Companies that use KairosDB
KairosDB is a fast distributed scalable time series database written on top of Cassandra.
23
companies
List of companies using KairosDB
curl --request POST \
--url "https://api.theirstack.com/v1/companies/search" \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer <api_key>" \
-d "{
\"company_technology_slug_or\": [
\"kairosdb\"
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is any of
KairosDB
Employees1.3kRevenue$1.4BTechnology, Information and InternetKairosDBLow confidence
Employees25kRevenue$7.8BAutomation Machinery ManufacturingKairosDBLow confidence
Employees103Revenue$6.7MSoftware DevelopmentKairosDBLow confidence
Employees331IT Services and IT ConsultingKairosDBLow confidence
Employees2.3MRevenue$611BRetailKairosDBLow confidence
Employees61IT Services and IT ConsultingKairosDBLow confidence
Employees220Revenue$126MIT Services and IT ConsultingKairosDBLow confidence
Employees199IT Services and IT ConsultingKairosDBLow confidence
Employees1.4kUtilitiesKairosDBLow confidence- Employees15kSoftware DevelopmentKairosDBLow confidence
We have data on 23 companies and users that use KairosDB. Our KairosDB users and customers list is available for download and comes enriched with vital company specifics, including industry classification, organizational size, geographical location, funding rounds, and revenue figures, among others.
Technology Usage Statistics and Market Share
How to target KairosDB users
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Frequently asked questions
Our data is sourced from job postings collected from millions of companies. We monitor these postings on company websites, job boards, and other recruitment platforms. Analyzing job postings provides a reliable method to understand the technologies companies are employing, including their use of internal tools.
We refresh our data daily to ensure you are accessing the most current information available. This frequent updating process guarantees that our insights and intelligence reflect the latest developments and trends within the industry.
KairosDB is an open-source time-series database written in Java that is designed to scale to massive levels, making it ideal for storing metrics and other time-series data. It offers high performance and scalability, allowing users to efficiently manage and analyze large volumes of timestamped data. KairosDB provides flexible data aggregation across different time intervals, enabling users to query and visualize time-series data effectively.
Databases such as KairosDB fall under the category of Time-Series Databases. These specialized databases are optimized for handling data that is indexed by time, making them especially suitable for applications that deal with metrics, sensor data, and IoT (Internet of Things) data. Time-series databases excel in efficiently storing, querying, and analyzing data points that are associated with timestamps, allowing users to derive valuable insights from temporal data patterns.
KairosDB was founded in 2012 by a team of developers with a vision to create a robust time-series database that could address the growing need for managing time-stamped data at scale. Motivated by the increasing demand for handling metrics and sensor data in real-time applications, the creators of KairosDB set out to build a solution that could meet the performance and scalability requirements of modern time-series data use cases.
In terms of current market share, KairosDB has carved a niche for itself within the Time-Series Database category, particularly favored by users looking for an open-source solution with strong scaling capabilities. While its market share may not be as dominant as some of the more established players in the database market, KairosDB continues to attract users who value its performance and flexibility for time-series data storage and analysis. With the increasing adoption of IoT technologies and the growing importance of real-time analytics, it is anticipated that KairosDB's market share is poised to see gradual growth in the future as organizations seek specialized solutions for managing time-series data effectively.