InfluxData, the creators of InfluxDB, delivers a modern Open Source Platform built from the ground up for analyzing metrics and events (time series data) for DevOps and IoT applications. Whether the data comes from humans, sensors, or machines, InfluxData empowers developers to build next-generation monitoring, analytics, and IoT applications faster, easier, and to scale delivering real business value quickly.
InfluxData provides the leading time series platform to instrument, observe, learn and automate any system, application and business process across a variety of use cases:
- DevOps Observability Observing and automating key customer-facing systems, infrastructure, applications and business processes.
- IoT Analytics Analyzing and automating sensors and devices in real-time delivering insight and value while it still matters.
- Real-Time Analytics Leveraging the investment in instrumentation and observability—detecting patterns and creating new business opportunities.
Customers turn to InfluxData to build DevOps Monitoring (Infrastructure Monitoring, Application Monitoring, Cloud Monitoring), IoT Monitoring, and Real-Time Analytics applications faster, easier, and to scale.
HashiCorp builds tools to manage both physical machines and virtual machines, Windows, and Linux, SaaS and IaaS, etc. And like InfluxDB, their foundational technologies are open source and developed openly.
Today, PagerDuty announced their PagerDuty Integration Partner Program and we are thrilled to be included. They built this program to establish an ecosystem that provides integrations to help their customers innovate and improve operations faster and comprehensively.
A new maintenance release for Chronograf is now available.
InfluxData, the modern Open Source Platform built specifically for metrics, events and other time series data that empowers developers to build next-generation monitoring, analytics and IoT applications, today announced that it will present on time series data monitoring and analysis at important industry
A new maintenance release for Telegraf is available now.
Everything related to how IT services are delivered and consumed is undergoing tremendous change. Monolithic architectures are being replaced by microservices-driven apps and the cloud- based infrastructure is being tied together and instrumented by DevOps processes.
Companies are committed to delivering on higher levels of customer satisfaction for their online services. Unfortunately, many organizations trying to support these initiatives take an interrupt-driven approach where they scramble to fix things when they break. However, to manage to these high levels of SLAs, you should take a structured approach in order to reduce the amount of unscheduled downtime by proactively monitoring and managing your systems.
This paper reviews how an IoT Data platform fits in with any IoT Architecture to manage the data requirements of every IoT implementation. It is based on the learnings from existing IoT practitioners that have adopted an IoT Data platform using InfluxData.
In this technical paper, we’ll compare the performance and features of InfluxDB 1.4.2 vs. Elasticsearch 5.6.3 for common time series workloads, specifically looking at the rates of data ingestion, on-disk data compression, and query performance. This data should prove valuable to developers and architects evaluating the suitability of these technologies for their use case.
In this technical paper, we’ll explore the aspects of scaling clusters of the InfluxEnterprise product, primarily through the lens of write performance of InfluxDB Clusters. This data should prove valuable to developers and architects evaluating the suitability of InfluxEnterprise for their use case, in addition to helping establish some rough guidelines for what those users should expect in terms of write performance in a real-world environment.
Paul will outline his vision around the platform and give the latest updates on Flux (a new Data Scripting language), the decoupling of query and storage, the impact of hybrid cloud environments on architecture, cardinality, and discuss the technical directions of the platform. This talk will walk through the vision and architecture with demonstrations of working prototypes of the projects.
Scientific python programmers adore Pandas due to its many functionalities. In particular, for data manipulation and analysis it offers handy data structures and operations for numerical tables and time series. Combined with the rest of the SciPy stack and scikit-learn (e.g. for Machine Learning Analysis), multiple goals can be achieved. When it comes to on-line data analysis, interaction, or simple data navigation by multiple users, the SciPy stack can be stressed to its limits.
The new Flux (formerly IFQL) super-charges queries both for analytics and data science. David gave a quick overview of the language features as well as the moving parts for a working deployment. Grafana is an open source dashboard solution that shares Flux’s passion for analytics and data science. For that reason, they are very excited to showcase the new Flux support within Grafana, and a couple of common analytics use cases to get the most out of your data.
Chronograf is the visualization tool for the TICK Stack that makes getting started with your Time Series Database easy. Tim will share best practices around using templates and libraries with Chronograf as well as share some exciting roadmap updates.
At Amazon, we created a collection of machine learning algorithms that scale to any amount of data, including k-means clustering for data segmentation, factorization machines for recommendations, and time-series forecasting. This talk will discuss those algorithms, understand where and how they can be used, and our design choices.