Best Time Series Databases for Small Business

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.

BangDB seamlessly incorporates AI, streaming, graph capabilities, and analytics directly into the database, allowing users to effectively manage a variety of complex data types, including text, images, videos, and objects, for real-time processing and analysis. Users can ingest or stream any data, perform processing, train models, make predictions, identify patterns, and automate actions to facilitate a wide range of applications such as IoT monitoring, fraud prevention, log analysis, lead generation, and personalized experiences. In today’s landscape, the demand for handling diverse data types simultaneously to address specific challenges is more critical than ever. BangDB accommodates a wide array of data formats, empowering users to tackle problems with ease. The increasing prevalence of real-time data necessitates robust streaming solutions and predictive analytics to enhance and optimize business operations, ensuring that organizations stay competitive and responsive to changing demands. This integrated approach not only streamlines workflows but also fosters innovative solutions across various sectors.

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.

NumXL is a suite time series Excel add-ins. It turns your Microsoft Excel application into a top-class time series software and an econometrics tool. It offers the same statistical accuracy as more expensive statistical packages. NumXL integrates with Excel natively, adding scores of econometric function, a rich set shortcuts, as well as intuitive user interfaces to help you navigate the entire process. (1) Summary Statistics - Gini and Hurst, KDE etc. (2) Statistical Testing - Normality, Stationarity, cointegration, etc. (3) Brown's, Holt's & Winter's exponential smoothing (4) ARMA/ARIMA/SARIMA & X12ARIMA (5) ARMAX/SARIMAX (6) GARCH/E-GARCH & E-GARCH

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.

Telegraf is an open-source server agent that helps you collect metrics from your sensors, stacks, and systems. Telegraf is a plugin-driven agent that collects and sends metrics and events from systems, databases, and IoT sensors. Telegraf is written in Go. It compiles to a single binary and has no external dependencies. It also requires very little memory. Telegraf can gather metrics from a wide variety of inputs and then write them into a wide range of outputs. It can be easily extended by being plugin-driven for both the collection and output data. It is written in Go and can be run on any system without external dependencies. It is easy to collect metrics from your endpoints with the 300+ plugins that have been created by data experts in the community.

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.

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.

Instaclustr, the Open Source-as a Service company, delivers reliability at scale. We provide database, search, messaging, and analytics in an automated, trusted, and proven managed environment. We help companies focus their internal development and operational resources on creating cutting-edge customer-facing applications. Instaclustr is a cloud provider that works with AWS, Heroku Azure, IBM Cloud Platform, Azure, IBM Cloud and Google Cloud Platform. The company is certified by SOC 2 and offers 24/7 customer support.

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.

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.

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/

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.

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.

VictoriaMetrics Cloud allows you to run VictoriaMetrics Enterprise on AWS without having to perform typical DevOps activities such as proper configuration and monitoring, log collection, security, software updates, software protection, or backups. We run VictoriaMetrics Cloud in our environment using AWS, and provide easy to use endpoints for data ingestion. VictoriaMetrics takes care of software maintenance and optimal configuration. It has the following features: It can be used to manage Prometheus. Configure Prometheus, Vmagent or VictoriaMetrics to write data into Managed VictoriaMetrics. Then use the endpoint provided as a Prometheus source in Grafana. Each VictoriaMetrics Cloud instance runs in a separate environment so that instances cannot interfere with one another; VictoriaMetrics Cloud can be scaled-up or scaled-down in just a few clicks. Automated backups.

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.

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.

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.

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.

Cortex is an innovative open-source solution that enhances horizontal scalability. While Prometheus is capable of handling up to 1 million samples per second on a single machine, Cortex enables a virtually limitless level of horizontal scaling. In an ever-evolving landscape, it is essential to adopt alternative strategies for monitoring individual virtual machines or servers. Prometheus features a service-discovery-driven, pull-based metrics system that caters to the dynamic characteristics of microservices. This capability allows for seamless monitoring of your entire ecosystem, regardless of the number of components involved. You can instrument your application to generate tailored metrics using the standard Prometheus client libraries, or you can leverage the vast array of Prometheus Exporters that gather data from existing software like MySQL, Redis, Java, ElasticSearch, and many others. By adopting these tools, organizations can ensure they maintain visibility and control over their complex infrastructures. This flexibility is particularly valuable in today's fast-paced, continuously changing technological environments.

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.

Effortlessly handle intricate models with ArcadeDB while ensuring no sacrifices are made. Forget the need for Polyglot Persistence; there's no requirement for various databases. Within a single ArcadeDB Multi-Model database, you can efficiently store graphs, documents, key-value pairs, and time series data all together. Because each model is inherently integrated into the database engine, concerns about translation delays are eliminated. ArcadeDB’s engine, crafted using advanced technology, can process millions of records every second. What sets ArcadeDB apart is its consistent traversal speed, which remains unaffected by the size of the database, whether it contains a handful of records or billions. Furthermore, ArcadeDB can serve as an embedded database on a single server and has the capability to scale across multiple servers using Kubernetes. Its versatility allows it to operate on any platform while maintaining a minimal resource footprint. Your data's security is paramount; our robust, fully transactional engine guarantees durability for critical production databases. To uphold consistency across various servers, ArcadeDB employs a Raft Consensus Algorithm, ensuring that your data remains reliable and synchronized even in a distributed environment. With ArcadeDB, you can confidently manage your data needs without the hassle of juggling multiple systems.

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.

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.

A parallel query engine facilitates access to time- and symbol-indexed data efficiently. It features an enhanced SQL syntax that allows for sophisticated filtering and comprehensive aggregations. This system consolidates various types of financial information, such as quotes, trades, snapshots, and reference data, into a single repository. Users can conduct strategy backtesting utilizing high-frequency data and engage in quantitative research as well as market microstructure analysis. The platform provides detailed transaction cost analysis and allows for rollup reporting, ensuring thorough insight into trading activities. It also includes market surveillance capabilities and tools for detecting anomalies. Moreover, it can decompose non-transparent ETFs and ETNs, utilizing FAST, SBE, and proprietary protocols for improved performance. A plain text protocol ensures ease of use, while both consolidated and direct feeds are available for data ingestion. Built-in tools for monitoring latency are included, along with comprehensive end-of-day archives. The engine supports ETL processes from both institutional and retail financial data sources. It boasts a parallel SQL engine with syntax extensions, allowing advanced filtering by various criteria such as trading session and auction stage. Additionally, it offers optimized aggregate calculations for OHLCV and VWAP metrics. An interactive SQL console equipped with auto-completion enhances user experience, and an API endpoint facilitates programmatic integration. Scheduled SQL reporting is available with options for delivery via email, file, or web, along with JDBC and ODBC drivers for broader accessibility. This robust system is designed to meet the demands of modern financial analysis and trading strategies.