This blog post explains how to read a query plan in InfluxDB 3.0 and requires basic knowledge of InfluxDB 3.0 System Architecture. InfluxDB 3.0 supports two query languages: SQL and InfluxQL. The database executes a query written in either SQL or InfluxQL according to the instructions of a query plan.

Yes, the title says it all: Grafana released the official V3 plugin for InfluxDB Data Source! Before delving into the tutorial, we’d like to thank Ismail Simsek, a Tech Lead at Grafana. Ismail was pivotal in adding the V3 SQL plugin to the InfluxDB data source and making significant backend code improvements. To clarify, this release isn’t an entirely new data source.

Query performance is critical in any database. Data partitioning is a mechanism that helps prune unnecessary data, allowing queries to run faster. However, there are always trade-offs between large and small numbers of partitions. For instance, fine-grained partitioning on high cardinality columns can reduce performance. This post describes different partitioning schemes supported by InfluxDB 3.0 and explains their trade-offs.

Imagine a bustling city with millions of people going about their daily lives. Now, picture a network of interconnected roads, each representing a data point, capturing the pulse of the city in real-time. This is the essence of data lakes and data warehouses, where vast amounts of information flow in and out, shaping the decisions that drive businesses forward. However, to harness the power of these architectures, real-time analytics is essential.

InfluxDB, the leading time series database, and AWS, the leading web services vendor, have a long-standing partnership. InfluxDB has been available as a SaaS product on AWS for many years. And as InfluxDB has grown and matured, most notably with the release of InfluxDB 3.0 this year, so has our partnership with AWS. That’s why we’re excited to announce that InfluxData achieved AWS Data and Analytics Competency status in the Data Analytics Platforms and NoSQL/New SQL categories.

In this post, we’ll be taking a closer look at Home Assistant, an open source platform for connecting your smart devices at home. We’ll walk through every important section of Home Assistant: dashboards, integrations, add-ons, devices and entities, automation, scripts, and scenes. In addition, we’ll be walking through how to set up your Home Assistant and create automation using Home Assistant’s graphical user interface.

Time difference analysis is a method of analyzing data points at regular time intervals over a set period. However, in time series analysis, we derive crucial information such as the variance of the variables among data points over a period of time. This gives additional information on how the data adapts over time. This can be used to analyze data during different trends at different time intervals.

Just in time for your holiday viewing! Learn how to solve real-time time series processing challenges with Quix—the stream processing framework using Kafka and Python—and purpose-built time series database InfluxDB.

Cybersecurity has become something of great concern to the IT world over the last few years. The increase in remote working and the move to cloud-based and e-commerce solutions have opened up many new opportunities for cyber exploits and malware attacks. According to the FBI, the incidence of malware and network attacks, particularly ransomware, on both commercial and home systems has shown a major increase. At a corporate and personal level, ID theft has also increased greatly.

Recently, InfluxData CEO Evan Kaplan sat down with Developer Advocate Jay Clifford to discuss the role of time series data and AI in industry, how it’s evolving, and specifically, the role of time series data in AI. They also discussed the future of InfluxDB in terms of real-time analytics and its role in the AI landscape.