A wide variety of industrial processes rely on water, and before it can be used, it needs to be treated to remove dissolved substances. Minerals have to be filtered out so they don’t form scales on equipment as water is heated and cooled, and bacteria needs to be removed in cases involving human health. Ekopak is a Belgian company working to make water treatment more sustainable by using less water and energy where possible.

In this tutorial we’ll learn how to use Python to get time series data from the OpenWeatherMap API and convert it to a Pandas DataFrame. Next we’ll write that data to InfluxDB, a time-series data platform, with the InfluxDB Python Client. We’ll convert the JSON response from our API call to a Pandas DataFrame because I find that that’s the easiest way to write data to InfluxDB.

Whether you’re using InfluxDB to record massive amounts of historical stock market data to analyze the current economic trends or simply to monitor the number of times the lights in your smart home turn on and off to cut down on wasted electricity, a sudden shock or delay in the flow of incoming data can be detrimental to your operation in the majority of scenarios.

Given the global health crises the world has faced over the last few years, the need for expeditious but accurate medical trials has never been more important. The faster clinical trial data is validated, the faster medicines get approved and treatments become available. Pinnacle 21’s customers are driving forces behind creating life-saving treatments.

Efficient manufacturing is important for saving companies time, money, and energy. Making decisions based on data can improve efficiency, but there’s a lot of data to sort through. Manufacturing equipment contains many sensors, especially in the IIoT space. Overall Equipment Effectiveness (OEE) was first described by Seiichi Nakajima in the mid-twentieth century as part of his Total Productive Maintenance (TPM) method.

Telegraf is a very powerful open source plugin-based agent that gathers data from stacks, sensors, and systems and sends it to a database. It collects data from an input and sends it to an output, and gives you the option to transform data with aggregators and processors before it reaches its endpoint.

Streaming time series data from brokers and services that are on-premises or in the cloud to a cloud-based database is a resource-intensive process requiring third-party software and heavy customizations. Today we’re announcing InfluxDB Native Collectors to make it easy for developers to collect, process, and analyze data by subscribing directly to supported message brokers.

SAN FRANCISCO — August 23, 2022 – InfluxData, creator of the leading time series platform InfluxDB, today announced new serverless capabilities to expedite time series data collection, processing, and storage in InfluxDB Cloud. InfluxDB Native Collectors enable developers building with InfluxDB Cloud to subscribe to, process, transform, and store real-time data from messaging and other public and private brokers and queues with a click of a button.

Today we are happy to officially announce that InfluxData has donated a generic object store implementation to the Apache Arrow project. Using this crate, the same code can easily interact with AWS S3, Azure Blob Storage, Google Cloud Storage, local files, memory, and more by a simple runtime configuration change. You can find the latest release on crates.io. We expect this will accelerate the pace of innovation within the Rust ecosystem.

InfluxDB is an open-source time series database. Built to handle enormous volumes of time-stamped data produced from IoT devices to enterprise applications. As data sources for InfluxDB can exist in many different situations and scenarios, providing different ways to get data into InfluxDB is essential. The InfluxDB client libraries are language-specific packages that integrate with the InfluxDB v2 API. These libraries give users a powerful method of sending, querying, and managing InfluxDB.

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.