We love to write and ship code to help developers bring their ideas and projects to life. That’s why we’re constantly working on improving our product to meet developers where they are, to ensure their happiness, and accelerate Time to Awesome. This week, we are covering a featured product release that we think will save you time and effort when onboarding to time series and InfluxDB.
This article was originally published in The New Stack and is reposted here with permission. You may be familiar with live examples of machine learning (ML) and deep learning (DL) technologies, like face recognition, optical character recognition OCR, the Python language translator, and natural language search (NLS). But now, DL and ML are working toward predicting things like the stock market, weather and credit fraud with astounding accuracy.
This article was written by Shane from Infosys. Infosys is a global IT Leader, headquartered in India, with over 200,000 employees and a focus on digital transformation, AI/ML, and Analytics. Our organization faces challenges when working with data to assist with proactive anomaly detection, triaging incidents to accommodate for data and volume growth, and maintaining high availability and SLA’s for a near 100% uptime.
Thanks to Vinay Kumar for being a key contributor to this article. We love to write and ship code to help developers bring their ideas and projects to life. That’s why we’re constantly working on improving our product to meet developers where they are, to ensure their happiness, and accelerate Time to Awesome. This week, we are covering a featured product release that we think will save you time and effort when building with time series, InfluxDB – and specifically – Tasks.
Cloud native and microservice architectures bring many advantages in terms of performance, scalability, and reliability, but one thing they can also bring is complexity. Having requests move between services can make debugging much more challenging and many of the past rules for monitoring applications don’t work well. This is made even more difficult by the fact that cloud services are inherently ephemeral, with containers constantly being spun up and spun down.
InfluxDB has over a dozen different client libraries to help developers work with time series data in whatever programming language they like best. The Python client library is one of our most popular options. It’s simple to learn, and working with InfluxDB in a language you’re comfortable with helps you get started doing powerful time series analysis quickly.
Commercial buildings produce 16% of carbon dioxide emissions in the US, and the EPA estimates that 30% of the energy used by these buildings is wasted. Energy efficiency in commercial buildings is a vital aspect of the transition to greener systems worldwide to fight climate change.
Joins are a common transformation in any query language, and as part of the effort to make Flux an increasingly valuable tool for our users, the engineers on InfluxData’s query team created, and continue to maintain, two separate join functions. And while these solutions have met some of our users’ needs, they both lack one key feature: support for outer joins.
User experience is the pinnacle of cloud technology. With cloud data centers handling 94 percent of all workloads, cloud optimization is vital. Users need fast, agile, scalable, and stable solutions over the long term. But how do you build these solutions? This is where cloud-native technology comes in. Cloud native computing provides the foundation for building, designing, running, and managing applications in the cloud.
We love to write and ship code to help developers bring their ideas and projects to life. That’s why we’re constantly working on improving our product to meet developers wherever they are, to ensure their happiness, and accelerate Time to Awesome. This is the third in a blog series covering our product’s latest features — features that we think will save you time and effort when building with time series and InfluxDB.
