If we turn the clocks back to September 2013, we released InfluxQL alongside InfluxDB. InfluxQL is a SQL-like query language, specifically designed to query time series data. For many of our users, InfluxQL still remains the primary way they interact with InfluxDB. Based on this feedback, InfluxQL has been reborn in InfluxDB 3.0 alongside native support for the SQL query language. So what do I mean by reborn?

We recently introduced a new version of InfluxDB, rewritten from the ground up to improve performance across the board. As with any undertaking of this nature, developers will need to make some adjustments to their applications in order to incorporate the new database. We even faced this challenge internally. We had many Telegraf instances sending data to legacy versions of InfluxDB.

This post was written by Pete Osah, a software developer who is familiar with web technologies, passionate about new software technologies, and keen on developing ways to pass knowledge to others in a simple manner. Thanks to new technologies, developers can release software and features to production at a faster pace and with greater efficiency. But maintaining software dependability and integrity requires having the necessary tools in place.

InfluxDB 3.0 (previously known as InfluxDB IOx) is a (cloud) scalable database that offers high performance for both data loading and querying, and focuses on time series use cases. This article describes the system architecture of the database. Figure 1 shows the architecture of InfluxDB 3.0 that includes four major components and two main storages.

InfluxDB Cloud 3.0 is a versatile time series database built on top of the Apache ecosystem. You can query InfluxDB Cloud with the Apache Arrow Flight SQL interface, which provides SQL support for working with time series data. In this tutorial, we will walk through the process of querying InfluxDB Cloud with Flight SQL, using C++. The C++ Flight SQL Client is part of Apache Arrow Flight, a framework for building high-performance data services.

InfluxDB 3.0 now offers support for connecting Tableau to InfluxDB 3.0 to query data for visualization using the Apache Arrow Flight SQL JDBC driver (Flight SQL driver). In this blog post, we will explore the capabilities and benefits of this integration and provide some instructions on how to connect them.

Telegraf is an open-source plugin-driven agent for collecting, processing, aggregating, and writing time series data. When collecting metrics it is common to filter out or pass through metrics with specific names, tags, fields, or timestamp values. The Common Expression Language (CEL) is an open-source language that provides a set of semantics for expression evaluation.

Open source Zipkin offers a robust set of features that make it easier for developers to understand and optimize complex distributed systems. Distributed tracing is a technique you can use to trace and monitor requests propagating through a distributed system. It can work in environments where multiple services process a request, making it an essential tool for modern microservices architectures. Zipkin is an open source distributed tracing system for monitoring and troubleshooting complex systems.

InfluxDB 3.0 is a versatile time series database built on top of the Apache ecosystem. The 3.0 product suite includes two cloud-based versions: InfluxDB Cloud Serverless, and InfluxDB Cloud Dedicated. For the purposes of this post, InfluxDB Cloud refers to these specific versions of InfluxDB. This post provides an update on the status of the client libraries for InfluxDB Cloud, as well as all the available resources to get started querying and writing data to InfluxDB.

Telegraf is an open source plugin-driven agent for collecting, processing, aggregating, and writing time series data. Telegraf relies on user-provided configuration files to define the various plugins and flow of this data. These configurations may require secrets or other sensitive data. The new secret store plugin type allows a user to store secrets and reference those secrets in their Telegraf configuration file.

Recently, I wrote an article discussing why industrial organizations should migrate from legacy data historians to modern, open source technologies. The reasons for such a migration remain valid; however, it dawned on me that such a heavy-handed approach is not always right for every organization.

The modern factory’s relationship with data is experiencing a major change. Data now shapes the future rather than only telling the story of the past. The language inside the factory sounds like higher Overall Equipment Effectiveness (OEE) as the result of a shift from preventive to predictive maintenance. It could also look like expanding business goals to a new market based on impactful data-driven decisions. A change in purpose requires an update in technology.

InfluxDB Cloud Dedicated is a hosted and managed InfluxDB Cloud cluster dedicated to a single tenant. The InfluxDB time series platform is designed to handle high write and query loads so you can use and leverage InfluxDB Cloud Dedicated for your specific time series use case. In this tutorial, we walk through the process of reading data from InfluxDB Cloud Dedicated using the Java Flight SQL client.

InfluxDB Cloud 3.0 is a versatile time series database built on top of the Apache ecosystem. You can query InfluxDB Cloud with the Apache Arrow Flight SQL interface, which provides SQL support for working with time series data. In this tutorial, we will walk through the process of querying InfluxDB Cloud with Flight SQL, using Go. The Go Flight SQL Client is part of Apache Arrow Flight, a framework for building high-performance data services.

The concept of observability centers around collecting data from all parts of the system to provide a unified view of the software at large. Fault tolerance, no single point of failure and redundancy are prominent design principles in modern software systems. But that doesn’t mean errors, degradation, bugs or even the occasional catastrophe don’t happen.