Alternatives to Amazon Timestream

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

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, scalable upserts, 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. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times. RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.

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.

Amazon ElastiCache enables users to effortlessly establish, operate, and expand widely-used open-source compatible in-memory data stores in the cloud environment. It empowers the development of data-driven applications or enhances the efficiency of existing databases by allowing quick access to data through high throughput and minimal latency in-memory stores. This service is particularly favored for various real-time applications such as caching, session management, gaming, geospatial services, real-time analytics, and queuing. With fully managed options for Redis and Memcached, Amazon ElastiCache caters to demanding applications that necessitate response times in the sub-millisecond range. Functioning as both an in-memory data store and a cache, it is designed to meet the needs of applications that require rapid data retrieval. Furthermore, by utilizing a fully optimized architecture that operates on dedicated nodes for each customer, Amazon ElastiCache guarantees incredibly fast and secure performance for its users' critical workloads. This makes it an essential tool for businesses looking to enhance their application's responsiveness and scalability.

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.

Amazon Relational Database Service (Amazon RDS) simplifies the process of establishing, managing, and scaling a relational database in the cloud. It offers a cost-effective and adjustable capacity while taking care of tedious administrative tasks such as hardware provisioning, setting up databases, applying patches, and performing backups. This allows you to concentrate on your applications, ensuring they achieve fast performance, high availability, security, and compatibility. Amazon RDS supports various database instance types optimized for memory, performance, or I/O, and offers a selection of six well-known database engines, including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and SQL Server. Additionally, the AWS Database Migration Service facilitates the seamless migration or replication of your existing databases to Amazon RDS, making the transition straightforward and efficient. Overall, Amazon RDS empowers businesses to leverage robust database solutions without the burden of complex management tasks.

A Time Series Database (TSDB) is designed for rapid data input and output, allowing for swift reading and writing of information. It achieves impressive compression rates that lead to economical data storage solutions. Moreover, this service facilitates visualization techniques, such as precision reduction, interpolation, and multi-metric aggregation, alongside the processing of query results. By utilizing TSDB, businesses can significantly lower their storage expenses while enhancing the speed of data writing, querying, and analysis. This capability allows for the management of vast quantities of data points and enables more frequent data collection. Its applications span various sectors, including IoT monitoring, enterprise energy management systems (EMSs), production security oversight, and power supply monitoring. Additionally, TSDB is instrumental in optimizing database structures and algorithms, capable of processing millions of data points in mere seconds. By employing an advanced compression method, it can minimize each data point's size to just 2 bytes, leading to over 90% savings in storage costs. Consequently, this efficiency not only benefits businesses financially but also streamlines operational workflows across different industries.

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.

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.

Azure Time Series Insights Gen2 is a robust and scalable IoT analytics service that provides an exceptional user experience along with comprehensive APIs for seamless integration into your current workflow or application. This platform enables the collection, processing, storage, querying, and visualization of data at an Internet of Things (IoT) scale, ensuring that the data is highly contextualized and specifically tailored for time series analysis. With a focus on ad hoc data exploration and operational analysis, it empowers users to identify hidden trends, detect anomalies, and perform root-cause investigations. Furthermore, Azure Time Series Insights Gen2 stands out as an open and adaptable solution that caters to the diverse needs of industrial IoT deployments, making it an invaluable tool for organizations looking to harness the power of their data. By leveraging its capabilities, businesses can gain deeper insights into their operations and make informed decisions to drive efficiency and innovation.

A parallel query engine designed for efficient access to time- and symbol-indexed data. It incorporates an extended SQL syntax that allows for sophisticated filtering and aggregation capabilities. Users can unify quotes, trades, snapshots, and reference data within a single environment. The platform supports strategy backtesting using high-frequency data for enhanced analysis. It facilitates quantitative research and insights into market microstructure. Additionally, it offers detailed transaction cost analysis and comprehensive rollup reporting features. Market surveillance mechanisms and anomaly detection capabilities are also integrated into the system. The decomposition of non-transparent ETF/ETN instruments is supported, along with the utilization of FAST, SBE, and proprietary communication protocols. A plain text protocol is available alongside consolidated and direct data feeds. The system includes built-in tools for monitoring latency and provides end-of-day archival options. It can perform ETL processes from both institutional and retail financial data sources. Designed with a parallel SQL engine that features syntax extensions, it allows advanced filtering by trading session, auction stage, and index composition for precise analysis. Optimizations for aggregates related to OHLCV and VWAP calculations enhance performance. An interactive SQL console with auto-completion improves user experience, while an API endpoint facilitates seamless programmatic integration. Scheduled SQL reporting options are available, allowing delivery via email, file, or web. JDBC and ODBC drivers ensure compatibility with various applications, making this system a versatile tool for financial data handling.

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.

Fauna is a data API that supports rich clients with serverless backends. It provides a web-native interface that supports GraphQL, custom business logic, frictionless integration to the serverless ecosystem, and a multi-cloud architecture that you can trust and grow with.

Amazon Athena serves as an interactive query service that simplifies the process of analyzing data stored in Amazon S3 through the use of standard SQL. As a serverless service, it eliminates the need for infrastructure management, allowing users to pay solely for the queries they execute. The user-friendly interface enables you to simply point to your data in Amazon S3, establish the schema, and begin querying with standard SQL commands, with most results returning in mere seconds. Athena negates the requirement for intricate ETL processes to prepare data for analysis, making it accessible for anyone possessing SQL skills to swiftly examine large datasets. Additionally, Athena integrates seamlessly with AWS Glue Data Catalog, which facilitates the creation of a consolidated metadata repository across multiple services. This integration allows users to crawl data sources to identify schemas, update the Catalog with new and modified table and partition definitions, and manage schema versioning effectively. Not only does this streamline data management, but it also enhances the overall efficiency of data analysis within the AWS ecosystem.

Amazon FinSpace streamlines the deployment of kdb Insights applications on AWS, making the process significantly easier. By automating the routine tasks necessary for provisioning, integrating, and securing the infrastructure needed for kdb Insights, Amazon FinSpace simplifies operations for its users. Furthermore, it offers intuitive APIs that enable customers to set up and initiate new kdb Insights applications in just a matter of minutes. This platform allows users the flexibility to transition their existing kdb Insights applications to AWS, harnessing the advantages of cloud computing without the burden of managing complex and expensive infrastructure. KX's kdb Insights serves as a robust analytics engine, tailored for the examination of both real-time and extensive historical time-series data. Frequently utilized by clients in Capital Markets, kdb Insights supports essential business functions such as options pricing, transaction cost analysis, and backtesting. Additionally, it eliminates the need to integrate more than 15 AWS services for the deployment of kdb, streamlining the entire process further. Overall, Amazon FinSpace empowers organizations to focus on their analytics while minimizing operational overhead.

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.

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.

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.

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.

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.

Oracle's database offerings provide clients with cost-effective and high-efficiency options, including the renowned multi-model database management system, as well as in-memory, NoSQL, and MySQL databases. The Oracle Autonomous Database, which can be accessed on-premises through Oracle Cloud@Customer or within the Oracle Cloud Infrastructure, allows users to streamline their relational database systems and lessen management burdens. By removing the intricacies associated with operating and securing Oracle Database, Oracle Autonomous Database ensures customers experience exceptional performance, scalability, and reliability. Furthermore, organizations concerned about data residency and network latency can opt for on-premises deployment of Oracle Database. Additionally, clients who rely on specific versions of Oracle databases maintain full authority over their operational versions and the timing of any updates. This flexibility empowers businesses to tailor their database environments according to their unique requirements.

Amazon Forecast is an entirely managed service that employs machine learning techniques to provide exceptionally precise predictions. In the contemporary business landscape, organizations utilize a range of tools, from basic spreadsheets to intricate financial planning applications, in their quest to accurately project future outcomes such as product demand, resource allocation, and overall financial results. These forecasting tools generate predictions by analyzing historical data known as time series data. For instance, they might estimate future demand for raincoats based solely on past sales figures, operating under the premise that future performance will mirror historical trends. However, this methodology can falter when tasked with managing extensive datasets that exhibit irregular patterns. Moreover, it often struggles to seamlessly integrate evolving data streams—like pricing, discounts, web traffic, and workforce numbers—with pertinent independent variables, such as product specifications and retail locations. As a result, businesses seeking reliable forecasts may find themselves facing significant challenges in adapting to the complexities of their data.

IBM Informix® is a highly adaptable and efficient database that can effortlessly combine SQL, NoSQL/JSON, as well as time series and spatial data. Its flexibility and user-friendly design position Informix as a top choice for diverse settings, ranging from large-scale enterprise data warehouses to smaller individual application development projects. Moreover, due to its compact footprint and self-managing features, Informix is particularly advantageous for embedded data management applications. The rising demand for IoT data processing necessitates strong integration and processing capabilities, which Informix fulfills with its hybrid database architecture that requires minimal administrative effort and has a small memory footprint while delivering robust functionality. Notably, Informix is well-equipped for multi-tiered architectures that necessitate processing at various levels, including devices, gateway layers, and cloud environments. Furthermore, it incorporates native encryption to safeguard data both at rest and in transit. Additionally, Informix supports a flexible schema alongside multiple APIs and configurations, making it a versatile choice for modern data management challenges.

Analyzing time-series data is crucial for the daily functions of numerous businesses. Common applications involve assessing consumer foot traffic and conversion rates for retailers, identifying anomalies in data, discovering real-time correlations within sensor information, and producing accurate recommendations. With the Cloud Inference API Alpha, businesses can derive real-time insights from their time-series datasets that they input. This tool provides comprehensive details about API query results, including the various groups of events analyzed, the total number of event groups, and the baseline probability associated with each event returned. It enables real-time streaming of data, facilitating the computation of correlations as events occur. Leveraging Google Cloud’s robust infrastructure and a comprehensive security strategy that has been fine-tuned over 15 years through various consumer applications ensures reliability. The Cloud Inference API is seamlessly integrated with Google Cloud Storage services, enhancing its functionality and user experience. This integration allows for more efficient data handling and analysis, positioning businesses to make informed decisions faster.

PuppyGraph allows you to effortlessly query one or multiple data sources through a cohesive graph model. Traditional graph databases can be costly, require extensive setup time, and necessitate a specialized team to maintain. They often take hours to execute multi-hop queries and encounter difficulties when managing datasets larger than 100GB. Having a separate graph database can complicate your overall architecture due to fragile ETL processes, ultimately leading to increased total cost of ownership (TCO). With PuppyGraph, you can connect to any data source, regardless of its location, enabling cross-cloud and cross-region graph analytics without the need for intricate ETLs or data duplication. By directly linking to your data warehouses and lakes, PuppyGraph allows you to query your data as a graph without the burden of constructing and maintaining lengthy ETL pipelines typical of conventional graph database configurations. There's no longer a need to deal with delays in data access or unreliable ETL operations. Additionally, PuppyGraph resolves scalability challenges associated with graphs by decoupling computation from storage, allowing for more efficient data handling. This innovative approach not only enhances performance but also simplifies your data management strategy.

Tabular is an innovative open table storage solution designed by the same team behind Apache Iceberg, allowing seamless integration with various computing engines and frameworks. By leveraging this technology, users can significantly reduce both query times and storage expenses, achieving savings of up to 50%. It centralizes the enforcement of role-based access control (RBAC) policies, ensuring data security is consistently maintained. The platform is compatible with multiple query engines and frameworks, such as Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python, offering extensive flexibility. With features like intelligent compaction and clustering, as well as other automated data services, Tabular further enhances efficiency by minimizing storage costs and speeding up query performance. It allows for unified data access at various levels, whether at the database or table. Additionally, managing RBAC controls is straightforward, ensuring that security measures are not only consistent but also easily auditable. Tabular excels in usability, providing robust ingestion capabilities and performance, all while maintaining effective RBAC management. Ultimately, it empowers users to select from a variety of top-tier compute engines, each tailored to their specific strengths, while also enabling precise privilege assignments at the database, table, or even column level. This combination of features makes Tabular a powerful tool for modern data management.

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.

IBM Db2 Big SQL is a sophisticated hybrid SQL-on-Hadoop engine that facilitates secure and advanced data querying across a range of enterprise big data sources, such as Hadoop, object storage, and data warehouses. This enterprise-grade engine adheres to ANSI standards and provides massively parallel processing (MPP) capabilities, enhancing the efficiency of data queries. With Db2 Big SQL, users can execute a single database connection or query that spans diverse sources, including Hadoop HDFS, WebHDFS, relational databases, NoSQL databases, and object storage solutions. It offers numerous advantages, including low latency, high performance, robust data security, compatibility with SQL standards, and powerful federation features, enabling both ad hoc and complex queries. Currently, Db2 Big SQL is offered in two distinct variations: one that integrates seamlessly with Cloudera Data Platform and another as a cloud-native service on the IBM Cloud Pak® for Data platform. This versatility allows organizations to access and analyze data effectively, performing queries on both batch and real-time data across various sources, thus streamlining their data operations and decision-making processes. In essence, Db2 Big SQL provides a comprehensive solution for managing and querying extensive datasets in an increasingly complex data landscape.

Enhance your metrics and alerting capabilities using a top-tier open-source monitoring tool. Prometheus inherently organizes all data as time series, which consist of sequences of timestamped values associated with the same metric and a specific set of labeled dimensions. In addition to the stored time series, Prometheus has the capability to create temporary derived time series based on query outcomes. The tool features a powerful query language known as PromQL (Prometheus Query Language), allowing users to select and aggregate time series data in real time. The output from an expression can be displayed as a graph, viewed in tabular format through Prometheus’s expression browser, or accessed by external systems through the HTTP API. Configuration of Prometheus is achieved through a combination of command-line flags and a configuration file, where the flags are used to set immutable system parameters like storage locations and retention limits for both disk and memory. This dual method of configuration ensures a flexible and tailored monitoring setup that can adapt to various user needs. For those interested in exploring this robust tool, further details can be found at: https://sourceforge.net/projects/prometheus.mirror/

As the volume of traffic and user demands on your application grows, enhance its performance in a straightforward and economical way. Implementing a caching layer within your application architecture can efficiently manage thousands of concurrent users, providing near-instantaneous response times, all while leveraging the advantages of a fully managed service. Achieve remarkable throughput and performance capable of processing millions of requests per second with sub-millisecond latency. This fully managed service includes automatic updates, patching, scaling, and provisioning, allowing you to concentrate on development without distraction. Integration of modules like RedisBloom, RediSearch, and RedisTimeSeries empowers your application with comprehensive capabilities for data analysis, search functionality, and real-time streaming. You will benefit from robust features such as clustering, built-in replication, Redis on Flash, and an impressive availability rate of up to 99.99 percent, ensuring reliability. Furthermore, by complementing services like Azure SQL Database and Azure Cosmos DB, you can enhance your data tier's throughput scalability at a more economical rate compared to merely expanding database instances. Ultimately, these enhancements not only improve the user experience but also position your application for future growth and adaptability.

Time-sensitive decisions require immediate attention. With TRND, machine learning projects can be accelerated from lengthy timelines to mere minutes, similar to the efficiency of Google's AI search engine that delivers insights in real time. Financial losses can stem from inaccuracies, and unlike standard KPIs, TRND identifies patterns that uncover critical information hidden from traditional metrics. This tool empowers decision-makers by highlighting trends that signal emerging threats or opportunities. In today's digital landscape, knowledge equates to financial advantage. TRND allows for the creation of shareable pattern libraries, promoting rapid learning and implementation for enhancing business operations. Without comprehensive monitoring, potential profits remain untapped. TRND excels at not only identifying rare insights but also continuously tracking relevant information across all indicators. The myth that scaling efforts must be excessively costly is shattered by our innovative search-based method, which makes extensive micro-monitoring both feasible and economical. In essence, TRND revolutionizes the approach to data utilization, ensuring that businesses stay ahead in a competitive environment.

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.

Kibana serves as a free and open user interface that enables the visualization of your Elasticsearch data while providing navigational capabilities within the Elastic Stack. You can monitor query loads or gain insights into how requests traverse your applications. This platform offers flexibility in how you choose to represent your data. With its dynamic visualizations, you can start with a single inquiry and discover new insights along the way. Kibana comes equipped with essential visual tools such as histograms, line graphs, pie charts, and sunbursts, among others. Additionally, it allows you to conduct searches across all your documents seamlessly. Utilize Elastic Maps to delve into geographic data or exercise creativity by visualizing custom layers and vector shapes. You can also conduct sophisticated time series analyses on your Elasticsearch data using our specially designed time series user interfaces. Furthermore, articulate queries, transformations, and visual representations with intuitive and powerful expressions that are easy to master. By employing these features, you can uncover deeper insights into your data, enhancing your overall analytical capabilities.

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.

SiriDB is optimized for speed. Inserts and queries are answered quickly. You can speed up your development with the custom query language. SiriDB is flexible and can be scaled on the fly. There is no downtime when you update or expand your database. You can scale your database without losing speed. As we distribute your time series data across all pools, we make full use of all resources. SiriDB was designed to deliver unmatched performance with minimal downtime. A SiriDB cluster distributes time series across multiple pools. Each pool has active replicas that can be used for load balancing or redundancy. The database can still be accessed even if one of the replicas is unavailable.

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.

Blueflood is an advanced distributed metric processing system designed for high throughput and low latency, operating as a multi-tenant solution that supports Rackspace Metrics. It is actively utilized by both the Rackspace Monitoring team and the Rackspace public cloud team to effectively manage and store metrics produced by their infrastructure. Beyond its application within Rackspace, Blueflood also sees extensive use in large-scale deployments documented in community resources. The data collected through Blueflood is versatile, allowing users to create dashboards, generate reports, visualize data through graphs, or engage in any activities that involve analyzing time-series data. With a primary emphasis on near-real-time processing, data can be queried just milliseconds after it is ingested, ensuring timely access to information. Users send their metrics to the ingestion service and retrieve them from the Query service, while the system efficiently handles background rollups through offline batch processing, thus facilitating quick responses for queries covering extended time frames. This architecture not only enhances performance but also ensures that users can rely on rapid access to their critical metrics for effective decision-making.

Detecting anomalies in time series data is critical for the daily functions of numerous organizations. The Timeseries Insights API Preview enables you to extract real-time insights from your time-series datasets effectively. It provides comprehensive information necessary for interpreting your API query results, including details on anomaly occurrences, projected value ranges, and segments of analyzed events. This capability allows for the real-time streaming of data, facilitating the identification of anomalies as they occur. With over 15 years of innovation in security through widely-used consumer applications like Gmail and Search, Google Cloud offers a robust end-to-end infrastructure and a layered security approach. The Timeseries Insights API is seamlessly integrated with other Google Cloud Storage services, ensuring a uniform access method across various storage solutions. You can analyze trends and anomalies across multiple event dimensions and manage datasets that encompass tens of billions of events. Additionally, the system is capable of executing thousands of queries every second, making it a powerful tool for real-time data analysis and decision-making. Such capabilities are invaluable for businesses aiming to enhance their operational efficiency and responsiveness.

kdb Insights is an advanced analytics platform built for the cloud, enabling high-speed real-time analysis of both live and past data streams. It empowers users to make informed decisions efficiently, regardless of the scale or speed of the data, and boasts exceptional price-performance ratios, achieving analytics performance that is up to 100 times quicker while costing only 10% compared to alternative solutions. The platform provides interactive data visualization through dynamic dashboards, allowing for immediate insights that drive timely decision-making. Additionally, it incorporates machine learning models to enhance predictive capabilities, identify clusters, detect patterns, and evaluate structured data, thereby improving AI functionalities on time-series datasets. With remarkable scalability, kdb Insights can manage vast amounts of real-time and historical data, demonstrating effectiveness with loads of up to 110 terabytes daily. Its rapid deployment and straightforward data ingestion process significantly reduce the time needed to realize value, while it natively supports q, SQL, and Python, along with compatibility for other programming languages through RESTful APIs. This versatility ensures that users can seamlessly integrate kdb Insights into their existing workflows and leverage its full potential for a wide range of analytical tasks.

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.

TrendMiner is an advanced industrial analytics platform that is fast, powerful, and intuitive. It was designed to monitor and troubleshoot industrial processes in real-time. It allows for robust data collection, analysis and visualization, allowing everyone in industrial operations to make smarter data-driven decision efficiently. TrendMiner is a Proemion Company founded in 2008. Our global headquarters are located in Belgium and we have offices in the U.S.A., Germany, Spain, and the Netherlands. TrendMiner has strategic alliances with major players like Amazon, Microsoft and SAP. It also offers standard integrations for a variety of historians, including Honeywell PHD and GE Proficy Historian.

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.

The Waylay platform is a modular IoT solution that offers top-tier OEM technology for backend development and operational processes, facilitating the rapid delivery of IoT solutions on a large scale. It features sophisticated rule logic modeling, execution capabilities, and comprehensive lifecycle management. This platform allows for the automation of various data workflows, regardless of their complexity. Designed to seamlessly handle the diverse data patterns found in IoT, OT, and IT, Waylay integrates both streaming and time series analytics into a single, collaborative intelligence environment. By providing non-developer teams with user-friendly, self-service apps focused on key performance indicators, it significantly shortens the time to market for IoT products. Users can identify the most effective automation tools for their specific IoT applications and evaluate them against established benchmarks. Additionally, developing IoT applications diverges significantly from traditional IT development, as it entails connecting the physical realm of Operations Technology, which involves sensors and actuators, with the digital landscape of Information Technology, encompassing databases and software systems. This bridging of worlds highlights the unique challenges and opportunities presented by IoT application development.

The ITTIA DB suite brings together advanced features for time series, real-time data streaming, and analytics tailored for embedded systems, ultimately streamlining development processes while minimizing expenses. With ITTIA DB IoT, users can access a compact embedded database designed for real-time operations on resource-limited 32-bit microcontrollers (MCUs), while ITTIA DB SQL serves as a robust time-series embedded database that operates efficiently on both single and multicore microprocessors (MPUs). These ITTIA DB offerings empower devices to effectively monitor, process, and retain real-time data. Additionally, the products are specifically engineered to meet the needs of Electronic Control Units (ECUs) within the automotive sector. To ensure data security, ITTIA DB incorporates comprehensive protection mechanisms against unauthorized access, leveraging encryption, authentication, and the DB SEAL feature. Furthermore, ITTIA SDL adheres to the standards set forth by IEC/ISO 62443, reinforcing its commitment to safety. By integrating ITTIA DB, developers can seamlessly collect, process, and enhance incoming real-time data streams through a specialized SDK designed for edge devices, allowing for efficient searching, filtering, joining, and aggregating of data right at the edge. This comprehensive approach not only optimizes performance but also supports the growing demand for real-time data handling in today's technology landscape.