Machbase Alternatives

Raima Database Manager 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.

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. • Gain critical real-time insights to run your business • Seamlessly integrate data streaming and batch data • High performance in throughput and low-latency at petabyte scale • Fully-managed cloud service • Tiered storage to optimize cloud performance & spend • Fully-secure & enterprise-ready

RavenDB is the pioneer NoSQL Document Database that is fully transactional (ACID) across your database and throughout your cluster. At a fraction of the total cost of ownership (TCO), our open source distributed database offers high availability and high performance with zero administration. It is designed as an easy to use all-in-one database which minimizes the need for third party addons, tools, or support to boost developer productivity and get your project into production fast. You can setup and secure a data cluster in minutes and deploy in the cloud, on-premise or in a hybrid environment. RavenDB offers a Database as a Service solution, allowing you to pass on all your database operations to us so you can focus exclusively on your application. RavenDB has a built-in storage engine, Voron, that operates at speeds up to 1 million reads per second and 150,000 writes per second on a single node using simple commodity hardware to increase your application’s performance.

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.

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

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.

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.

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/

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

A streaming database is purpose-built to ingest, store, process, and analyze massive data streams. It is a modern data infrastructure that unifies messaging, stream processing, and storage to help get value out of your data in real-time. Ingest massive amounts of data continuously generated from various sources, such as IoT device sensors. Store millions of data streams reliably in a specially designed distributed streaming data storage cluster. Consume data streams in real-time as fast as from Kafka by subscribing to topics in HStreamDB. With the permanent data stream storage, you can playback and consume data streams anytime. Process data streams based on event-time with the same familiar SQL syntax you use to query data in a relational database. You can use SQL to filter, transform, aggregate, and even join multiple data streams.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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-M

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.

Build high-speed, data-intensive applications that elastically meet performance requirements at any scale. Take advantage of Apache Geode's unique technology that blends advanced techniques for data replication, partitioning and distributed processing. Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing. Data can easily be partitioned (sharded) or replicated between nodes allowing performance to scale as needed. Durability is ensured through redundant in-memory copies and disk-based persistence. Super fast write-ahead-logging (WAL) persistence with a shared-nothing architecture that is optimized for fast parallel recovery of nodes or an entire cluster.

Focus on developing data stream processing applications and don’t waste time maintaining the infrastructure. Managed Service for Apache Kafka is responsible for managing Zookeeper brokers and clusters, configuring clusters, and updating their versions. Distribute your cluster brokers across different availability zones and set the replication factor to ensure the desired level of fault tolerance. The service analyzes the metrics and status of the cluster and automatically replaces it if one of the nodes fails. For each topic, you can set the replication factor, log cleanup policy, compression type, and maximum number of messages to make better use of computing, network, and disk resources. You can add brokers to your cluster with just a click of a button to improve its performance, or change the class of high-availability hosts without stopping them or losing any data.

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.

The superfast nosql database for mobile and iot with integrated data synchronization. High-performance Objectbox is 10x faster than any alternative, improving response rates and enabling real-time applications. Check out our benchmarks. From sensor to server and everything in between. We support linux, windows, mac/ios, android, raspbian, etc. Embedded or containerized. Sync data seamlessly. Objectbox’ out-of-the-box synchronization makes data available when needed where needed, so you can take your app live faster. Offline first Develop applications that work on- and offline, independently from a constant internet connection, providing an “always-on”-feeling. Save time & dev. resources. Accelerate time-to-market, save development and lifecycle costs, save precious developer time for tasks that bring value, and let objectbox deal with the risk. Objectbox reduces cloud costs up to 60% by persisting data locally (on the edge), and syncing necessary data quicker and more efficiently.

PipelineDB is a PostgreSQL extension for high-performance time-series aggregation, designed to power realtime reporting and analytics applications. PipelineDB allows you to define continuous SQL queries that perpetually aggregate time-series data and store only the aggregate output in regular, queryable tables. You can think of this concept as extremely high-throughput, incrementally updated materialized views that never need to be manually refreshed. Raw time-series data is never written to disk, making PipelineDB extremely efficient for aggregation workloads. Continuous queries produce their own output streams, and thus can be chained together into arbitrary networks of continuous SQL.

Our intelligent document handling platform brings automated extraction, redaction, classification, and indexing to companies of all industries. Extract’s document handling platform reads your incoming unstructured documents. Our customizable platform intelligently extracts or redacts the information you need and routes your data and the original document to their final destination. Our platform runs your source documents through an Optical Character Recognition (OCR) software and rules that have been written by us, specifically for your company's needs. The Extract Systems Platform begins to extract or redact the information you need. With our intelligent software, we are then able to send the data and original document to any final destination you choose. This process not only reduces the time spent on manual entry, but also reduces human error typically caused by manual data entry and speeds up access to valuable discrete data so you can share, compare, report, and analyze the data.

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.

Instaclustr is the Open Source-as-a-Service company, delivering reliability at scale. We operate an automated, proven, and trusted managed environment, providing database, analytics, search, and messaging. We enable companies to focus internal development and operational resources on building cutting edge customer-facing applications. Instaclustr works with cloud providers including AWS, Heroku, Azure, IBM Cloud, and Google Cloud Platform. The company has SOC 2 certification and provides 24/7 customer support.