various facilities in real time, is the only DBMS solution that can process and analyze big data at high speed. Experience the amazing speed of Machbase! It is the most innovative product that enables real-time processing, storage, and analysis of sensor data. High speed sensor data storage and inquiry for sensor data by embedding DBMS in an Edge devices. Best data storage and extraction performance by DBMS running in a single server. Configuring Multi-node cluster with the advantages of availability and scalability. Total management solution of Edge computing for device, connectivity and data.

QuestDB is a relational column-oriented database designed for time series and event data. It uses SQL with extensions for time series to assist with real-time analytics. These pages cover core concepts of QuestDB, including setup steps, usage guides, and reference documentation for syntax, APIs and configuration. This section describes the architecture of QuestDB, how it stores and queries data, and introduces features and capabilities unique to the system. Designated timestamp is a core feature that enables time-oriented language capabilities and partitioning. Symbol type makes storing and retrieving repetitive strings efficient. Storage model describes how QuestDB stores records and partitions within tables. Indexes can be used for faster read access on specific columns. Partitions can be used for significant performance benefits on calculations and queries. SQL extensions allow performant time series analysis with a concise syntax.

Proficy Historian is a best-in-class historian software solution that collects industrial time-series and A&E data at very high speed, stores it efficiently and securely, distributes it, and allows for fast retrieval and analysis —driving greater business value. With decades of experience and thousands of successful customer installations around the world, Proficy Historian changes the way companies perform and compete by making data available for asset and process performance analysis. The most recent Proficy Historian enhances usability, configurability and maintainability with significant architectural improvements. Take advantage of the solution’s simple yet powerful features to unlock new value from your equipment, process data, and business models. Remote collector management with UX. Horizontal scalability that enables enterprise-wide data visibility.

Powerful computing at your fingertips. Parallel computing, groundbreaking algorithms, so what are you waiting for? Designed to empower data scientists in business. Get the fastest computing in an all-inclusive time-series platform. Shapelets provides you with analytical features such as causality, discords and motif discovery, forecasting, clustering, etc. Run, extend and integrate your own algorithms into the Shapelets platform to make the most of Big Data analysis. Shapelets integrates seamlessly with any data collection and storage solution. It also integrates with MS Office and any other visualization tool to simplify and share insights without any technical acumen. Our UI works with the server to bring you interactive visualizations. You can make the most of your metadata and represent it in the many different visual graphs provided by our modern interface. Shapelets enables users from the oil, gas, and energy industry to perform real-time analysis of operational data.

The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.

Riak® TS is the only enterprise-grade NoSQL time series database optimized specifically for IoT and Time Series data. It ingests, transforms, stores, and analyzes massive amounts of time series data. Riak TS is engineered to be faster than Cassandra. The Riak TS masterless architecture is designed to read and write data even in the event of hardware failures or network partitions. Data is evenly distributed across the Riak ring and, by default, there are three replicas of your data. This ensures at least one copy of your data is available for read operations. Riak TS is a distributed system with no central coordinator. It is easy to set up and operate. The masterless architecture makes it easy to add and remove nodes from a cluster. The masterless architecture of Riak TS makes it easy to add and remove nodes from your cluster. You can achieve predictable and near-linear scale by adding nodes using commodity hardware.

Decisions can't wait. Compress machine learning projects from months to minutes. Like Google, our AI search engine brings you insights instantly. Inaccuracy costs money. Patterns reveal what KPIs and averages miss. TRND uncovers the patterns that provide the early warning signs missing from the KPIs. Empower the decision maker. Trends are most relevant to decision-makers who want to know whether a threat or an opportunity is bubbling up. In the digital economy knowledge is money. TRND enables creation of sharable pattern libraries that facilitate fast learning and deployment for business improvement. If you can't monitor all, you monetize none. TRND doesn't just find needles in haystacks; it constantly monitors all needles for relevant information. If you can't afford it, you can't do it. It used to be that scale broke the bank. Our search-based approach makes micro monitoring at scale affordable.

OpenTSDB consists of a Time Series Daemon (TSD) as well as set of command line utilities. Interaction with OpenTSDB is primarily achieved by running one or more of the independent TSDs. There is no master, no shared state so you can run as many TSDs as required to handle any load you throw at it. Each TSD uses the open source database HBase or hosted Google Bigtable service to store and retrieve time-series data. The data schema is highly optimized for fast aggregations of similar time series to minimize storage space. Users of the TSD never need to access the underlying store directly. You can communicate with the TSD via a simple telnet-style protocol, an HTTP API or a simple built-in GUI. The first step in using OpenTSDB is to send time series data to the TSDs. A number of tools exist to pull data from various sources into OpenTSDB.

Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures.

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.

KX Streaming Analytics provides the ability to ingest, store, process, and analyze historic and time series data to make analytics, insights, and visualizations instantly available. To help ensure your applications and users are productive quickly, the platform provides the full lifecycle of data services, including query processing, tiering, migration, archiving, data protection, and scaling. Our advanced analytics and visualization tools, used widely across finance and industry, enable you to define and perform queries, calculations, aggregations, machine learning and AI on any streaming and historical data. Deployable across multiple hardware environments, data can come from real-time business events and high-volume sources including sensors, clickstreams, radio-frequency identification, GPS systems, social networking sites, and mobile devices.

Manage complex models using ArcadeDB without any compromise. Forget about Polyglot Persistence. no need for multiple databases. You can store graphs, documents, key values and time series all in one ArcadeDB Multi-Model database. Since each model is native to the database engine, you don't have to worry about translations slowing you down. ArcadeDB's engine was built with Alien Technology. It's able to crunch millions of records per second. With ArcadeDB, the traversing speed is not affected by the database size. It is always constant, whether your database has a few records or billions. ArcadeDB can work as an embedded database, on a single server and can scale up using multiple servers with Kubernetes. Flexible enough to run on any platform with a small footprint. Your data is secure. Our unbreakable fully transactional engine assures durability for mission-critical production databases. ArcadeDB uses a Raft Consensus Algorithm to maintain consistency across multiple servers.

Time Series Database (TSDB) supports high-speed data reading and writing. It offers high compression ratios for cost-efficient data storage. This service also supports visualization of precision reduction, interpolation, multi-metric aggregate computing, and query results. The TSDB service reduces storage costs and improves the efficiency of data writing, query, and analysis. This enables you to handle large amounts of data points and collect data more frequently. This service has been widely applied to systems in different industries, such as IoT monitoring systems, enterprise energy management systems (EMSs), production security monitoring systems, and power supply monitoring systems. Optimizes database architectures and algorithms. TSDB can read or write millions of data points within seconds. Applies an efficient compression algorithm to reduce the size of each data point to 2 bytes, saving more than 90% in storage costs.

BangDB natively integrates AI, streaming, graph, analytics within the DB itself to enable users to deal with complex data of different kinds, such as text, images, videos, objects etc. for real time data processing and analysis Ingest or stream any data, process it, train models, do prediction, find patterns, take action and automate all these to enable use cases such as IOT monitoring, fraud or disruption prevention, log analysis, lead generation, 1-on-1 personalisation and many more. Today’s use cases require different kinds of data to be ingested, processed, and queried at the same time for a given problem. BangDB supports most of the useful data formats to allow user to solve the problem in a simple manner. Rise of real time data pushes for real time streaming and predictive data analytics for advanced and optimized business operations.

Anomaly detection in time series data is essential for the day-to-day operation of many companies. With Timeseries Insights API Preview, you can gather insights in real-time from your time-series datasets. Get everything you need to understand your API query results, such as anomaly events, forecasted range of values, and slices of events that were examined. Stream data in real-time, making it possible to detect anomalies while they are happening. Rely on Google Cloud's end-to-end infrastructure and defense-in-depth approach to security that's been innovated for over 15 years through consumer apps like Gmail and Search. At its core, Timeseries Insights API is fully integrated with other Google Cloud Storage services, providing you with a consistent method of access across storage products. Detect trends and anomalies with multiple event dimensions. Handle datasets consisting of tens of billions of events. Run thousands of queries per second.

Kibana is a free and open user interface that lets you visualize your Elasticsearch data and navigate the Elastic Stack. Do anything from tracking query load to understanding the way requests flow through your apps. Kibana gives you the freedom to select the way you give shape to your data. With its interactive visualizations, start with one question and see where it leads you. Kibana core ships with the classics: histograms, line graphs, pie charts, sunbursts, and more. And, of course, you can search across all of your documents. Leverage Elastic Maps to explore location data, or get creative and visualize custom layers and vector shapes. Perform advanced time series analysis on your Elasticsearch data with our curated time series UIs. Describe queries, transformations, and visualizations with powerful, easy-to-learn expressions.

Seeq is the first application dedicated to process data analytics. Search your data, add context, cleanse, model, find patterns, establish boundaries, monitor assets, collaborate in real time, and interact with time series data like never before. Whatever your process historian or operational data system of record – the OSIsoft® PI System®, Honeywell's Uniformance® PHD, Emerson DeltaV and Ovation, Inductive Automation's Ignition, AspenTech IP.21, Wonderware, GE Proficy or any other – Seeq can connect and get you working in minutes. In the current hype around predictive analytics, machine learning, and data science, what’s missing are solutions to the real challenges to an analytics-driven organization. Tapping the expertise of your current employees. Support for collaboration and knowledge capture to foster sharing and reuse of analytics efforts. And the ability to rapidly distribute insights to the people who need them to quickly improve outcomes.

Autobox is simply the easiest way to forecast. Designed with both the novice and expert forecaster in mind you can load your data and forecast like a Pro. No matter what method you currently use to forecast, Autobox will improve your ability to forecast accurately. Autobox won the prestigious “best-dedicated forecasting program” in the Principles of Forecasting textbook and is now a website. AFS’s unique approach doesn’t try to shoehorn the data into a model or a limited number of models, allowing Autobox to combine, history and causal are in an optimal way incorporating when needed level shifts, local time trends, pulses, and seasonal pulses. Autobox discovers new causal variables by gleaning patterns from historical forecast errors and outliers identified by the Autobox engine! Many cases result in causal variables you may not have even known existed. i.e. promotions, holidays, day of the week effects, and many others.

TimescaleDB is the leading open-source relational database with support for time-series data. Fully managed or self‑hosted. Rely on the same PostgreSQL you know and love, with full SQL, rock-solid reliability, and a massive ecosystem. Write millions of data points per second per node. Horizontally scale to petabytes. Don’t worry about cardinality. Simplify your stack, ask more complex questions, and build more powerful applications. Spend less with 94-97% compression rates from best-in-class algorithms and other performance improvements. A modern, cloud-native relational database platform for time-series data based on TimescaleDB and PostgreSQL. The fast, easy, and reliable way to store all your time-series data. All observability data is time-series data. Efficiently finding and addressing infrastructure and application issues is a time-series problem.

Google Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. Fast and performant: Use Cloud Bigtable as the storage engine that grows with you from your first gigabyte to petabyte-scale for low-latency applications as well as high-throughput data processing and analytics. Seamless scaling and replication: Start with a single node per cluster, and seamlessly scale to hundreds of nodes dynamically supporting peak demand. Replication also adds high availability and workload isolation for live serving apps. Simple and integrated: Fully managed service that integrates easily with big data tools like Hadoop, Dataflow, and Dataproc. Plus, support for the open source HBase API standard makes it easy for development teams to get started.

Data can be pushed in KairosDB via multiple protocols like Telnet, Rest and Graphite. Other mechanisms such as plugins can also be used. KairosDB stores time series in Cassandra, the popular and performant NoSQL datastore. The schema consists of 3 column families. This API provides operations to list existing metric names, list tag names and values, store metric data points, and query for metric data points. With a default install, KairosDB serve up a query page whereby you can query data within the data store. It's designed primarily for development purposes. Aggregators perform an operation on data points and down samples. Standard functions like min, max, sum, count, mean and more are available. Import and export is available on the KairosDB server from the command line. Internal metrics to the data store can monitor the server’s performance.

The beauty of the Canary Historian is that the same solution works as well on site as it does for the entire enterprise. You can log data locally, while sending it to your enterprise historian simultaneously. Best of all, as you grow, so does the solution. A single Canary Historian can log more than two million tags, and multiple Canary Historians can be clustered to handle tens of millions of tags. Enterprise historian solutions can be hosted in your own data centers or in AWS and Azure. And, unlike other enterprise historian solutions, Canary Historians don't require specialized teams of ten and more to maintain them. The Canary Historian is a NoSQL time series database that uses loss-less compression algorithms to provide you the best of both worlds, high-speed performance without requiring data interpolation!

IBM Informix® is a fast and flexible database with the ability to seamlessly integrate SQL, NoSQL/JSON, and time series and spatial data. Its versatility and ease of use make Informix a preferred solution for a wide range of environments, from enterprise data warehouses to individual application development. Also, with its small footprint and self-managing capabilities, Informix is well suited for embedded data-management solutions. IoT data demands robust processing and integration capabilities. Informix offers a hybrid database system with minimal administrative requirements and memory footprint combined with powerful functionality. Key features make Informix ideal for multi-tiered architectures that require processing at the device level, at gateway layers and in the cloud. Native encryption to protect data at rest and in motion. Support for flexible schema, multiple APIs and configurations.

Telegraf is the open source server agent to help you collect metrics from your stacks, sensors and systems. Telegraf is a plugin-driven server agent for collecting and sending metrics and events from databases, systems, and IoT sensors. Telegraf is written in Go and compiles into a single binary with no external dependencies, and requires a very minimal memory footprint. Telegraf can collect metrics from a wide array of inputs and write them into a wide array of outputs. It is plugin-driven for both collection and output of data so it is easily extendable. It is written in Go, which means that it is a compiled and standalone binary that can be executed on any system with no need for external dependencies, no npm, pip, gem, or other package management tools required. With 300+ plugins already written by subject matter experts on the data in the community, it is easy to start collecting metrics from your end-points.

Cortex is an open source project that adds horizontal scalability. While Prometheus can scale up to 1 million samples/sec on a single machine, with Cortex horizontal scalability is practically limitless. In a constantly changing environment, you need alternative approaches to monitoring individual VMs or servers. Prometheus' service-discovery driven pull-based metrics system was designed for the dynamic nature of microservices. It lets you easily monitor your whole environment no matter how many moving parts. Instrument your application to create custom metrics using standard Prometheus client libraries, or take advantage of the extensive collection of Prometheus Exporters that collect data from existing applications like MySQL, Redis, Java, ElasticSearch and many more.

Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts. Companies today use everything from simple spreadsheets to complex financial planning software to attempt to accurately forecast future business outcomes such as product demand, resource needs, or financial performance. These tools build forecasts by looking at a historical series of data, which is called time series data. For example, such tools may try to predict the future sales of a raincoat by looking only at its previous sales data with the underlying assumption that the future is determined by the past. This approach can struggle to produce accurate forecasts for large sets of data that have irregular trends. Also, it fails to easily combine data series that change over time (such as price, discounts, web traffic, and number of employees) with relevant independent variables like product features and store locations.

From transactions to meetings, every event happens at a point or duration in time. Successful investigations need to understand how those events unfold, and how they’re linked. KronoGraph is the first toolkit for scalable timeline visualizations that reveal patterns in time data. Build interactive timeline tools to explore how relationships and events evolve. Whether you need to investigate phone calls between two people or IT traffic across a whole enterprise network, KronoGraph provides a rich, interactive view of the data. Transition smoothly from an aggregated high-level summary to individual events, powering investigations as they grow. Investigations often rely on identifying specific points of interest a person, an event, a connection. With KronoGraph’s interactive view you can scroll through time, uncover anomalies and patterns and zoom into individual entities that reveal the hidden story in your data.

A Revenue Operations Platform that accelerates revenue results. Automated CRM updates? Check. Time series analysis? Check. But Clari is much more than innovative features. By combining revenue intelligence with forecasting and execution insights, Clari solves your real problem—efficiently and predictably hitting your targets, quarter after quarter, year after year. Purpose-built to drive more predictable revenue, Clari’s Revenue Operations Platform takes previously untapped data—from email, CRM, call logs and beyond—and turns it into execution insights for your entire revenue team. Clari backs up human intuition with AI insights, so your team can forecast with newfound accuracy and foresight—using a consistent, automated process that flexes to manage every business in your company. Harvest valuable activity data from reps, prospects and customers so you always know what’s going on in your deals, your teams, and in your business.

A NEW WAY TO BUY ENERGY AND RAW MATERIALS Datapred is an integrated online software for energy and raw material buyers, helping them with reporting and market awareness, and providing powerful decision support. Connections to external and internal data sources, as well as powerful analysis, forecasting and optimization models, ensure that buying decisions are consistent with both market and operational conditions. Datapred is used by industrial companies and energy advisors.

TrendMiner is a fast, powerful and intuitive advanced industrial analytics platform designed for real-time monitoring and troubleshooting of industrial processes. It provides robust data collection, analysis, and visualization enabling everyone in industrial operations for making smarter data-driven decisions efficiently to accelerate innovation, optimization, and sustainable growth. TrendMiner, a Proemion company, is founded in 2008 with our global headquarters located in Belgium, and offices in the U.S., Germany, Spain and the Netherlands. TrendMiner has strategic partnerships with all major players such as Amazon, Microsoft, SAP, GE Digital, Siemens and Aveva, and offers standard integrations with a wide range of historians such as OSIsoft PI, Yokogawa Exaquantum, AspenTech IP.21, Honeywell PHD, GE Proficy Historian and Wonderware InSQL.

JaguarDB enables fast ingestion of time series data, coupling location-based data. It also can index in both dimensions, space and time. Back-filling time series data is also fast (inserting large volumes of data in past time). Normally time series is a series of data points indexed in time order. In JaguarDB, the time series has a different meaning: it is both a sequence of data points and a series of tick tables holding aggregated data values at specified time spans. For example, a time series table in JaguarDB can have a base table storing data points in time order, and tick tables such as 5 minute, 15 minute, hourly, daily, weekly, monthly tables to store aggregated data within these time spans. The format for the RETENTION is the same as the TICK format, except that it can have any number of retention periods. The RETENTION specifies how long the data points in the base table should be kept.

Power your metrics and alerting with a leading open-source monitoring solution. Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions. Besides stored time series, Prometheus may generate temporary derived time series as the result of queries. Prometheus provides a functional query language called PromQL (Prometheus Query Language) that lets the user select and aggregate time series data in real time. The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus's expression browser, or consumed by external systems via the HTTP API. Prometheus is configured via command-line flags and a configuration file. While the command-line flags configure immutable system parameters (such as storage locations, amount of data to keep on disk and in memory, etc.). Download: https://sourceforge.net/projects/prometheus.mirror/

SiriDB is designed with performance in mind, inserts and queries are answered in a blink of an eye. The custom query language gives you the ability to speed up your development. SiriDB is scalable on the fly and has no downtime while updating or expanding your database. The scalable possibilities enable you to enlarge the database time after time without losing speed. We take full leverage of all available resources as we distribute your time series data over all pools. SiriDB is developed to give an unprecedented performance without downtime. A SiriDB cluster distributes time series across multiple pools. Each pool supports active replicas for load balancing and redundancy. When one of the replicas is not available the database is still accessible.

Real-Time Analytics on Raw Data. Live ingest from S3, Kafka, DynamoDB & more. Explore raw data as SQL tables. Build amazing data-driven applications & live dashboards in minutes. Rockset is a serverless search and analytics engine that powers real-time apps and live dashboards. Operate directly on raw data, including JSON, XML, CSV, Parquet, XLSX or PDF. Plug data from real-time streams, data lakes, databases, and data warehouses into Rockset. Ingest real-time data without building pipelines. Rockset continuously syncs new data as it lands in your data sources without the need for a fixed schema. Use familiar SQL, including joins, filters, and aggregations. It’s blazing fast, as Rockset automatically indexes all fields in your data. Serve fast queries that power the apps, microservices, live dashboards, and data science notebooks you build. Scale without worrying about servers, shards, or pagers.

It's performance, superior features and unmatched functionality have led OneTick Database to be embraced by leading banks, brokerages, data vendors, exchanges, hedge funds, market makers and mutual funds. OneTick is the premier enterprise-wide solution for tick data capture, streaming analytics, data management and research. With its superior features and unmatched functionality, OneTick is being embraced enthusiastically by leading hedge funds, mutual funds, banks, brokerages, market makers, data vendors and exchanges. OneTick’s proprietary time series database is a unified, multi-asset class platform that includes a fully integrated streaming analytics engine and built-in business logic to eliminate the need for multiple disparate systems. The system provides the lowest total cost of ownership available.

Circonus IRONdb makes it easy to handle and store unlimited volumes of telemetry data, easily handling billions of metric streams. Circonus IRONdb enables users to identify areas of opportunity and challenge in real time, providing forensic, predictive, and automated analytics capabilities that no other product can match. Rely on machine learning to automatically set a “new normal” as your data and operations dynamically change. Circonus IRONdb integrates with Grafana, which has native support for our analytics query language. We are also compatible with other visualization apps, such as Graphite-web. Circonus IRONdb keeps your data safe by storing multiple copies of your data in a cluster of IRONdb nodes. System administrators typically manage clustering, often spending significant time maintaining it and keeping it working. Circonus IRONdb allows operators to set and forget their cluster, and stop wasting resources manually managing their time series data store.

Redis Labs: home of Redis. Redis Enterprise is the best version of Redis. Go beyond cache; try Redis Enterprise free in the cloud using NoSQL & data caching with the world’s fastest in-memory database. Run Redis at scale, enterprise grade resiliency, massive scalability, ease of management, and operational simplicity. DevOps love Redis in the Cloud. Developers can access enhanced data structures, a variety of modules, and rapid innovation with faster time to market. CIOs love the confidence of working with 99.999% uptime best in class security and expert support from the creators of Redis. Implement relational databases, active-active, geo-distribution, built in conflict distribution for simple and complex data types, & reads/writes in multiple geo regions to the same data set. Redis Enterprise offers flexible deployment options, cloud on-prem, & hybrid. Redis Labs: home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.

Blueflood is a high throughput, low latency, multi-tenant distributed metric processing system behind Rackspace Metrics, which is currently used in production by the Rackspace Monitoring team and Rackspace public cloud team to store metrics generated by their systems. In addition to Rackspace metrics, other large scale deployments of Blueflood can be found at community Wiki. Data from Blueflood can be used to construct dashboards, generate reports, graphs or for any other use involving time-series data. It focuses on near-realtime data, with data that is queryable mere milliseconds after ingestion. You send metrics to the ingestion service. You query your metrics from the Query service. And in the background, rollups are batch-processed offline so that queries for large time-periods are returned quickly.

How is platform independent eXtremeDB different? - Hybrid data storage. Unlike other IMDS, eXtremeDB can be all-in-memory, all-persistent, or have a mix of in-memory tables and persistent tables - Active Replication Fabric™ is unique to eXtremeDB, offering bidirectional replication, multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more - Row & Columnar Flexibility for Time Series Data supports database designs that combine row-based and column-based layouts, in order to best leverage the CPU cache speed - Embedded and Client/Server. Fast, flexible eXtremeDB is data management wherever you need it, and can be deployed as an embedded database system, and/or as a client/server database system -A hard real-time deterministic option in eXtremeDB/rt Designed for use in resource-constrained, mission-critical embedded systems. Found in everything from routers to satellites to trains to stock markets worldwide

The Open, Multi-Cloud Stack for Modern Data Apps. Built on open-source Apache Cassandra™. Global-scale and 100% uptime without vendor lock-in. Deploy on multi-cloud, on-prem, open-source, and Kubernetes. Elastic and pay-as-you-go for improved TCO. Start building faster with Stargate APIs for NoSQL, real-time, reactive, JSON, REST, and GraphQL. Skip the complexity of multiple OSS projects and APIs that don’t scale. Ideal for commerce, mobile, AI/ML, IoT, microservices, social, gaming, and richly interactive applications that must scale-up and scale-down with demand. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Use REST, GraphQL, JSON with your favorite full-stack framework Richly interactive apps that are elastic and viral-ready from Day 1. Pay-as-you-go Apache Cassandra DBaaS that scales effortlessly and affordably.

Parallel query engine with time- and symbol-indexed data access. Extended SQL syntax with advanced filtering and aggregations. Consolidate quotes, trades, snapshots, and reference data in one place. Strategy backtesting on high-frequency data. Quantitative and market microstructure research. Granular transaction cost analysis and rollup reporting. Market surveillance and anomaly detection. Non-transparent ETF/ETN decomposition. FAST, SBE, and proprietary protocols. Plain text protocol. Consolidated and direct feeds. Built-in latency monitoring tools. End-of-day archives. ETL from institutional and retail financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering by trading session, auction stage, index composition. Optimized aggregates for OHLCV and VWAP calculations. Interactive SQL console with auto-completion. API endpoint for programmatic integration. Scheduled SQL reporting with email, file, and web delivery. JDBC and ODBC drivers.

VictoriaMetrics Cloud allows users to run the Enterprise version of VictoriaMetrics, hosted on AWS, without the need to perform typical DevOps tasks such as proper configuration, monitoring, log collection, access protection, software updates, and backups. We run VictoriaMetrics Cloud instances in our environment on AWS and provide easy-to-use endpoints for data ingestion and querying. The VictoriaMetrics team takes care of optimal configuration and software maintenance. It comes with the following features: It can be used as a Managed Prometheus - configure Prometheus or Vmagent to write data to Managed VictoriaMetrics and then use the provided endpoint as a Prometheus data source in Grafana; Every VictoriaMetrics Cloud instance runs in an isolated environment, so instances cannot interfere with each other; VictoriaMetrics Cloud instance can be scaled up or scaled down in a few clicks; Automated backups;

Quasar's brain is QuasarDB, a high-performance, distributed, column-oriented timeseries database management system designed from the ground up to deliver real-time on petascale use cases. Up to 20X less disk usage. Quasardb ingestion and compression capabilities are unmatched. Up to 10,000X faster feature extraction. QuasarDB can extract features in real-time from the raw data, thanks to the combination of a built-in map/reduce query engine, an aggregation engine that leverages SIMD from modern CPUs, and stochastic indexes that use virtually no disk space. The most cost-effective timeseries solution, thanks to its ultra-efficient resource usage, the capability to leverage object storage (S3), unique compression technology, and fair pricing model. Quasar runs everywhere, from 32-bit ARM devices to high-end Intel servers, from Edge Computing to the cloud or on-premises.

Odyx yHat is a Time Series Forecasting tool designed to simplify the intricate field of data science, making it accessible and user-friendly for individuals without any background in data science.

Our highly innovative machine learning technology is designed specifically to accurately predict financial time series where only a small number of training data points are available. Advance AI is computationally consuming. YottamineAI leverages the cloud to eliminate the need to invest time and money on managing hardware, shortening the time to benefit from higher ROI significantly. Strong encryption and protection of keys ensure trade secrets stay safe. We follow the best practices of AWS and utilize strong encryption to secure your data. We evaluate how your existing or future data can generate predictive analytics in helping you make information-based decisions. If you need predictive analytics on a project basis, Yottamine Consulting Services provides project-based consulting to accommodate your data-mining needs.