For most of the past year, we here at InfluxData focused on shipping the latest version of InfluxDB. To date, we launched three commercial products (InfluxDB Cloud Serverless, InfluxDB Cloud Dedicated, and InfluxDB Clustered), with more open source options on the way. All the while, we claimed that this latest version of InfluxDB surpasses anything we built before.
With over 500,000 residential, business, and wholesale customers across multiple markets in the United States, WideOpenWest (WOW!) is one of the United States’ largest broadband providers. They aim to connect homes and businesses to the world with fast and reliable internet, TV, and phone services.
Industrial IoT (IIoT) machines and sensors generate valuable time series data. It’s impossible to derive the insights necessary to inform decisions as a company to produce or operate more efficiently without sending operational technology (OT) data to informational technology (IT) systems.
InfluxDB v3 allows users to write data at a rate of 4.3 million points per second. However, an incredibly fast ingest rate like this is meaningless without the ability to query that data. Apache DataFusion is an “extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.” It enables 5–25x faster query responses across a broad range of query types compared to previous versions of InfluxDB that didn’t use the Apache ecosystem.
This article coins the term “FDAP stack”, explains why we used it to build InfluxDB 3.0, and argues that it will enable and power a generation of analytics applications in the same way that the LAMP stack enabled and powered a generation of interactive websites (by the way we are hiring!).
Imagine, if you will, having hundreds of devices that you need to monitor. All these devices generate data at sub-second intervals, and you need all that high fidelity data for historical analysis to feed machine learning models. Storing all that data can get really expensive, really fast. When that happens, you must decide what’s more important: keeping all your data or sacrificing insights and analysis. It may not be a big stretch of the imagination for many readers.
With our plans for InfluxDB 3.0 OSS laid out, both myself and the rest of the DevRel team have been actively searching for ecosystem platforms that would be logical integrations for the future of InfluxDB. One of these platforms is Quix! Quix is a comprehensive solution tailored for crafting, launching, and overseeing event streaming applications using Python. If you’re looking to sift through time series or event data in real-time for instant decision-making, Quix is your go-to.
With the smart home revolution in full swing, choosing the proper hardware for platforms like Home Assistant can be overwhelming. Whether you’re new to home automation or a seasoned pro, the hardware you select can make or break your experience. But fear not! This comprehensive guide will demystify the requirements, delve into the various options, and help you make an informed decision. From the compact Raspberry Pi to the powerful Intel NUC, we’ve got you covered.
In the field of big data analytics, stream processing has emerged as a crucial paradigm, reshaping how businesses interact with data. But what is stream processing and why are more businesses using it?
This article was originally published on The New Stack and is reposted here with permission. By taking advantage of monitoring data, companies can ensure their infrastructure is performing optimally while reducing costs. While building new features and launching new products is fun, none of it matters if your software isn’t reliable. One key part of making sure your apps run smoothly is having robust infrastructure monitoring in place.
