Best Time Series Databases for Mid Size Business - Page 2

QuasarDB, the core of Quasar's intelligence, is an advanced, distributed, column-oriented database management system specifically engineered for high-performance timeseries data handling, enabling real-time processing for massive petascale applications. It boasts up to 20 times less disk space requirement, making it exceptionally efficient. The unmatched ingestion and compression features of QuasarDB allow for up to 10,000 times quicker feature extraction. This database can perform real-time feature extraction directly from raw data via an integrated map/reduce query engine, a sophisticated aggregation engine that utilizes SIMD capabilities of contemporary CPUs, and stochastic indexes that consume minimal disk storage. Its ultra-efficient resource utilization, ability to integrate with object storage solutions like S3, innovative compression methods, and reasonable pricing structure make it the most economical timeseries solution available. Furthermore, QuasarDB is versatile enough to operate seamlessly across various platforms, from 32-bit ARM devices to high-performance Intel servers, accommodating both Edge Computing environments and traditional cloud or on-premises deployments. Its scalability and efficiency make it an ideal choice for businesses aiming to harness the full potential of their data in real-time.

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.

Apache Druid is a powerful open-source distributed data storage solution that integrates principles from data warehousing, timeseries databases, and search technologies to deliver exceptional performance for real-time analytics across various applications. Its innovative design synthesizes essential features from these three types of systems, which is evident in its ingestion layer, storage format, query execution, and foundational architecture. By individually storing and compressing each column, Druid efficiently accesses only the necessary data for specific queries, enabling rapid scanning, sorting, and grouping operations. Additionally, Druid utilizes inverted indexes for string values to enhance search and filtering speeds. Equipped with ready-to-use connectors for platforms like Apache Kafka, HDFS, and AWS S3, Druid seamlessly integrates with existing data workflows. Its smart partitioning strategy greatly accelerates time-based queries compared to conventional databases, allowing for impressive performance. Users can easily scale their systems by adding or removing servers, with Druid automatically managing the rebalancing of data. Furthermore, its fault-tolerant design ensures that the system can effectively navigate around server failures, maintaining operational integrity. This resilience makes Druid an excellent choice for organizations seeking reliable analytics solutions.

KX Streaming Analytics offers a comprehensive solution for ingesting, storing, processing, and analyzing both historical and time series data, ensuring that analytics, insights, and visualizations are readily accessible. To facilitate rapid productivity for your applications and users, the platform encompasses the complete range of data services, which includes query processing, tiering, migration, archiving, data protection, and scalability. Our sophisticated analytics and visualization tools, which are extensively utilized in sectors such as finance and industry, empower you to define and execute queries, calculations, aggregations, as well as machine learning and artificial intelligence on any type of streaming and historical data. This platform can be deployed across various hardware environments, with the capability to source data from real-time business events and high-volume inputs such as sensors, clickstreams, radio-frequency identification, GPS systems, social media platforms, and mobile devices. Moreover, the versatility of KX Streaming Analytics ensures that organizations can adapt to evolving data needs and leverage real-time insights for informed decision-making.

Versio.io is a cutting-edge enterprise software solution designed to oversee the identification and post-processing of changes within large organizations. Our innovative methodologies have allowed us to develop a completely novel type of enterprise product that stands out in the market. In this document, we provide an overview of our extensive research and development efforts. Relationships can form between various assets and configurations, serving as a crucial enhancement of the available information. Traditional data sources typically capture only a fraction of this essential information. Through Versio.io, we leverage our topology service to automatically identify and map these relationships, facilitating the connection of dependencies among instances from any data source. Consequently, all critical business assets and configuration items across every level of an organization can be effectively gathered, historicized, topologized, and stored in a centralized repository, ensuring comprehensive visibility and management. This capability not only enhances operational efficiency but also supports informed decision-making across the enterprise.

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

OpenTSDB comprises a Time Series Daemon (TSD) along with a suite of command line tools. Users primarily engage with OpenTSDB by operating one or more independent TSDs, as there is no centralized master or shared state, allowing for the scalability to run multiple TSDs as necessary to meet varying loads. Each TSD utilizes HBase, an open-source database, or the hosted Google Bigtable service for the storage and retrieval of time-series data. The schema designed for the data is highly efficient, enabling rapid aggregations of similar time series while minimizing storage requirements. Users interact with the TSD without needing direct access to the underlying storage system. Communication with the TSD can be accomplished through a straightforward telnet-style protocol, an HTTP API, or a user-friendly built-in graphical interface. To begin utilizing OpenTSDB, the initial task is to send time series data to the TSDs, and there are various tools available to facilitate the import of data from different sources into OpenTSDB. Overall, OpenTSDB's design emphasizes flexibility and efficiency for time series data management.

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

Hawkular Metrics is a robust, asynchronous, multi-tenant engine designed for long-term metrics storage, utilizing Cassandra for its data management and REST as its main interface. This segment highlights some of the essential characteristics of Hawkular Metrics, while subsequent sections will delve deeper into these features as well as additional functionalities. One of the standout aspects of Hawkular Metrics is its impressive scalability; its architecture allows for operation on a single instance with just one Cassandra node, or it can be expanded to encompass multiple nodes to accommodate growing demands. Moreover, the server is designed with a stateless architecture, facilitating easy scaling. Illustrated in the accompanying diagram are various deployment configurations enabled by the scalable design of Hawkular Metrics. The upper left corner depicts the most straightforward setup involving a lone Cassandra node connected to a single Hawkular Metrics node, while the lower right corner demonstrates a scenario where multiple Hawkular Metrics nodes can operate in conjunction with fewer Cassandra nodes, showcasing flexibility in deployment. Overall, this system is engineered to meet the evolving requirements of users efficiently.

Heroic is an open-source monitoring solution initially developed at Spotify to tackle challenges related to the large-scale collection and near real-time analysis of metrics. It comprises a limited number of specialized components that each serve distinct purposes. The system offers indefinite data retention, contingent upon adequate hardware investment, alongside federation capabilities that enable multiple Heroic clusters to connect and present a unified interface. A key component, Consumers, is tasked with the consumption of metrics, illustrating the system's design for efficiency. During the development of Heroic, it became evident that managing hundreds of millions of time series without sufficient context poses significant challenges. Additionally, the federation support facilitates the handling of requests across various independent Heroic clusters, allowing them to serve clients via a single global interface. This feature not only streamlines operations but also minimizes geographical traffic, as it allows individual clusters to function independently within their designated zones. Such capabilities ensure that Heroic remains a robust choice for organizations needing effective monitoring solutions.

Proficy Historian stands out as a premier historian software solution designed to gather industrial time-series and A&E data at remarkable speeds, ensuring secure and efficient storage, distribution, and rapid access for analysis, ultimately enhancing business value. With a wealth of experience and a track record of thousands of successful implementations globally, Proficy Historian transforms how organizations operate and compete by making critical data accessible for analyzing asset and process performance. The latest version of Proficy Historian offers improved usability, configurability, and maintainability thanks to significant advancements in its architecture. Users can leverage the solution's powerful yet straightforward features to derive new insights from their equipment, process data, and business strategies. Additionally, the remote collector management feature enhances user experience, while horizontal scalability facilitates comprehensive data visibility across the enterprise, making it an essential tool for modern businesses. By adopting Proficy Historian, companies can unlock untapped potential and drive operational excellence.

Circonus IRONdb simplifies the management and storage of limitless telemetry data, effortlessly processing billions of metric streams. It empowers users to recognize both opportunities and challenges in real time, offering unmatched forensic, predictive, and automated analytics capabilities. With the help of machine learning, it automatically establishes a "new normal" as your operations and data evolve. Additionally, Circonus IRONdb seamlessly integrates with Grafana, which natively supports our analytics query language, and is also compatible with other visualization tools like Graphite-web. To ensure data security, Circonus IRONdb maintains multiple copies across a cluster of IRONdb nodes. While system administrators usually oversee clustering, they often dedicate considerable time to its upkeep and functionality. However, with Circonus IRONdb, operators can easily configure their clusters to run autonomously, allowing them to focus on more strategic tasks rather than the tedious management of their time series data storage. This streamlined approach not only enhances efficiency but also maximizes resource utilization.

KairosDB allows data ingestion through various protocols including Telnet, Rest, and Graphite, in addition to supporting plugins for further flexibility. It utilizes Cassandra, a well-regarded NoSQL database, to manage time series data effectively. The database schema is organized into three column families, facilitating efficient data storage. The API offers a range of functionalities, such as listing existing metric names, retrieving tag names and their corresponding values, storing metric data points, and querying these points for analysis. Upon a standard installation, users can access a query page that enables them to extract data from the database easily. This tool is primarily tailored for development applications. Aggregators within the system can perform operations on data points, allowing for down sampling and analysis. A set of standard functions, including min, max, sum, count, and mean, among others, are readily available for users to utilize. Additionally, the KairosDB server supports import and export functionalities via the command line interface. Internal metrics related to the database not only provide insights into the stored data but also allow for monitoring the performance of the server itself, ensuring optimal operation and efficiency. This comprehensive approach makes KairosDB a powerful solution for managing time series data.

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

Azure Time Series Insights Gen2 is a comprehensive and adaptable IoT analytics platform that provides exceptional user experiences alongside robust APIs, enabling seamless integration of its advanced features into your current applications or workflows. This service allows for the collection, processing, storage, querying, and visualization of data tailored for the vast scale of the Internet of Things (IoT), focusing on highly contextualized data specifically optimized for time series analysis. Designed for both ad hoc exploration and operational analytics, it empowers users to discover hidden patterns, identify anomalies, and perform root-cause analyses effectively. As a versatile and open solution, it caters to the diverse requirements of industrial IoT implementations while ensuring scalability and ease of use. Furthermore, its capabilities can significantly enhance decision-making processes and operational efficiencies in various sectors.

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

Amazon FinSpace streamlines the deployment of kdb Insights applications on AWS, effectively handling the routine tasks necessary for provisioning, integration, and securing infrastructure tailored for kdb Insights. It also features user-friendly APIs that enable clients to set up and run new kdb Insights applications within mere minutes. This platform offers customers the versatility to transition existing kdb Insights applications to AWS, allowing them to reap the advantages of cloud computing while removing the burdensome and expensive tasks associated with self-managing their infrastructure. KX’s kdb Insights serves as a high-performance analytics engine, specifically designed for the analysis of real-time and extensive historical time-series data, making it a favored choice among Capital Markets clients for critical business applications such as options pricing, transaction cost analysis, and backtesting. Furthermore, Amazon FinSpace eliminates the need to integrate over 15 different AWS services for deploying kdb, greatly simplifying the process. As a result, businesses can focus more on their core operations rather than getting bogged down by infrastructure management challenges.

Introducing a versatile, open-source multi-cloud platform for contemporary data applications, built on Apache Cassandra™. Achieve global-scale performance with guaranteed 100% uptime while avoiding vendor lock-in. You have the flexibility to deploy on multi-cloud environments, on-premises infrastructures, or use Kubernetes. The platform is designed to be elastic and offers a pay-as-you-go pricing model to enhance total cost of ownership. Accelerate your development process with Stargate APIs, which support NoSQL, real-time interactions, reactive programming, as well as JSON, REST, and GraphQL formats. Bypass the difficulties associated with managing numerous open-source projects and APIs that lack scalability. This solution is perfect for various sectors including e-commerce, mobile applications, AI/ML, IoT, microservices, social networking, gaming, and other highly interactive applications that require dynamic scaling based on demand. Start your journey of creating modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Leverage REST, GraphQL, and JSON alongside your preferred full-stack framework. This platform ensures that your richly interactive applications are not only elastic but also ready to gain traction from the very first day, all while offering a cost-effective Apache Cassandra DBaaS that scales seamlessly and affordably as your needs evolve. With this innovative approach, developers can focus on building rather than managing infrastructure.

Introducing a robust cross-platform columnar database designed for high-performance historical time-series data, which includes: - A compute engine optimized for in-memory operations - A streaming processor that functions in real time - A powerful query and programming language known as q Kdb+ drives the kdb Insights portfolio and KDB.AI, offering advanced time-focused data analysis and generative AI functionalities to many of the world's top enterprises. Recognized for its unparalleled speed, kdb+ has been independently benchmarked* as the leading in-memory columnar analytics database, providing exceptional benefits for organizations confronting complex data challenges. This innovative solution significantly enhances decision-making capabilities, enabling businesses to adeptly respond to the ever-evolving data landscape. By leveraging kdb+, companies can gain deeper insights that lead to more informed strategies.

A Time Series Database (TSDB) facilitates rapid data reading and writing, allowing for efficient handling of extensive datasets. It achieves impressive compression ratios that significantly lower storage expenses. Additionally, this service provides capabilities for visualizing precision reduction, performing interpolation, and executing multi-metric aggregate computations alongside query outcomes. By optimizing the storage costs, the TSDB enhances the speed of data writing, querying, and analysis processes. As a result, it is well-equipped to manage large volumes of data points and enables more frequent data collection. The versatility of this service makes it applicable across various sectors, including IoT monitoring, enterprise energy management systems (EMSs), production security monitoring, and power supply monitoring. Moreover, it optimizes database architectures and algorithms, enabling it to read or write millions of data points in just seconds. The use of a highly effective compression algorithm allows for a reduction in the size of each data point to only 2 bytes, achieving over 90% savings in storage costs, thus making it an invaluable tool for data-driven decision-making and operational efficiency.

Amazon Timestream is a rapid, scalable, and serverless database service designed for time series data, catering to IoT and operational applications, allowing users to store and analyze trillions of events daily at speeds up to 1,000 times faster and at costs as low as one-tenth of traditional relational databases. By efficiently managing the lifecycle of time series data, Amazon Timestream keeps current data in memory while transferring older data to a more economical storage tier based on user-defined policies, thus saving both time and expenses. Its unique query engine enables seamless access to and analysis of both recent and historical data without the need for users to specify whether the data is stored in the in-memory or cost-effective tier. Additionally, Amazon Timestream comes equipped with built-in time series analytics functions, allowing users to detect trends and patterns in their data almost in real-time, enhancing decision-making processes. This combination of features makes Timestream an optimal choice for businesses looking to leverage time series data efficiently.

GridDB utilizes multicast communication to form its cluster, so it's essential to configure the network for this purpose. Start by verifying the host name and IP address; you can do this by running the command “hostname -i” to check the host's IP address configuration. If the reported IP address matches the specified value below, you can proceed directly to the next section without any further network adjustments. GridDB is a database designed to manage a collection of data entries, each consisting of a key paired with several values. In addition to functioning as an in-memory database that organizes all data within the memory, it also supports a hybrid architecture that combines both memory and disk storage, which can include solid-state drives (SSDs). This flexibility allows for efficient data management and retrieval, catering to various application needs.

JaguarDB facilitates the rapid ingestion of time series data while integrating location-based information. It possesses the capability to index data across both spatial and temporal dimensions effectively. Additionally, the system allows for swift back-filling of time series data, enabling the insertion of significant volumes of historical data points. Typically, time series refers to a collection of data points that are arranged in chronological order. However, in JaguarDB, time series encompasses both a sequence of data points and multiple tick tables that hold aggregated data values across designated time intervals. For instance, a time series table in JaguarDB may consist of a primary table that organizes data points in time sequence, along with tick tables that represent various time frames such as 5 minutes, 15 minutes, hourly, daily, weekly, and monthly, which store aggregated data for those intervals. The structure for RETENTION mirrors that of the TICK format but allows for a flexible number of retention periods, defining the duration for which data points in the base table are maintained. This approach ensures that users can efficiently manage and analyze historical data according to their specific needs.

Blueflood is a distributed metric processing system designed for high throughput and low latency, serving as the backbone for Rackspace Metrics and currently utilized by the Rackspace Monitoring and public cloud teams to manage metrics produced by their infrastructure. Beyond its use within Rackspace, Blueflood has also been implemented in various large-scale deployments, which can be explored on community Wiki. The data processed by Blueflood is ideal for creating dashboards, producing reports, and generating graphs or any application that requires time-series data analysis. It emphasizes the importance of near real-time data availability, allowing metrics to be queried just milliseconds after they are ingested. Users send metrics to the ingestion service and retrieve them through the Query service, while the system efficiently handles offline batch processing of rollups in the background, ensuring quick query responses for extensive time periods. This combination of features makes Blueflood a versatile tool for managing and analyzing metric data.