InfluxData

San Francisco, CA, USA
2012
  |  By Jason Myers /
Picture a bustling control room at a major aerospace company, where engineers and executives monitor aircraft performance, analyze flight data, and make critical decisions in real-time. In this dynamic environment, the ability to harness the power of real-time analytics becomes paramount. This is where InfluxDB 3.0, the latest version of InfluxData’s time series database, delivers an innovative edge to organizations with time-critical analytics needs.
  |  By Jason Myers /
Despite changes in technology, culture, economics, or virtually any other factor imaginable, the adage ‘time is money’ remains relevant. When it comes to data analysis, the faster you can conduct analysis, the better. However, increasing data volumes across the board make it challenging to analyze and act on data in a timely manner.
  |  By Jason Myers /
In the fast-paced world of software engineering, efficient data management is a cornerstone of success. Imagine you’re working with streams of data that not only require rapid analysis but also need to store that data for long-term insights. This is where the powerful duo of time series databases (TSDBs) and data lakes can help.
  |  By Jason Myers /
Monitoring the performance and health of infrastructure is crucial for ensuring smooth operations. From data centers and cloud environments to networks and IoT devices, infrastructure monitoring plays a vital role in identifying issues, optimizing resource utilization, and maintaining high availability. However, traditional monitoring approaches often struggle to handle the volume and velocity of data generated by modern infrastructures. This is where time series databases, like InfluxDB, come into play.
  |  By Anais Dotis-Georgiou /
Imagine a data engineer working for a large e-commerce company tasked with building a system that can process and analyze customer clickstream data in real-time. By leveraging Amazon Kinesis and InfluxDB, they can achieve this goal efficiently and effectively. So, how do we get from idea to finished solution? First, we need to understand the tools at hand.
  |  By Jason Myers /
Database Administrators (DBAs) rely on time series data every day, even if they don’t think of time series data as a unique data type. They rely on metrics such as CPU usage, memory utilization, and query response times to monitor and optimize databases. These metrics inherently have a time component, making them time series data. However, traditional databases aren’t specifically designed to handle the unique characteristics and workloads associated with time series data.
  |  By Jason Myers /
This article was originally published on IIoT World and is reprinted here with permission. In the rapidly evolving world of Industrial Internet of Things (IIoT), organizations face numerous challenges when it comes to managing and analyzing the vast amounts of data generated by their industrial processes. Data generated by instrumented industrial equipment is consistent, predictable, and inherently time-stamped.
  |  By Jason Myers /
When it comes to network monitoring, time series data is a transformative force, revolutionizing how network engineers monitor and manage their networks. By capturing and analyzing data points over time, time series data provides a detailed and dynamic view of network performance, enabling network professionals to identify trends, patterns, and anomalies that might otherwise go unnoticed.
  |  By Anais Dotis-Georgiou /
If you’re an InfluxDB v2 user, you might be wondering what happened to the task engine in InfluxDB 3.0. The answer is that we removed it in order to support broader interoperability with other task tools. V3 enables users to leverage any existing ETL tool rather than being locked into the limited capabilities of the Flux task engine.
  |  By Anais Dotis-Georgiou /
DronaHQ is a cloud-based platform designed to simplify the process of building and deploying business applications. It serves as a low-code development environment, enabling users—even those with limited technical expertise—to create custom applications quickly and efficiently. The platform offers a range of tools and features, including drag-and-drop interfaces, pre-built templates, and integrations with various databases and APIs.
  |  By InfluxData
It's your data. You should be able to do whatever you want with it. However, vendor lock-in can trap your data in a single solution, making it extremely difficult to switch to something that better meets your needs. When your data goes in, but doesn't come out—that's a data roach motel. Open source technologies, and solutions built with open source tools, enable organizations to take control of their data, giving them the freedom to put it into and take it out of whatever databases or solutions they see fit.
  |  By InfluxData
InfluxData CEO, Evan Kaplan, talks about time series data workloads, how InfluxDB is purpose-built to support those workloads, and why that is so darn important.
  |  By InfluxData
Turn insights into action–in real-time–using your time series data. Now, more than ever, businesses generate massive amounts of time-stamped data. To get value from that data, you need to be able to ingest and query it in real-time. InfluxDB 3.0, built on innovative open source technology (Apache ecosystem), is the solution startups and enterprises use to achieve real-time insights.
  |  By InfluxData
InfluxData CEO, Evan Kaplan, sits down to talk about AI, how AI has become table stakes for modern software, and the role of time series data as a foundational component for building and training AI models.
  |  By InfluxData
InfluxData founder and CTO, Paul Dix, talks with CMO Brian Mullens about using InfluxDB 3.0 to bring real-time analytics to data lake and data warehouse architectures.
  |  By InfluxData
InfluxData Founder and CTO, Paul Dix, sits down to chat about real-time analytics, the role that time series data plays, and how a time series database complements data lakes and data warehouses for large workloads.
  |  By InfluxData
Paul Dix, founder and CTO of InfluxData, discusses how we built support for InfluxQL into the new InfluxDB 3.0, what the advantages of InfluxQL are, and how the broader open source ecosystem makes InfluxQL better.
  |  By InfluxData
InfluxData founder and CTO, Paul Dix, and VP of Product Marketing, Balaji Palani, talk about the product options available in InfluxDB 3.0 and what the ideal user for each one looks like, based on their data workloads.
  |  By InfluxData
High cardinality data presented a challenge to previous versions of InfluxDB, but InfluxDB 3.0 solved that problem. Influxers Jay Clifford and Zoe Steinkamp explain what cardinality is, why high cardinality impacts performance, and how InfluxDB 3.0 eliminates cardinality limits to open up new time series use cases.
  |  By InfluxData
InfluxData CEO, Evan Kaplan, sits down to discuss why we built InfluxDB 3.0, the key problems version 3.0 solves, and the new opportunities and use cases the advanced capabilities of InfluxDB 3.0 present to users.
  |  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.