Grafana has been a staple visualization tool used alongside InfluxDB since its inception. With the release of InfluxDB Cloud powered by IOx, there is now a new way to integrate InfluxDB and Grafana: Flight SQL. Two of our engineers, Brett and Helen, have been working hard to create a new Grafana plugin called Flight SQL. This open-source plugin allows users to perform SQL queries directly against InfluxDB IOx and other storage engines compatible with Apache DataFusion.

Telecommunication organizations need to ensure they have the necessary resources and technology to maintain service uptime SLAs. Increased regulations and emerging technologies forced telecommunications companies to evolve quickly in recent years. These organizations’ engineers and site reliability engineering (SRE) teams must use technology to improve performance, reliability and service uptime.

Today is an important milestone for InfluxData. I am thrilled to share that we closed our $51 million Series E round, and a $30 million debt facility, raising $81 million in capital. The Series E round was led by our new investors Princeville Capital and Citi Ventures, with participation from our existing investors Battery Ventures, Mayfield Fund, Sapphire Ventures, Trinity Ventures, Norwest Venture Partners, Sorenson Capital, and Harmony Partners.

With a standard model for monitoring data across the organization, different teams can use a common infrastructure and extract maximum value from it. Monitoring (also sometimes referred to as observability) involves collecting and analyzing data from a source over time to track its health and/or performance. Because change occurs over time, virtually all monitoring data is time series data, meaning it has a timestamp.

This tutorial covers how to perform downsampling with the new InfluxDB storage engine, InfluxDB IOx, in InfluxDB Cloud (available on AWS us-east-1 and AWS eu-central-1 starting January 31st) using AWS Lambda. This tutorial describes how to: InfluxDB IOx addresses key user needs including (but not limited to): We achieved these goals by building InfluxDB IOx on the Apache ecosystem (Apache Parquet, Apache DataFusion, Apache Arrow, and Apache Flight SQL).

Back in October 2022, our Founder and CTO Paul Dix announced the limited release of InfluxDB IOx, our new database engine. After several months of beta testing, we’re excited to announce the next phase of our database engine: general availability. As of today, InfluxDB IOx releases to the rest of the world as the new and improved InfluxDB Cloud.

Some things are inevitable, like Thanos, paying taxes, and change. While it would be nice to simply snap our fingers and deliver new products, things aren’t so simple in the real world. InfluxDB has been the leading time series database since January 2016. But we’re not content to rest on our laurels. The quest to improve InfluxDB is constant and ongoing. As of today, we’re beginning the rollout of an all-new and improved InfluxDB Cloud powered by IOx.

Crosser is a Swedish company that builds a streaming analytics platform. The idea behind Crosser is to take the data from a connected, sensor-rich world and integrate it in real time to deliver faster insights and innovation. Primarily focused on the industrial IoT (IIoT) space, Crosser helps manufacturers gain insight into their machines and processes to drive improvements and to take advantage of newer trends and requirements that companies have for their data.

Accurate data is one of the most important aspects of any organizational function. It helps in decision-making and planning, and for most businesses, it also helps in generating revenue. The data can be anything from a list of clients and products to an inventory list. Nothing comes close to SQL timestamps regarding data accuracy, timeliness, and management. SQL Server timestamp is a critical component of relational databases, but they aren’t used on a daily basis by most database professionals.

This article was originally published in The New Stack and is reposted here with permission. A consequence of living in a rapidly changing society is that the state of all systems changes just as rapidly, and with that comes inconsistencies in operations. But what if you could foresee these inconsistencies? What if you could take a peek into the future? This is where time-series data can help.