ChaosSearch's Chaos LakeDB helps organizations make better use of their log and event data. The cloud data platform enables users to search, analyze, and visualize application telemetry data stored in Amazon S3 or Google Cloud Platform. Use cases include application observability, security analytics, product data analysis, and embedded analytics.
Our Chaos LakeDB is the first and only data lake database designed to power live Search, SQL, and Generative Artificial Intelligence (GenAI) analytics. By integrating with Amazon Web Services’ (AWS) Amazon Simple Storage Service (Amazon S3), the preferred object store for millions of AWS customers of all sizes and industries, ChaosSearch helps merge the vast storage capabilities of data lakes with the accessibility of cloud databases. Eliminating the need for complex extract, transform, load (ETL) and extract, load, transform (ELT) processes, we offer live analytics while ensuring enhanced cost efficiency and performance at scale.
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ChaosSearch is an ideal replacement for Elasticsearch (ELK stack) or Opensearch. With ChaosSearch, customers can perform scalable log analytics on AWS S3 or GCS, using familiar APIs for queries, and Kibana for log analytics and visualizations, while reducing costs and improving analytical capabilities.
ChaosSearch helps customers centralize logs to extend retention and reduce their Datadog budget in one of two ways - Use only Datadog's monitoring tools, alongside ChaosSearch for centralized log management. Or, reduce Datadog’s log retention to three days and use ChaosSearch for unlimited retention, with a cost savings of approximately 40%.
ChaosSearch reduces security and observability costs for modern enterprises, replacing Splunk for long-term analysis. Customers can keep Splunk for key security workflows and centralize all other logs in ChaosSearch – achieving 50-80% savings with unlimited, long-term data retention.
CHAOSSEARCH's answer
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CHAOSSEARCH's answer
Our Chaos LakeDB is the first and only data lake database designed to power live Search, SQL, and Generative Artificial Intelligence (GenAI) analytics. By integrating with Amazon Web Services’ (AWS) Amazon Simple Storage Service (Amazon S3), the preferred object store for millions of AWS customers of all sizes and industries, ChaosSearch helps merge the vast storage capabilities of data lakes with the accessibility of cloud databases. Eliminating the need for complex extract, transform, load (ETL) and extract, load, transform (ELT) processes, we offer live analytics while ensuring enhanced cost efficiency and performance at scale.
CHAOSSEARCH's answer
Reduced Time, Cost & Complexity
Unlimited retention without rehydration needs
Unmatched Cost-Performance at Scale
Data only in cloud storage
Chaos Index® is 5-20x smaller than raw
Small data = Small compute
Stateless = Compute just for ingest & query, not store
Unified Live Search+ SQL+GenAI Analytics
Single platform across operational & business use cases
All data stored in customers' cloud storage with granular RBAC
No sharding, partitioning, schema management including of nested data
Auto-scaling & seamless upgrades
ChaosSearch is an ideal replacement for Elasticsearch (ELK stack) or Opensearch. With ChaosSearch, customers can perform scalable log analytics on AWS S3 or GCS, using familiar APIs for queries, and Kibana for log analytics and visualizations, while reducing costs and improving analytical capabilities.
ChaosSearch helps customers centralize logs to extend retention and reduce their Datadog budget in one of two ways - Use only Datadog's monitoring tools, alongside ChaosSearch for centralized log management. Or, reduce Datadog’s log retention to three days and use ChaosSearch for unlimited retention, with a cost savings of approximately 40%.
ChaosSearch reduces security and observability costs for modern enterprises, replacing Splunk for long-term analysis. Customers can keep Splunk for key security workflows and centralize all other logs in ChaosSearch – achieving 50-80% savings with unlimited, long-term data retention.
Based on our record, Random User Generator seems to be more popular. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Currently I use Fastmail to host my own domain, and then for every new service I save the account in 1Password using service@service.mydomain.com It's a bit extreme, but surprisingly little extra effort. That said, most of the accounts I create are throwaway. For phone numbers I have an old, unused Google voice number I give out. It's a real number, but never checked, except for once every 90? days, to keep it... - Source: Hacker News / 3 months ago
Import axios from 'axios'; Import { FC, useState } from 'react'; Import { AppProps, Users } from './App.types'; Import User from './components/User'; Const App: FC = ({ title }) => { const [users, setUsers] = useState([]); const [isLoading, setIsLoading] = useState(false); // useEffect(() => { // const getUsers = async () => { // try { // setIsLoading(true); ... - Source: dev.to / 6 months ago
Fetch("https://randomuser.me/api/") .then(response=>{ if(!response.ok){ throw new Error("Failed") } return response.json() }) .then(data => console.log(data)) .catch(error => alert(error)). - Source: dev.to / 9 months ago
I just released the first version of random_user, a convenient generator of random user. It simply use the great api from https://randomuser.me/, with reqwest. Source: 10 months ago
Hello, I'd like to make a React Native application using the RandomUser API. I want to make a simple app that takes the data from RandomUser and displays it as a list of people to hire for different jobs. On top of the current API, I want to add a property for categories each person will work in. Something like:. Source: over 1 year ago
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