TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license. 
 
TimescaleDB automatically scales and spreads data written to a hypertable into many dynamically-created chunks. Hypertables present the abstraction of a single table, yet transparently partitioned by time and space, they enable high insert rates even at billions of rows.

 TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support. TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license. 

Timescaledb - An open-source time-series database optimized for fast ingest and complex queries

InfluxDB is an open source time series platform. This includes APIs for storing and querying data, processing it in the background for ETL or monitoring and alerting purposes, user dashboards, and visualizing and exploring the data and more. The master branch on this repo now represents the latest InfluxDB, which now includes functionality for Kapacitor (background processing) and Chronograf (the UI) all in a single binary.  It is designed to handle high write and query loads and provides a SQL-like query language called InfluxQL for interacting with data.

InfluxDB - Scalable datastore for metrics, events, and real-time analytics

Cortex provides horizontally scalable, highly available, multi-tenant, long term storage for Prometheus. Cortex can run across multiple machines in a cluster, exceeding the throughput and storage of a single machine. This enables you to send the metrics from multiple Prometheus servers to a single Cortex cluster and run "globally aggregated" queries across all data in a single place. Cortex makes your PromQL queries blazin' fast through aggressive parallelization and caching.
 
When run in a cluster, Cortex can replicate data between machines. This allows you to survive machine failure without gaps in your graphs. Cortex can isolate data and queries from multiple different independent Prometheus sources in a single cluster, allowing untrusted parties to share the same cluster.
 
Cortex supports Amazon DynamoDB, Google Bigtable, Cassandra, S3, GCS and Microsoft Azure for long term storage of metric data. This allows you to durably store data for longer than the lifetime of any single machine, and use this data for long term capacity planning.

Cortex provides horizontally scalable, highly available, multi-tenant, long term storage for Prometheus. Cortex can run across multiple machines in a cluster, exceeding the throughput and storage of a single machine. This enables you to send the metrics from multiple Prometheus servers to a single Cortex cluster and run "globally aggregated" queries across all data in a single place. Cortex makes your PromQL queries blazin' fast through aggressive parallelization and caching.



Cortex - A multitenant, horizontally scalable Prometheus as a Service

QuestDB is a high-performance, open-source SQL database for applications in financial services, IoT, machine learning, DevOps and observability. It includes endpoints for PostgreSQL wire protocol, high-throughput schema-agnostic ingestion using InfluxDB Line Protocol, and a REST API for queries, bulk imports, and exports.
 
QuestDB implements ANSI SQL with native extensions for time-oriented language features. These extensions make it simple to correlate data from multiple sources using relational and time series joins. QuestDB achieves high performance from a column-oriented storage model, massively-parallelized vector execution, SIMD instructions, and various low-latency techniques. The entire codebase was built from the ground up in Java and C++, with no dependencies, and is 100% free from garbage collection.

QuestDB is a high-performance, open-source SQL database for applications in financial services, IoT, machine learning, DevOps and observability. It includes endpoints for PostgreSQL wire protocol, high-throughput schema-agnostic ingestion using InfluxDB Line Protocol, and a REST API for queries, bulk imports, and exports.

QuestDB - Fast relational time-series database

Kibana provides flexible analytics and visualization platform for Elasticsearch. It understands large volume of data and easily create bar charts, line and scatter plots, histograms, pie charts, and maps. It can provide real-time summary and charting of streaming data. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Kibana - Analytics and search dashboard for Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected. 
 
 Its feature include: 
 <ul>
	<li>Distributed and Highly Available Search Engine.
	<ul>
		<li>Each index is fully sharded with a configurable number of shards.</li>
		<li>Each shard can have one or more replicas.</li>
		<li>Read / Search operations performed on either one of the replica shard.</li>
	</ul></li>
	<li>Multi Tenant with Multi Types.
	<ul>
		<li>Support for more than one index.</li>
		<li>Support for more than one type per index.</li>
		<li>Index level configuration (number of shards, index storage, &#8230;).</li>
	</ul></li>
	<li>Various set of APIs
	<ul>
		<li>HTTP RESTful API</li>
		<li>Native Java API.</li>
		<li>All APIs perform automatic node operation rerouting.</li>
	</ul></li>
	<li>Document oriented
	<ul>
		<li>No need for upfront schema definition.</li>
		<li>Schema can be defined per type for customization of the indexing process.</li>
	</ul></li>
	<li>Reliable, Asynchronous Write Behind for long term persistency.</li>
	<li>(Near) Real Time Search.</li>
	<li>Built on top of Lucene
	<ul>
		<li>Each shard is a fully functional Lucene index</li>
		<li>All the power of Lucene easily exposed through simple configuration / plugins.</li>
	</ul></li>
	<li>Per operation consistency
	<ul>
		<li>Single document level operations are atomic, consistent, isolated and durable.</li>
	</ul></li>
</ul>

Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected. 

ElasticSearch - Distributed, RESTful search and analytics engine

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations. Druid can load both streaming and batch data. 

Druid IO - Real Time Exploratory Analytics on Large Datasets

Timelion, pronounced "Timeline", brings together totally independent data sources into a single interface, driven by a simple, one-line expression language combining data retrieval, time series combination and transformation, plus visualization. Every Timelion expression starts with a data source function.

Timelion -  Time series composer for Elasticsearch and beyond

Cubism.js is a D3 plugin for visualizing time series. Use Cubism to construct better realtime dashboards, pulling data from Graphite, Cube and other sources. Cubism fetches time series data incrementally: after the initial display, Cubism reduces server load by polling only the most recent values. Cubism renders incrementally, too, using Canvas to shift charts one pixel to the left. 

Cubism.js - Time Series Visualization

KairosDB is a fast distributed scalable time series database written on top of Cassandra. Data can be pushed in KairosDB via multiple protocols : Telnet, Rest, Graphite. KairosDB stores time series in Cassandra, the popular and performant NoSQL datastore. It supports aggregators which can perform an operation on data points and down samples. Standard functions like min, max, sum, count, mean etc.

Kairosdb - Fast distributed scalable time series database written on top of Cassandra

TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.
 
<ul><li>10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.</li><li>1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.</li><li>Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust.</li><li>Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.</li><li>Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.</li><li>Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.</li></ul><div> </div>

TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.

TDengine -  Big data platform designed and optimized for the Internet of Things

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.
 
DalmatinerDB is designed to run on ZFS file system and it relies heavily on taking advantage of ZFS facilities such as the ARC, ZIL, checksums and volume compression. The frontend servers give users access to a rich SQL-like query language that's easy to use for both dev and ops. It integrates nicely with Grafana making it easy to build beautiful dashboards.

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.

DalmatinerDB - Fast distributed metrics database in Erlang

AresDB is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.

AresDB - A GPU-powered real-time analytics storage and query engine

Chaos Genius is an open source ML powered analytics engine for outlier detection and root cause analysis. Chaos Genius can be used to monitor and analyse high dimensionality business, data and system metrics at scale. Using Chaos Genius, users can segment large datasets by key performance metrics (e.g. Daily Active Users, Cloud Costs, Failure Rates) and important dimensions (e.g., countryID, DeviceID, ProductID, DayofWeek) across which they want to monitor and analyse the key metrics.

Chaos Genius - ML powered analytics engine for outlier detection and root cause analysis

SiriDB is a highly-scalable, robust and super fast time series database. Build from the ground up SiriDB uses a unique mechanism to operate without indexes and allows server resources to be added on the fly. SiriDB's unique query language includes dynamic grouping of time series for easy and super fast analysis over large amount's of time series. 

SiriDB - Highly-scalable, robust and super fast time series database

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