In Part 1 of this series, we started to compare the uses of Kafka and MQTT within an IoT infrastructure. It was concluded that in a basic publish-and-subscribe model of an IoT device, Kafka might simply be overkill.
With the Kafka Summit fast approaching, I thought it was time to get my hands dirty and see what it’s all about. As an advocate for IoT, I heard about Kafka but was too embedded in protocols like MQTT to investigate further. For the uninitiated (like me) both protocols seem extremely similar if not almost competing. However, I have learned this is far from the case and actually, in many cases, they complement one another.
Keeping your systems secure is a never-ending challenge. Not only is it necessary to monitor and secure your own tech stack, but each new service a company uses creates another potential avenue for bad actors to try to exploit for their own ends.
The industrial revolution was a watershed period in human history. The shift from piecemeal, cottage-industry work to mechanized manufacturing transformed the way humans work. Since the 18th century, successive waves of innovation, such as the assembly line and the computer, continued to alter and change the nature of manufacturing. Today, we find ourselves in the midst of another industrial transformation.
Many Telegraf and InfluxDB users often spend a lot of time finding that perfect balance of getting the data they want in while not writing in too much data that they have to deal with unnecessary data in their database. This blog post will give you a better understanding of Telegraf’s data collection settings and help you fine-tune your configuration.
This article was originally published in The New Stack. As the technology landscape continues to change at a rapid pace, enterprise companies are in a rush to catch up and modernize their legacy IT and network infrastructure to capture the benefits of newly developed tools and best practices. By adopting modern DevOps techniques, they can reduce their operational costs, increase the reliability of their services and improve the overall speed and agility at which their IT teams are able to move.
The WP Engine platform provides brands the solutions they need to create remarkable sites and apps on WordPress that drive their business forward faster. It hosts over 1.5 million websites, serving over 175,000 customers in more than 150 different countries, and processes 5.2 billion requests per day. In total, WP Engine’s footprint comprises about 8 percent of the entire web.
Annual awards recognize companies based on workplace culture ratings from current employees SAN FRANCISCO, April 6, 2022 – InfluxData, creator of the leading time series platform InfluxDB, today announced it has won awards for Best Company Outlook and Best Engineering Team from Comparably, a leading workplace culture and corporate brand reputation platform.
As a data professional, you may come across some datasets with few independent variables (input variables). One variable would be time, and the other can be any sort of time-dependent column, such as the number of bookings in a hotel or the number of passengers on a flight.
If you’re a 1.x user of InfluxDB, you’re most likely more familiar with InfluxQL than you are with Flux. To gain a deep understanding of Flux, it’s important to understand: However, you can still use Flux without studying those topics. In this TL;DR, we’ll convert common InfluxQL queries into Flux and identify patterns between the two languages to help you get started using Flux more easily if you come from a InfluxQL or SQL background.
