InfluxData

San Francisco, CA, USA
2012
  |  By Product
The stakes are high in Aerospace manufacturing and operations. Aerospace systems are highly complex and require extremely precise engineering—every part of an aircraft or spacecraft must work together flawlessly, and error tolerance is minuscule. Ensuring that all components work perfectly under various conditions (pressure, temperature, vibration) is vital. The cost of building and operating aerospace systems is enormous.
  |  By Community
In a world driven by data, efficient time series data management is a growing concern. APIs play a significant role in automating tasks, especially in cloud-based environments. Go, with its high performance and concurrency, is quickly becoming one of the standard languages for writing cloud infrastructure and utilities for managing streams of data.
  |  By Andrew Lamb
This blog was originally published on Apache DataFusion Project News & Blog I am extremely excited to announce that Apache DataFusion 43.0.0 is the fastest engine for querying Apache Parquet files in ClickBench. It is faster than both DuckDB and chDB/Clickhouse using the same hardware. It also marks the first time a Rust based engine holds the top spot, which has previously been held by traditional C/C++ based engines.
  |  By Anais Dotis-Georgiou
In this blog post, we’ll explore how to build a data pipeline using Kafka, Faust, and InfluxDB to effectively ingest, transform, and store data. We’ll start with an overview of Kafka, a high-performance messaging platform, and Faust, a Python library designed for stream processing, now maintained by the community as Faust-streaming.
  |  By Anais Dotis-Georgiou
If you’re looking to dive into the world of IoT data collection, you’ve probably come across MQTT—a lightweight messaging protocol designed for efficiently transmitting data between devices.
  |  By Nga Tran
In a previous post, we described the technique that makes the ”most recent values” queries hundreds of times faster and has benefited many of our customers. The idea behind this technique is to progressively evaluate time-organized files until we reach the most recent values.
  |  By Jeffrey Smith
We have added a new function, tz, to InfluxDB 3.0 SQL to improve the ergonomics of working with timezones. This blog post will provide more details on how to use this function and when it makes sense to use it instead of the existing SQL timezone functions.
  |  By Neha Julka
In the first blog in this series, Setting Up InfluxDB and Visualizing Data: Part 1, we built a data collection and visualization platform for time series data using InfluxDB Cloud Serverless. Inspired by the CSTR with PID controllers use case, the project showcased how to ingest real-time data and visualize it using InfluxDB and Grafana. This follow-up post focuses on InfluxDB’s 3.0 architecture, giving an in-depth look at the platform’s inner workings.
  |  By David Sprogis
We are excited to announce the launch of User Groups, a major update that facilitates enhanced security through access control in InfluxDB Cloud Dedicated. This new feature allows for more granular access management by limiting limited access accounts. Giving customers more access control helps them implement PoLP (“Principle of Least Privilege”) for improved security.
  |  By Anais Dotis-Georgiou
The demand for LLM is rapidly increasing—it’s estimated that there will be 750 million apps using LLMs by 2025. As a result, the need for LLM observability and monitoring tools is also rising. In this blog, we’ll dive into what LLM monitoring and observability are, why they’re both crucial and how we can track various metrics to ensure our model isn’t just working but thriving.
  |  By InfluxData
This is quick tutorial using our three most popular technologies. This will be a basic overview, for more details on each technology in particular please check out our other videos.
  |  By InfluxData
This is short video describing what makes time series data unique. This is a common question we get asked about within our community.
  |  By InfluxData
This is a short video describing retention policies in InfluxDB, this is a concept used in all 3 version of influx.
  |  By InfluxData
Veteran developers and staff engineers at InfluxData, Nga Tran and Andrew Lamb, have an honest conversation about dealing with software bugs. Bugs can be frustrating, but they can also be thrilling. They are a sign that people are actually using your software - and that's a good thing! Andrew and Nga talk through a recent bug their team encountered, how they approached resolving the issue, and what considerations go into building a permanent fix.
  |  By InfluxData
Veteran developers and staff engineers at InfluxData, Nga Tran and Andrew Lamb, discuss what it was like to rewrite InfluxDB for version 3.0. Several factors prevent companies, especially startups, from rewriting their products. But what does the process look like once a company embarks on a rewrite? And how do they balance innovation with user feedback?
  |  By InfluxData
InfluxData staff engineers Nga Tran and Andrew Lamb discuss what separates a coder from a software engineer.
  |  By InfluxData
To the unfamiliar, building with open source tools may seem like the kind of chaos that leads to Boaty McBoatface-like decisions. Andrew Lamb, staff engineer at InfluxData and PMC for the Apache DataFusion project, provides insight from a developer and a PMC perspective about what it's like to build with, and manage a major open source project. InfluxData recently rebuilt its core database using Apache projects: Flight, DataFusion, Arrow, and Parquet, dubbed the FDAP stack.
  |  By InfluxData
Using open source projects from the Apache foundation to build low-level database software drives innovation. Andrew Lamb, Staff Engineer at InfluxData and PMC for the Apache DataFusion project, discusses the components of the FDAP stack - Flight, Arrow, DataFusion, and Parquet, explaining how building with these tools helps companies focus on innovation instead of spending dev cycles reinventing the wheel.
  |  By InfluxData
This video will go over how to build a dashboard with different graph types in @Grafana using InfluxDB V3.
  |  By InfluxData
This video goes over how to take advantage of variables in @Grafana while using InfluxDB as a data source.
  |  By InfluxData
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.
  |  By InfluxData
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.
  |  By InfluxData
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.
  |  By 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.
  |  By InfluxData
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.
  |  By InfluxData
In this technical paper, InfluxData CTO - Paul Dix will walk you through what time series is (and isn't), what makes it different than stream processing, full-text search and other solutions. He'll also work through why time series database engines are the superior choice for the monitoring, metrics, real-time analytics and Internet of Things/sensor data use cases.
  |  By InfluxData
As the number of metrics collected and acted on increases, developers need a solution that is fast and efficient to keep up with the demands of their solutions. We'll compare the performance and features of InfluxDB and OpenTSDB for common time series db 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.
  |  By InfluxData
In this this technical paper, we'll compare the performance and features of InfluxDB vs MongoDB 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.
  |  By InfluxData
In this technical paper, we'll compare the performance and features of InfluxDB and Cassandra 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.
  |  By InfluxData
To help provide a better understanding of how to get the best performance out of InfluxDB, this technical paper we will delve into the top five performance tuning tips for improving both write and query performance with InfluxDB. Topics covered include cardinality, batching, down-sampling, schema design and time-stamp precision.

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.