These are heady times for InfluxDB, which is the world's most popular time-series database, which has been the fastest growing category of databases the past two years, per DB-Engines.com. But when Paul Dix and his partner founded it a decade ago, the company behind the time-series database and the product itself and looked much different. In fact, InfluxDB went through several transformations to get to where it is today, mirroring the evolution of the time-series database category. And more change appears on the horizon. Dix and Todd Persen co-founded Errplane, the predecessor to InfluxData, back in June 2012 with the idea of building a SaaS metrics and monitoring platform, à la Datadog or New Relic.

Time-series analysis has been studied for more than a hundred years, however, the extraordinary growth of data available from numerous sources and more frequent growth of data alongside the growth of computer power (GPU & Multicore) makes the analysis of large-scale time-series data possible today in a way that was not previously practical. The use of time-series data has been traditionally linked to sectors where time is not just a metric but a primary axis, such as in finance, Industrial IoT, and energy. However, in the last 10 years, it is starting to be generally used in other sectors such as marketing, gambling, or any other sector where performance monitoring and time-series analysis is needed. There are three main solutions in the ecosystem to treat, analyze, and visualize time-series data. These are Time-series Databases, Time-Series Data Analytics Solutions, and Machine Learning Platforms.

Like all good superheroes, every company has its own origin story explaining why they were created and how they grew over time. This article covers the origin story of QuestDB and frames it with an introduction to time series databases to show where we sit in that landscape today. Time series is a succession of data points ordered by time. These data points could be a succession of events from an application's users, the state of CPU and memory usage over time, financial trades recorded every microsecond, or sensors from a car emitting data about the vehicle acceleration and velocity. For that reason, time-series is synonymous with large amounts of data.

The challenges encountered by manufacturing companies when it comes to handling data are well reported, but what can they do to ensure that data is an asset rather than a problem? Data has long been treated in the manufacturing industry as the orphan nephew living in the cupboard under the stairs. While operational and service industries have leapt on the benefits of data as the catalyst of business growth and efficiency gains, the manufacturing sector has been slow to adopt the culture of becoming a data-driven business. According to Accenture, only 13 per cent of manufacturing companies have seen through a digital transformation of their processes. "In many ways the core approach to manufacturing has remained unchanged for the past 50 years despite the industry experimenting with offshoring and integrated manufacturing in mega factories," Tim Hall, VP products, InfluxData, says.

Hands-on tips for companies to build Machine Learning Products that are being adopted by their users and customers. The biggest difficulty for products based on machine learning (ML) will be user or customer adoption. How did I come to this conclusion? A top executive of one of the biggest European insurance companies told me: "We have the money and technical talent to build sophisticated ML-products, but we do not know how to make users adopt those products. We spent millions of dollars on an ML-based app but only got around 300 users. We do not understand why people do not want to use our app."

Myths around the challenges of implementing IIoT systems and building smart factories have made the prospect of adoption unnecessarily intimidating. In some cases, industrial organizations have avoided the most effective IIoT implementations available to them simply due to false understandings of the technology. While IIoT adoption does require a new approach to managing and analyzing data collected in real-time, this isn't as difficult an obstacle as many have been led to believe. Let's take a look at four common myths about IIoT systems and the realities behind them: The traditional databases that most industrial organizations already have in place (Microsoft SQL Server, Oracle, etc.) are wholly inappropriate for use with IIoT systems, given the tremendous volume and complexity of data in question. When industrial businesses mistakenly implement IIoT infrastructures using traditional databases (and this happens often), they soon discover them to be expensive to scale, unable to process the vast amount of incoming data, or incapable of handling the more complex queries required to realize the IIoT's benefits.