Cloud Native / Data / Storage / Sponsored

Superior Monitoring with a Time-Series Database

18 Feb 2020 1:19pm, by and

InfluxData sponsored this podcast.

Time series data management continues to be used to back application deployments today across on-premises, and increasingly, cloud native environments. Whether it’s video streaming, real-time financial security data management, energy utility management or any application that requires time stamps for often very complex datasets at massive scales, time-series data will play an integral role. Today’s time series data platforms can typically be used for data analysis and forecasting by processing millions of data points per second.

Pricing has also become more affordable for a growing number of enterprises seeking high-powered data analysis as a way to distinguish themselves from the competition. These organizations also do not necessarily have the financial backing that the world’s largest financial institutions or Fortune 100 companies have at their disposal.

In this, The New Stack Makers podcast, Chris Churilo, responsible for technical product marketing at InfluxData, offers some background and perspective on why organizations increasingly rely on time series databases to “make products or services better.” Churilo also discussed why organizations are shifting their databases and management to cloud environments and why InfluxData recently extended to its InfluxDB Cloud 2.0 serverless time-series Platform as a Service (PaaS) to include Google Cloud Platform (GCP) as well as Amazon Web Services (AWS) cloud environments.

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“Time series data is useful for monitoring anything that you want to make improvements on,” Churilo said. “So, of course,  your cloud infrastructure is one thing that you definitely want to always be monitoring to make sure that you can provide the best service, especially if you have applications sitting on top of it that are customer-facing or even internally-facing — no one can tolerate having a slow application.”

However, while time-series datasets have been in use for “many, many years,” not ‘everyone has understood the name ‘time-series data,'” Churilo said. “So, I think now people are starting to become more familiar with that,” Churilo said.

What Churilo finds “fascinating” is how many talented development teams are more than capable of designing and managing their own time-series databases. However, many also consider the opportunities the cloud can offer as they “look at the work that they have to do for their own product and realize that that’s probably more important and how it’s going to be more of a differentiator for them to focus on their products,” Churilo said. “And, so they often look for a data store for this time-series data. And having it in the cloud makes sense for them because they don’t have to build and manage it.”

DevOps teams sometimes adopt open source projects they “put it into their own infrastructure, but then they realize that’s also taking time from their busy schedules,” Churilo said. “They don’t want to have to manage it. They don’t want to have to keep the software up to date have to optimize the environment constantly,” Churilo said. “So, they quickly ask us, ‘hey, do you have something that’s already available in a managed form’ and that’s why InfluxDB Cloud 2.0 is so popular.”

A shift to take advantage of a cloud infrastructure for time series data management can overlap with other monitoring capabilities. The Cloud Native Computing Foundation‘s Prometheus, for example, the OS monitoring platform SoundCloud pioneered, relies heavily on time-series databases.

It also became readily apparent that “we that we couldn’t just stick with AWS” as a cloud alternative for InfluxDB Cloud 2.0 Churilo said. This is why InfluxData recently extended InfluxDB Cloud 2.0 to run on Google Cloud and why the company is “working on also offering it for Microsoft Azure,” Churilo said.

The Cloud Native Computing Foundation is a sponsor of The New Stack.

Image by Yan Wong from Pixabay.

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