Alternatives to Circonus IRONdb
Compare Circonus IRONdb alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Circonus IRONdb in 2026. Compare features, ratings, user reviews, pricing, and more from Circonus IRONdb competitors and alternatives in order to make an informed decision for your business.
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Grafana Cloud
Grafana Labs
Grafana Labs delivers the leading AI-powered observability platform, built around Grafana—the world’s most widely adopted open source technology for dashboards and visualization. Recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Observability Platforms, Grafana Labs supports more than 25 million users and thousands of organizations, from startups to the Fortune 500. Grafana Cloud is the open observability cloud, built on open source, open standards, and open ecosystems. Powered by the LGTM stack—Grafana (visualization), Mimir (metrics), Loki (logs) & Tempo (traces)—it unifies telemetry in one platform for full-stack visibility across applications, infrastructure, and digital experiences. With the AI-powered Grafana Assistant and Adaptive Telemetry suite, teams detect and resolve issues faster, reduce wasteful telemetry spend, and gain real-time insights to ensure reliability. Native OTel support and 100s of integrations mean you can plug in existing tools & data sources. -
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RaimaDB
Raima
RaimaDB is an embedded time series database for IoT and Edge devices that can run in-memory. It is an extremely powerful, lightweight and secure RDBMS. Field tested by over 20 000 developers worldwide and has more than 25 000 000 deployments. RaimaDB is a high-performance, cross-platform embedded database designed for mission-critical applications, particularly in the Internet of Things (IoT) and edge computing markets. It offers a small footprint, making it suitable for resource-constrained environments, and supports both in-memory and persistent storage configurations. RaimaDB provides developers with multiple data modeling options, including traditional relational models and direct relationships through network model sets. It ensures data integrity with ACID-compliant transactions and supports various indexing methods such as B+Tree, Hash Table, R-Tree, and AVL-Tree. -
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Riak TS
Riak
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.Starting Price: $0 -
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Machbase
Machbase
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. -
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KairosDB
KairosDB
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. -
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kdb Insights
KX
kdb Insights is a cloud-native, high-performance analytics platform designed for real-time analysis of both streaming and historical data. It enables intelligent decision-making regardless of data volume or velocity, offering unmatched price and performance, and delivering analytics up to 100 times faster at 10% of the cost compared to other solutions. The platform supports interactive data visualization through real-time dashboards, facilitating instantaneous insights and decision-making. It also integrates machine learning models to predict, cluster, detect patterns, and score structured data, enhancing AI capabilities on time-series datasets. With supreme scalability, kdb Insights handles extensive real-time and historical data, proven at volumes of up to 110 terabytes per day. Its quick setup and simple data intake accelerate time-to-value, while native support for q, SQL, and Python, along with compatibility with other languages via RESTful APIs. -
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Google Cloud Bigtable
Google
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. -
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Heroic
Heroic
Heroic is an open-source monitoring system originally built at Spotify to address problems faced with large scale gathering and near real-time analysis of metrics. Heroic uses a small set of components which are responsible for very specific things. Indefinite retention, as long as you have the hardware spend. Federation support to connect multiple Heroic clusters into a global interface. Heroic uses a small set of components which are responsible for very specific things. Consumers are the component responsible for consuming metrics. When building Heroic it was quickly realized that navigating hundreds of millions of time series without context is hard. Heroic has support for federating requests, which allows multiple independent Heroic clusters to serve clients through a single global interface. This can be used to reduce the amount of geographical traffic by allowing one cluster to operate completely isolated within its zone. -
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Canary Historian
Canary
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!Starting Price: $9,970 one-time payment -
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VictoriaMetrics
VictoriaMetrics
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.Starting Price: $0 -
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ArcadeDB
ArcadeDB
ArcadeDB is an open-source, next-generation multi-model database. Forget Polyglot Persistence — store graphs, documents, key-value pairs, search engine indexes, vectors, and time-series data all in one database with native support for every model. No translation layers, no performance penalties. Process over 10 million records per second. Traversal speed stays constant whether your database has hundreds or billions of records. Query in the language you prefer: SQL, Cypher, Gremlin, GraphQL, MongoDB API, or Java. Deploy ArcadeDB embedded in your JVM application, on a standalone server, or distributed across multiple nodes with Raft Consensus for high availability. Fully ACID-compliant. Super lightweight. Apache 2.0 licensed — free for production and commercial use.Starting Price: Free -
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SiriDB
Cesbit
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. -
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QuestDB
QuestDB
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. -
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JaguarDB
JaguarDB
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. -
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Amazon Timestream
Amazon
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. -
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TimescaleDB
Tiger Data
TimescaleDB is the leading time-series database built on PostgreSQL, designed to handle massive volumes of real-time data efficiently. It enables organizations to store, analyze, and query time-series data — such as IoT sensor data, financial transactions, or event logs — using standard SQL. With hypertables, TimescaleDB automatically partitions data by time and ID for fast ingestion and predictable query performance. Its compression engine reduces storage costs by up to 95%, while continuous aggregates make real-time dashboards instantly responsive. Fully compatible with PostgreSQL, it integrates seamlessly with existing tools and applications. TimescaleDB combines the simplicity of Postgres with the scalability and speed of a specialized analytical system. -
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OpenTSDB
OpenTSDB
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. -
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Altinity
Altinity
Altinity's expert engineering team can implement everything from core ClickHouse features to Kubernetes operator behavior to client library improvements. A flexible docker-based GUI manager for ClickHouse that can do the following: Install ClickHouse clusters; Add, delete, and replace nodes; Monitor cluster status; Help with troubleshooting and diagnostics. 3rd party tools and software integrations: Ingest: Kafka, ClickTail; APIs: Python, Golang, ODBC, Java; Kubernetes; UI tools: Grafana, Superset, Tabix, Graphite; Databases: MySQL, PostgreSQL; BI tools: Tableau and many more. Altinity.Cloud incorporates lessons from helping hundreds of customers operate ClickHouse-based analytics. Altinity.Cloud has a Kubernetes-based architecture that delivers portability and user choice of where to operate. Designed from the beginning to run anywhere without lock-in. Cost management is critical for SaaS businesses. -
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InfluxDB
InfluxData
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.Starting Price: $0 -
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Azure Time Series Insights
Microsoft
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.Starting Price: $36.208 per unit per month -
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VictoriaMetrics Cloud
VictoriaMetrics
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;Starting Price: $190 per month -
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Fauna
Fauna
Fauna is a data API for modern applications that facilitates rich clients with serverless backends by providing a web-native interface with support for GraphQL and custom business logic, frictionless integration with the serverless ecosystem, a no compromise multi-cloud architecture you can trust and grow with and total freedom from database operations. Instantly create multiple databases in one account leveraging multi-tenancy for development or customer-facing use case. Create a distributed database across one geography or the globe in just three clicks and easily import existing data. Scale seamlessly without ever managing servers, clusters, data partitioning, or replication. Track usage and consumption-based billing in near real time via a dashboard.Starting Price: Free -
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Prometheus
Prometheus
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/Starting Price: Free -
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Hawkular Metrics
Hawkular Metrics
Hawkular Metrics is a scalable, asynchronous, multi tenant, long term metrics storage engine that uses Cassandra as the data store and REST as the primary interface. This section provides an overview of some of the key features of Hawkular Metrics. The following sections provide in-depth discussions on these as well as other features. Hawkular Metrics is all about scalability. You can run a single instance backed by a single Cassandra node. You can also scale out Cassandra to multiple nodes to handle increasing loads. The Hawkular Metrics server employs a stateless architecture, which makes it easy to scale out as well. This diagram illustrates the various deployment options made possible with Hawkular Metrics' scalable architecture. The upper left shows the simplest deployment with a single Cassandra node and single Hawkular Metrics node. The bottom right picture shows that it is possible to run more Hawkular Metrics nodes than Cassandra nodes. -
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Warp 10
SenX
Warp 10 is a modular open source platform that collects, stores, and analyzes data from sensors. 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 is both a time series database and a powerful analytics environment, allowing 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 analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system. -
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Tiger Data
Tiger Data
Tiger Data is the creator of TimescaleDB, the world’s leading PostgreSQL-based time-series and analytics database. It provides a modern data platform purpose-built for developers, devices, and AI agents. Designed to extend PostgreSQL beyond traditional limits, Tiger Data offers built-in primitives for time-series data, search, materialization, and scale. With features like auto-partitioning, hybrid storage, and compression, it helps teams query billions of rows in milliseconds while cutting infrastructure costs. Tiger Cloud delivers these capabilities as a fully managed, elastic environment with enterprise-grade security and compliance. Trusted by innovators like Cloudflare, Toyota, Polymarket, and Hugging Face, Tiger Data powers real-time analytics, observability, and intelligent automation across industries.Starting Price: $30 per month -
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StorMagic SvHCI
StorMagic
StorMagic SvHCI is a hyperconverged infrastructure (HCI) solution that incorporates hypervisor, software-defined storage, and virtualized networking into a single software stack. With SvHCI, your organization can virtualize your entire infrastructure without the significant financial commitment required by other solutions on the market. SvHCI provides high availability with a unique cluster architecture of just 2 nodes. Data is synchronously mirrored between the two nodes, meaning an exact copy is always available on either node. If one node goes offline, the StorMagic witness maintains the cluster's health, keeping stores open, production lines moving and services running until the failed node is restored. A single StorMagic witness located anywhere in the world can service 1000 StorMagic clusters simultaneously. -
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GridDB
GridDB
GridDB uses multicast communication to constitute a cluster. Set the network to enable multicast communication. First, check the host name and an IP address. Execute “hostname -i” command to check the settings of an IP address of the host. If the IP address of the machine is the same as below, no need to perform additional network setting and you can jump to the next section. GridDB is a database that manages a group of data (known as a row) that is made up of a key and multiple values. Besides having a composition of an in-memory database that arranges all the data in the memory, it can also adopt a hybrid composition combining the use of a disk (including SSD as well) and a memory. -
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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.
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Proficy Historian
GE Vernova
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. -
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ITTIA DB
ITTIA
The ITTIA DB product family combines the best of time series, real-time data streaming, and analytics for embedded systems to reduce development time and costs. ITTIA DB IoT is a small-footprint embedded database for real-time resource-constrained 32-bit microcontrollers (MCUs), and ITTIA DB SQL is a high-performance time-series embedded database for single or multicore microprocessors (MPUs). Both ITTIA DB products enable devices to monitor, process, and store real-time data. ITTIA DB products are also built for the automotive industry Electronic Control Units (ECUs). ITTIA DB data security protocols offer data protection against malicious access with encryption, authentication, and DB SEAL. ITTIA SDL is conformant to the principles of IEC/ISO 62443. Embed ITTIA DB to collect, process, and enrich incoming real-time data streams in a purpose-built SDK for edge devices. Search, filter, join, and aggregate at the edge. -
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CrateDB
CrateDB
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. -
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Blueflood
Blueflood
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. -
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kdb+
KX Systems
A high-performance cross-platform historical time-series columnar database featuring: - An in-memory compute engine - A real-time streaming processor - An expressive query and programming language called q kdb+ powers kdb Insights portfolio and KDB.AI, together delivering time-oriented data insights and generative AI capabilities to the world’s leading enterprise organizations. Independently benchmarked* as the fastest in-memory, columnar analytics database available, kdb+ delivers unmatched value to businesses operating in the toughest data environments. kdb+ improves decision-making processes to help navigate rapidly changing data landscapes. -
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Chronosphere
Chronosphere
Purpose built for cloud-native’s unique monitoring challenges. Built from day one to handle the outsized volume of monitoring data produced by cloud-native applications. Offered as a single centralized service for business owners, application developers and infrastructure engineers to debug issues throughout the stack. Tailored for each use case from sub-second data for continuous deployments to one hour data for capacity planning. One-click deployment with support for Prometheus and StatsD ingestion protocols. Storage and index for both Prometheus and Graphite data types in the same solution. Embedded Grafana compatible dashboards with full support for PromQL and Graphite. Dependable alerting engine with integration for PagerDuty, Slack, OpsGenie and webhooks. Ingest and query billions of metric data points per second. Trigger alerts, pull up dashboards and detect issues within a second. Keep three consistent copies of your data across failure domains. -
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Apache Helix
Apache Software Foundation
Apache Helix is a generic cluster management framework used for the automatic management of partitioned, replicated and distributed resources hosted on a cluster of nodes. Helix automates reassignment of resources in the face of node failure and recovery, cluster expansion, and reconfiguration. To understand Helix, you first need to understand cluster management. A distributed system typically runs on multiple nodes for the following reasons: scalability, fault tolerance, load balancing. Each node performs one or more of the primary functions of the cluster, such as storing and serving data, producing and consuming data streams, and so on. Once configured for your system, Helix acts as the global brain for the system. It is designed to make decisions that cannot be made in isolation. While it is possible to integrate these functions into the distributed system, it complicates the code. -
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NumXL
SPIDER FINANCIAL CORP
NumXL is a suite of time series Excel add-ins. It transforms your Microsoft Excel application into a first-class time series software and econometrics tool, offering the kind of statistical accuracy provided by far more expensive statistical packages. NumXL integrates natively with Excel, adding scores of econometric functions, a rich set of shortcuts, and intuitive user interfaces to guide you through the entire process. (1) Summary Statistics - Gini, Hurst, KDE, etc. (2) Statistical Testing - Normality, Stationarity, cointegration, etc. (3) Brown's, Holt's & Winter's exponential smoothing (4) ARMA/ARIMA/SARIMA & X12ARIMA (5) ARMAX/SARIMA-X (6) GARCH, E-GARCH & GARCH-MStarting Price: $25/user/month -
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OneTick
OneMarketData
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. -
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Apache Druid
Druid
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. -
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Alibaba Cloud TSDB
Alibaba
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. -
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Microsoft Storage Spaces
Microsoft
Storage Spaces is a technology in Windows and Windows Server that can help protect your data from drive failures. It is conceptually similar to RAID, implemented in software. You can use Storage Spaces to group three or more drives together into a storage pool and then use capacity from that pool to create Storage Spaces. These typically store extra copies of your data so if one of your drives fails, you still have an intact copy of your data. If you run low on capacity, just add more drives to the storage pool. There are four major ways to use Storage Spaces, on a Windows PC, on a stand-alone server with all storage in a single server, on a clustered server using Storage Spaces Direct with local, direct-attached storage in each cluster node, and on a clustered server with one or more shared SAS storage enclosures holding all drives. Expand volumes on Azure Stack HCI and Windows Server clusters. -
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IBM Analytics Engine provides an architecture for Hadoop clusters that decouples the compute and storage tiers. Instead of a permanent cluster formed of dual-purpose nodes, the Analytics Engine allows users to store data in an object storage layer such as IBM Cloud Object Storage and spins up clusters of computing notes when needed. Separating compute from storage helps to transform the flexibility, scalability and maintainability of big data analytics platforms. Build on an ODPi compliant stack with pioneering data science tools with the broader Apache Hadoop and Apache Spark ecosystem. Define clusters based on your application's requirements. Choose the appropriate software pack, version, and size of the cluster. Use as long as required and delete as soon as an application finishes jobs. Configure clusters with third-party analytics libraries and packages. Deploy workloads from IBM Cloud services like machine learning.Starting Price: $0.014 per hour
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BangDB
BangDB
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.Starting Price: $2,499 per year -
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Amazon FinSpace
Amazon
Amazon FinSpace simplifies running kdb Insights applications on AWS. Amazon FinSpace automates the undifferentiated tasks required to provision, integrate, and secure infrastructure for kdb Insights. In addition, Amazon FinSpace provides easy-to-use APIs so customers can configure and run new kdb Insights applications in just a few minutes. Amazon FinSpace gives customers the flexibility required to move existing kdb Insights applications to AWS and get the benefits of the cloud while eliminating the complex and costly work of self-managing the infrastructure. KX's kdb Insights is a high-performance analytics engine that is optimized for the analysis of real-time and multi-petabyte historical time-series data. Kdb Insights is commonly used by Capital Markets customers to power business-critical workloads, such as options pricing, transaction cost analysis, and backtesting. Eliminate the work to integrate more than 15 AWS services to deploy kdb. -
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StorMagic SvSAN
StorMagic
StorMagic SvSAN is simple storage virtualization. It provides high availability with two nodes per cluster, and boasts users among thousands of organizations to keep mission-critical applications and data online and available 24 hours a day, 365 days a year. SvSAN is a lightweight solution that has been designed specifically for small-to-medium-sized businesses and edge computing environments such as retail stores, manufacturing plants and even oil rigs at sea. SvSAN is a simple, 'set and forget' solution that enables lightweight high availability as a virtual SAN (VSAN) with a witness VM that can be local, in the cloud, or as-a-service, and support up to 1,000 2-node clusters. It gives organizations choice and control by allowing configurations of any x86 servers and storage types, even mixed within a cluster. Plus, SvSAN eliminates downtime with synchronous mirroring and no single point of failure, and non-disruptive hardware and software upgrades -
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eXtremeDB
McObject
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 -
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IBM Db2 Event Store is a cloud-native database system that is designed to handle massive amounts of structured data that is stored in Apache Parquet format. Because it is optimized for event-driven data processing and analysis, this high-speed data store can capture, analyze, and store more than 250 billion events per day. The data store is flexible and scalable to adapt quickly to your changing business needs. With the Db2 Event Store service, you can create these data stores in your Cloud Pak for Data cluster so that you can govern the data and use it for more in-depth analysis. You need to rapidly ingest large amounts of streaming data (up to one million inserts per second per node) and use it for real-time analytics with integrated machine learning capabilities. Analyze incoming data from different medical devices in real time to provide better health outcomes for patients while providing cost savings for moving the data to storage.
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IBM Informix
IBM
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. -
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etcd
etcd
etcd is a strongly consistent, distributed key-value store that provides a reliable way to store data that needs to be accessed by a distributed system or cluster of machines. It gracefully handles leader elections during network partitions and can tolerate machine failure, even in the leader node. Store data in hierarchically organized directories, as in a standard filesystem. Watch specific keys or directories for changes and react to changes in values. -
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NetApp MetroCluster
NetApp
NetApp MetroCluster configurations implement two physically separated, mirrored ONTAP clusters that operate in concert to deliver continuous data and SVM protection. Each cluster synchronously replicates its data aggregates to its partner to maintain identical copies mirrored across both sites. In the event of a site failure, administrators can activate the mirrored SVM on the surviving cluster and resume data serving seamlessly. MetroCluster supports both fabric-attached (FC) and IP-based cluster setups: fabric-attached MetroCluster uses FC transport for SyncMirror between sites, while MetroCluster IP leverages layer‑2 stretched IP networks. Stretch MetroCluster deployments enable campus-wide coverage, MetroCluster IP supports configurations up to four nodes with NVMe/FC or NVMe/TCP starting in ONTAP 9.12.1/9.15.1, and front-end SAN protocols like FC, FCoE, and iSCSI are all supported.
