Operations | Monitoring | ITSM | DevOps | Cloud

Our own Chief Revenue Officer, Todd McNabb, recently sat down with Melissa Graham, Sr. Vice President of Global Sales at SHI International, to discuss our new partnership.

Rebuilt and reimagined storage engine built on open source project InfluxDB IOx delivers faster queries, unlimited time series, and introduces SQL for writing queries and BI tool support.

Two years ago I announced that InfluxData was working on a new core for InfluxDB, a project we named InfluxDB IOx. InfluxDB IOx is a cloud-native, real-time, columnar database optimized for time series data built in Rust on top of Apache Arrow and DataFusion. Today I’m excited to announce that we deployed our next-generation storage engine that’s built on InfluxDB IOx in our InfluxDB Cloud platform.

In the first post of this series, I detailed ways companies considering cloud adoption can achieve quick wins in performance and cost savings. While these benefits of the cloud certainly remain true in theory, realizing these benefits in practice can be increasingly difficult as applications and their networks become more complex.

As long as humans have written software, we’ve needed to understand why our expectations (the logic we thought we wrote) don’t match reality (the logic being executed). To that end, we developed techniques to help measure reality—logging text strings, or capturing aggregated metrics—and persevered, seeking out newer and fancier logging or monitoring solutions over the intervening decades.

Although Java has been around for 27 years, enterprise applications still favor it as one of their preferred platforms. Java's functionality and programming flexibility increased concurrently with technological advancement, keeping it a useful language for more than 25 years. Outstanding examples of this progression include new garbage collection algorithms and memory management systems.

Organizations of all sizes rely on their observability data to drive critical business decisions. Production Engineers across Development, ITOps, and Security use it to understand their systems better, respond to issues faster, and ultimately provide more performant and secure user experiences. But while the value of observability data is well understood, teams struggle to derive value from it.

How enterprises store and split up observability and security data is a great analogy to how lint, spare change, and partially-eaten bags of popcorn end up under couch cushions. Or when you tell your kids to clean up the house when company is coming over and they stash their toys and your tools in various nooks and crannies.

My father worked with some of the very first computers ever imported to Italy. It was a time when a technician was a temple of excellence built on three pillars: on-the-field experience, a bag of technical manuals, and a fully-stocked toolbox. It was not uncommon that missing the right manual or the correct replacement part turned into a day-long trip from the customers’ site to headquarters and back.