Alternatives to JaguarDB

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

Amazon DynamoDB, a key-value and document databank, delivers single-digit millisecond performance on any scale. It is a fully managed, multiregional, multimaster, durable database that offers built-in security, backup, restore, and in-memory cache for internet-scale apps. DynamoDB can process more than 10 trillion requests per hour and can handle peak requests of more than 20,000,000 requests per second. Many of the fastest-growing businesses in the world, such as Lyft, Redfin, and Airbnb, as well as enterprises like Samsung, Toyota and Capital One, rely on DynamoDB's scale and performance to support mission-critical workloads.

Azure Table storage can store petabytes semi-structured data at low costs and keeps costs down. Table storage is able to scale up, unlike many cloud-based or on-premise data stores. Also, availability is not a concern. With geo-redundant storage, data can be replicated three times within one region and three times in another region hundreds of miles away. Flexible data such as web app user data, address books, device data and other metadata can be stored in table storage. You can also use table storage to build cloud applications without having to lock down the data model to specific schemas. Different rows can have different structures in the same table, so you can easily change your application and table schema without having to take it offline. Table storage embraces a strong consistency model.

BangDB integrates AI, streaming and graph analytics within its DB to allow users to deal complex data of all types, such as text, images and objects. Real-time data processing and analysis Many types of data are required to be ingested and processed simultaneously for today's use cases. BangDB supports almost all the data formats that are useful to users to solve their problem quickly. The rise of real-time data allows for real-time streaming and predictive analytics to optimize business operations.

Redis Labs is the home of Redis. Redis Enterprise is the best Redis version. Redis Enterprise is more than a cache. Redis Enterprise can be free in the cloud with NoSQL and data caching using the fastest in-memory database. Redis can be scaled, enterprise-grade resilience, massive scaling, ease of administration, and operational simplicity. Redis in the Cloud is a favorite of DevOps. Developers have access to enhanced data structures and a variety modules. This allows them to innovate faster and has a faster time-to-market. CIOs love the security and expert support of Redis, which provides 99.999% uptime. Use relational databases for active-active, geodistribution, conflict distribution, reads/writes in multiple regions to the same data set. Redis Enterprise offers flexible deployment options. Redis Labs is the home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.

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

LeanXcale is fast and scalable database that combines SQL and NoSQL. It can ingest large batches of data and make it available via SQL or GIS for any purpose, including operational applications, analytics and dashboarding. No matter which stack you use, LeanXcale offers both SQL and NoSQL interfaces. The KiVi storage engine can be used as a relational key/value data store. The data can be accessed via the standard SQL API or a direct ACID key/value interface. This key-value interface allows users data ingestion at extremely high rates and efficiently, while avoiding SQL processing overhead. High-scalable, efficient, and distributed storage engine distributed data along a cluster to improve performance and increase reliability.

The Open, Multi-Cloud Stack to Modern Data Apps. Built on Apache Cassandra™, an open-source Apache Cassandra™. Global scale and 100% uptime without vendor lock in You can deploy on multi-clouds, open-source, on-prem and Kubernetes. For a lower TCO, use elastic and pay-as you-go. Stargate APIs allow you to build faster with NoSQL, reactive, JSON and REST. Avoid the complexity of multiple OSS projects or APIs that don’t scale. It is ideal for commerce, mobile and AI/ML. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Richly interactive apps that are viral-ready and elastic using REST, GraphQL and JSON. Pay-as you-go Apache Cassandra DBaaS which scales easily and affordably

Azure Cosmos DB, a fully managed NoSQL databank service, is designed for modern app development. It offers guaranteed single-digit millisecond response time and 99.999 percent availability. This service is backed by SLAs and instant scalability. Open source APIs for MongoDB or Cassandra are also available. With turnkey multi-master global distribution, you can enjoy fast writes and readings from anywhere in the world.

ArcadeDB allows you to manage complex models without any compromises. Polyglot Persistence is gone. There is no need to have multiple databases. ArcadeDB Multi-Model databases can store graphs and documents, key values, time series, and key values. Each model is native to the database engine so you don't need to worry about translations slowing down your computer. ArcadeDB's engine was developed with Alien Technology. It can crunch millions upon millions of records per second. ArcadeDB's traversing speed does not depend on the size of the database. It doesn't matter if your database contains a few records or a billion. ArcadeDB can be used as an embedded database on a single server. It can scale up by using Kubernetes to connect multiple servers. It is flexible enough to run on any platform that has a small footprint. Your data is protected. Our unbreakable fully transactional engine ensures durability for mission-critical production database databases. ArcadeDB uses the Raft Consensus Algorithm in order to maintain consistency across multiple servers.

Riak is a distributed systems expert and works with Application teams to overcome distributed system challenges. Riak's Riak®, a distributed NoSQL databank, delivers: Unmatched resilience beyond the typical "high availability" offerings - Innovative technology to ensure data accuracy, and never lose a word. - Massive scale for commodity hardware - A common code foundation that supports true multi-model support Riak®, offers all of this while still focusing on ease-of-use. Choose Riak®, KV flexible key value data model for web scale profile management, session management, real time big data, catalog content management, customer 360, digital message and other use cases. Choose Riak®, TS for IoT, time series and other use cases.

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

Tablestore allows seamless expansion of data size, access concurrency, and data sharding technologies. It provides storage and real-time access of massive structured data. Three copies of data with high consistency and full host, high availability, data high reliability, and service high availability. Provides full/incremental data tunnels that seamlessly connect with other products for big-data analysis and real time stream computing. Distributed architecture, single-table auto scaling, support for 10-PB-level data, and 10-million-level access concurrency. Multi-level and multi-level security protection, as well as resource access management, are available to ensure data security. This service's low latency, high concurrency and elastic resources, as well as the Pay-As You-Go billing method, allow your risk control system, which allows you to control transaction risks.

Amazon Timestream is a fast, scalable and serverless time series data service for IoT/operational applications. It makes it possible to store and analyze trillions per day up to 1000 times faster than traditional relational databases and at as low as 1/10th of the cost. Amazon Timestream helps you save time and money when managing the lifecycles of time series data. It stores recent data in memory and moves historical data to a cost-optimized storage tier according to user defined policies. Amazon Timestream's purpose-built query tool allows you to access and analyze both recent and historic data simultaneously, without having to specify in the query whether the data is in the in-memory tier or the cost-optimized. Amazon Timestream's built-in time series analytics functions allow you to identify trends and patterns within your data in real-time.

QuestDB is a relational database that uses column-oriented databases. It can be used for event and time series data. It uses SQL with extensions to time series to aid in real-time analytics. These pages provide information about core concepts of QuestDB. They include setup steps, usage guides, as well as reference documentation for syntax, APIs, and configuration. This section explains the architecture of QuestDB and how it stores and queries data. It also introduces new capabilities and features that are unique to the system. The core feature of QuestDB is the designated timestamp. It enables partitioning and time-oriented language capabilities. The symbol type makes it easy to store and retrieve repetitive strings. QuestDB's storage model describes how it stores records and partitions within tables. Indexes can be used to provide faster access to specific columns. Partitions can be used to provide significant performance improvements in calculations and queries. SQL extensions enable time series analysis that is efficient and concise with a concise syntax.

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

OpenTSDB is composed of a Time Series Daemon, (TSD), and a set of command-line utilities. OpenTSDB can only be interacted with by one or more TSDs. Each TSD can be run independently. There is no master or shared state, so you can run as many TSDs you need to handle any load. Each TSD uses the HBase open-source database or hosted Google Bigtable service for time-series data storage and retrieval. The data schema is optimized for fast aggregations and retrieval of similar time series, minimizing storage space. The TSD does not require users to directly access the underlying store. The TSD can be communicated with via a simple telnet protocol, an HTTP API, or a built-in GUI. OpenTSDB's first step is to send time series data directly to the TSDs. OpenTSDB has many tools that allow you to pull data from different sources.

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

KairosDB can push data via multiple protocols: Telnet Rest, Graphite, and Rest. You can also use plugins and other mechanisms. KairosDB stores time series using Cassandra NoSQL datastore. The schema is composed of three column families. This API allows you to list existing metric names, value tags, store metric data, and query for metric points. KairosDB will automatically install the query page. This page allows you to query data stored in the data store. It is intended for development purposes only. Aggregators can perform operations on data points and down samples. You can use standard functions such as min, max, sum and count to calculate the mean, mean, count, mean, and other parameters. The command line allows you to import and export data from the KairosDB server. The server's performance can be monitored internally by the data store.

Apache Druid, an open-source distributed data store, is Apache Druid. Druid's core design blends ideas from data warehouses and timeseries databases to create a high-performance real-time analytics database that can be used for a wide range of purposes. Druid combines key characteristics from each of these systems into its ingestion, storage format, querying, and core architecture. Druid compresses and stores each column separately, so it only needs to read the ones that are needed for a specific query. This allows for fast scans, ranking, groupBys, and groupBys. Druid creates indexes that are inverted for string values to allow for fast search and filter. Connectors out-of-the box for Apache Kafka and HDFS, AWS S3, stream processors, and many more. Druid intelligently divides data based upon time. Time-based queries are much faster than traditional databases. Druid automatically balances servers as you add or remove servers. Fault-tolerant architecture allows for server failures to be avoided.

Apache HBase™, is used when you need random, real-time read/write access for your Big Data. This project aims to host very large tables, billions of rows and X million columns, on top of clusters of commodity hardware.

The ITTIA DB family of products combines time series, real time data streaming and analytics to reduce development costs and time. ITTIA DB IoT, a small embedded database designed for 32-bit microcontrollers with limited resources for real-time data streaming, and ITTIA DB SQL are high-performance embedded databases for time-series for single-core or multicore microprocessors. Both ITTIA DB product enable devices to monitor real-time data, process it, and store it. ITTIA DB also offers products for Electronic Control Units in the automotive industry. ITTIA DB's data security protocols protect data from malicious access through encryption, authentication and DB Seal. ITTIA SDL conforms to the principles of IEC/ISO 62443. ITTIA DB can be embedded in a SDK designed for edge devices to collect, enrich, and process real-time data streams. Search, filter, combine, and aggregate data at the edge.

We provide a geo-distributed, real-time database, stream processing, and compute runtime for event driven applications across up to 175 global edge data centers. Our platform is loved by API and app developers because it solves the most difficult problems of sharing mutable states across hundreds of locations around the world. We also have high consistency and low latency. Macrometa allows you to surgically expand your existing infrastructure to bring your application closer to your users. This allows you to improve performance and user experience, as well as comply with global data governance laws. Macrometa is a streaming, serverless NoSQL database that can be used for stream data processing, pub/sub, and compute engines. You can create stateful data infrastructure, stateful function & containers for long-running workloads, and process data streams real time. We do the ops and orchestration, you write the code.

Ledisdb, a high-performance NoSQL server and database library written in Go, is Ledisdb. It is similar to Redis, but stores data on disk. It supports many data structures, including kv and list, hash, set, zset, and zset. LedisDB now supports multiple databases as backends.

Open-source monitoring solutions are able to power your alerting and metrics. Prometheus stores all data in time series. These are streams of timestamped value belonging to the same metric with the same labeled dimensions. Prometheus can also generate temporary derived times series as a result of queries. Prometheus offers a functional query language called PromQL, which allows the user to select and aggregate time series data real-time. The expression result can be displayed as a graph or tabular data in Prometheus’s expression browser. External systems can also consume the HTTP API. Prometheus can be configured using command-line flags or a configuration file. The command-line flags can be used to configure immutable system parameters such as storage locations and the amount of data to be kept on disk and in memory. . Download: https://sourceforge.net/projects/prometheus.mirror/

Aerospike is the global leader for next-generation, real time NoSQL data solutions at any scale. Aerospike helps enterprises overcome seemingly impossible data bottlenecks and compete with other companies at a fraction of the cost and complexity of legacy NoSQL databases. Aerospike's Hybrid Memory Architecture™ is a patented technology that unlocks the full potential of modern hardware and delivers previously unimaginable value. It does this by delivering unimaginable value from huge amounts of data at both the edge, core, and in the cloud. Aerospike empowers customers with the ability to instantly combat fraud, dramatically increase shopping cart sizes, deploy global digital payment networks, and provide instant, one-to-1 personalization for millions. Aerospike customers include Airtel and Banca d'Italia as well as Snap, Verizon Media, Wayfair, PayPal, Snap, Verizon Media, and Nielsen. The company's headquarters is in Mountain View, California. Additional locations are in London, Bengaluru, India, and Tel Aviv in Israel.

Time Series Database (TSDB), supports high-speed data writing and reading. It has high compression ratios to ensure data storage costs are low. This service supports visualization of precision reduction and interpolation, multimetric aggregate computing, as well as query results. The TSDB service lowers storage costs and improves data writing, query, analysis efficiency. This allows you to manage large numbers of data points and collect more data. This service is widely used in a variety of industries, including IoT monitoring systems and enterprise energy management systems (EMSs), power supply monitoring systems, production security monitoring systems, and IoT monitoring. Optimizes database architectures. TSDB can read and write millions of data points in seconds. Uses an efficient compression algorithm that reduces the data point's size to 2 bytes. This saves more than 90% on storage costs.

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

Proficy Historian, a top-of-the-line historian software solution, collects industrial time-series data and A&E data at a high speed, stores it securely and efficiently, distributes it, and allows fast retrieval and analysis. This results in greater business value. Proficy Historian has thousands of satisfied customers around the globe and decades of experience. It makes data available for process and asset performance analysis. Proficy Historian's latest version improves usability, configuration and maintainability by making significant architectural improvements. The solution's powerful yet simple features will allow you to unlock new value for your equipment, process data and business models. Remote collector management with UX. Horizontal scaling that allows enterprise-wide data visibility

KX Streaming Analytics allows you to ingest and store historical and time series data. This data can then be processed, stored, processed, and analyzed instantly to provide analytics, insights, or visualizations. The platform offers the complete lifecycle of data services to ensure that your applications and users can be productive quickly. This includes query processing, query processing, tiering and migration, archiving and data protection. Our advanced analytics and visualization tools are widely used in finance and industry. They allow you to create and execute queries, calculations, aggregations and machine learning on any streaming or historical data. Data can be used across multiple hardware environments and can come from high-volume sources such as clickstreams, radio frequency identification, GPS systems and social networking sites.

Parallel query engine with symbol- and time-indexed data access. Extended SQL syntax with advanced filtering, aggregations and more. Consolidate all quotes, trades and snapshots in one place. Strategy backtesting using high-frequency data. Quantitative and market microstructure analysis. Granular transaction cost analysis and rollup report. 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 retail and institutional financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering via trading session, auction stage, and index composition. Optimized aggregates to OHLCV and VWAP calculations. Interactive SQL console with auto completion. API endpoint for programmatic integrtion. Scheduled SQL reporting via email, file, or web delivery. JDBC and ODBC drivers.

OneTick Database is embraced by top banks, brokerages and data vendors as well as exchanges, markets, hedge funds, market makers, mutual funds and other financial institutions. OneTick is the best enterprise-wide solution for tick data collection, streaming analytics and data management. OneTick is loved by top hedge funds, mutual funds and banks as well as brokers, banks, brokerages and market makers. OneTick's proprietary time-series database is a unified platform for multi-asset classes. It includes a fully integrated streaming analytics engine, built-in business logic, and eliminates the need to use multiple disparate systems. The system has the lowest total cost-of-ownership.

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

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

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

Azure Time Series Intelligences Gen2 is an open-source, scalable IoT analytics service that can be integrated into existing workflows and applications. It can be used to collect, store, query, query, and visualize data at Internet of Things scale (IoT),-data that is highly contextualized and optimized in time series. Azure Time Series Insights Gen2 can be used for operational analysis and data exploration. It allows you to spot anomalies and uncover hidden trends. It is flexible and open to all industrial IoT deployments.

LevelDB is a fast key/value storage library that Google has created. It provides an ordered mapping of string keys to string value. Keys and values can be stored in arbitrary byte arrays. Data is stored in key order. To override the order of the data, callers can provide a custom comparator function. Multiple changes can be made to an atomic batch. To maintain a consistent view of data, users can create a temporary snapshot. Data can be used for forward and backward iteration. Snappy is used to automatically compress data. External activity (file system operations, etc.) The information is transmitted via a virtual interface to allow users to customize the operating system interactions. A database with over a million entries is used. Each entry is assigned a 16-byte key and a 100-byte value. The benchmark reduces the size of the values to approximately half of their original size. The benchmark lists the performance of sequential reading in the forward and reverse directions, as well as the performance of random lookups.

GridDB uses multicast communication in order to create a cluster. To enable multicast communication, set the network. First, verify the host name and IP address. To check the settings for an IP address on the host, run "hostname-i" command. If the IP address of your machine is identical to the below, you don't need to adjust network settings and can skip to the next section. GridDB is a database that manages a group (known as a Row) of data that is composed of multiple values and a key. It can be an in-memory database which arranges all data in the memory. However, it can also use a hybrid composition that uses both a disk (including SSD) and a memory.

Machbase is a time-series database that stores, analyzes, and displays a lot sensor data from different facilities in real time. It is the only DBMS that can process big data at high speed. Machbase's incredible speed is amazing! It is the most innovative product to enable real-time processing, storage and analysis of sensor data. DBMS embedded in Edge devices allows for high speed storage and inquiry of sensor data. DBMS running on a single server provides the best data storage and extraction performance. Multi-node cluster configuration with the benefits of availability and scaleability. Edge computing is a complete management solution for device, connectivity, and data.

Berkeley DB is a set of embedded key-value databases libraries that provide high-performance data management services for applications.

The Apache Cassandra database provides high availability and scalability without compromising performance. It is the ideal platform for mission-critical data because it offers linear scalability and demonstrated fault-tolerance with commodity hardware and cloud infrastructure. Cassandra's ability to replicate across multiple datacenters is first-in-class. This provides lower latency for your users, and the peace-of-mind that you can withstand regional outages.

The fastest NoSQL database in the world. The fastest NoSQL database available, capable of millions IOPS per node with less than 1 millisecond latency. This database will accelerate your application performance. Scylla, a drop-in Apache Cassandra and Amazon DynamoDB alternative, powers your applications with extreme throughput and ultra-low latency. To power modern, high-performance applications, we used the best features of high availability databases to create a NoSQL database that is significantly more efficient, fault-tolerant, and resource-efficient. This high-availability database is built from scratch in C++ for Linux. Scylla unleashes your infrastructure's true potential for running high-throughput/low-latency workloads.

InfinityDB Embedded, a Java NoSQL Java database, is a hierarchical sorted value store. It is flexible, high-performance, multicore, and maintenance-free. InfinityDB Client/Server and InfinityDB Encrypted databases are now also available. According to our customers and provided performance tests, InfinityDB offers the best performance. Multi-core overlapping operations scale almost linearly with thread count. Threads use fair scheduling with very low interthread interference. Random I/O scales logarithmically with file size. Caches grow only as they are used and are packed efficiently. Database open is instantaneous even after abrupt exit.

Couchbase, unlike other NoSQL database, provides a multicloud to edge enterprise-class database that offers robust capabilities for business-critical apps on a highly available and scalable platform. Couchbase is a distributed cloud native database that runs on any cloud. It can be managed by the customer or fully managed. Couchbase is built using open standards and combines the best of NoSQL and SQL with the power and familiarity that mainframes and relational databases provide. Couchbase Server is an open-source, multipurpose distributed database. It combines the best of relational databases, such as SQL, ACID transactions, and JSON, with a foundation which is fast and scalable. It is used in many industries for things such as user profiles, dynamic catalogs, GenAI applications, vector search, caching at high speed, and more.

kdb Insights, a cloud native, high-performance analytics solution designed for real-time data analysis of streaming and historical data, is a platform that can be used to analyze both streams and historical information. It allows for intelligent decision making regardless of data volume and velocity. It offers unmatched performance and price, and delivers analytics up to 100-fold faster than other solutions. The platform allows interactive data visualization via real-time dashboards to facilitate instantaneous insight and decision-making. It also integrates machine-learning models to predict and cluster structured data, detect patterns, score it, and enhance AI capabilities for time-series datasets. kdb Insights is scalable enough to handle large volumes of real-time data and historical data. This has been proven with volumes up to 110 Terabytes per Day. Its simple data intake and quick setup accelerate time-to value. Native support for q SQL and Python is also available, as well as compatibility with other programming languages via RESTful interfaces.

Oracle database products offer customers cost-optimized, high-performance versions Oracle Database, the world's most popular converged, multi-model database management software. They also include in-memory NoSQL and MySQL databases. Oracle Autonomous Database is available on-premises via Oracle Cloud@Customer and in the Oracle Cloud Infrastructure. It allows customers to simplify relational databases environments and reduce management burdens. Oracle Autonomous Database reduces the complexity of operating and protecting Oracle Database, while delivering the highest levels performance, scalability and availability to customers. Oracle Database can also be deployed on-premises if customers have network latency and data residency concerns. Customers who depend on Oracle database versions for their applications have full control over which versions they use and when they change.

Blueflood, a distributed metric processing system that is high throughput and low latency for multi-tenant multi-tenants, is behind Rackspace Metrics. It is currently being used in production by Rackspace Monitoring team as well as Rackspace public cloud team to store metrics created by their systems. Blueflood is also used in large-scale Rackspace deployments. Blueflood data can be used to create dashboards, reports, graphs, or any other use that involves time-series information. It is focused on near-realtime data and data that can be queryable within milliseconds of ingestion. You send metrics to ingestion service. The Query service allows you to query your metrics. Rollups are processed offline in the background so that queries with long time periods are quickly returned.

Circonus IRONdb makes it simple to store and manage unlimited volumes of Telemetry data. It can also handle billions of metric streams. Circonus IRONdb allows users to identify areas where there is opportunity and challenges in real-time. It provides forensic, predictive and automated analytics capabilities that are unmatched by any other product. Machine learning can automatically establish a "new norm" for your data and operations as they change. Circonus IRONdb is compatible with Grafana which supports our analytics query language. We also work with Graphite-web and other visualization apps. Circonus IRONdb protects your data by storing multiple copies in a cluster IRONdb nodes. Clustering is often managed by system administrators, who spend considerable time maintaining it and keeping the system running. Circonus IRONdb allows operators the ability to set and forget their cluster and stop manually managing their time series data stores.

The beauty of Canary Historian's solution is that it works on-site as well as for the entire enterprise. You can log data locally and send it to your enterprise historian simultaneously. The best part is that the solution grows with you. A single Canary Historian is capable of logging more than two million tags. Multiple Canary Historians can also be clustered to manage tens of thousands of tags. Enterprise historian solutions can be hosted in either your own data centers, or in AWS or Azure. Canary Historians are not dependent on specialized teams of ten or more to maintain, unlike other enterprise history solutions. The Canary Historian, a NoSQL time-series database, uses loss-less compression algorithms for high-speed performance without data interpolation.

IBM Informix®, a fast and flexible database that can seamlessly integrate SQL, NoSQL/JSON and time series data, is available. Informix's versatility and ease-of-use make it a popular choice for a wide variety of environments, including enterprise data warehouses and individual application development. Informix is also well-suited for embedded data management solutions due to its small footprint and self-managing capabilities.