In a previous post, we described a CSTR and a PID controller. This post will cover the code and architecture of the digital twin from this project repo. The project leverages Kafka for data streaming, Faust for data processing, InfluxDB for storing the time series data, and Telegraf for writing data from the topic to InfluxDB. We’ll also cover the advantages and disadvantages of this stack.

McKenzie Intelligence Services (MIS) is a company specializing in damage assessment post-disasters. Their platform, the Global Events Observer (GEO), offers comprehensive coverage of natural and manmade disasters worldwide, such as hurricanes, floods, civil unrest, and rioting. MIS provides an expert-assessed view of the world through analysts who analyze various imagery sources, including satellite and street-level imagery.

If you’ve ever worked with Apache Kafka, you know that it’s a powerful tool, but it can also be a bit finicky. Things can go wrong, and when they do, it’s important to know how to troubleshoot and resolve those issues quickly. Over the years, I’ve encountered my fair share of Kafka errors—some that had me scratching my head for days and others that were relatively straightforward once I knew what to look for.

SaaS services are one of the biggest drivers of OpEx (operating expenses) for modern businesses. With Gartner projecting $247.2 billion in global SaaS spending this year, it’s no wonder SaaS budgets are a big deal in the world of finance and IT. Efficient SaaS utilization can significantly affect both the bottom line and employee productivity.

Elasticsearch is primarily a search engine optimized for fast, complex search queries, especially text searches, and is often used for log and event data analysis. MongoDB, on the other hand, is a general-purpose, document-oriented database that excels in storing and retrieving structured and semi-structured data. It is commonly used for mobile, social, and IoT applications. While Elasticsearch provides superior search capabilities, MongoDB offers more robust data processing and storage features.

Microservices architectures have revolutionized software development, enabling scalability and flexibility. However, they also introduce complexities in system monitoring and troubleshooting. Effective logging is crucial for maintaining visibility and diagnosing issues in these distributed environments. This comprehensive guide explores best practices for microservices logging, helping you navigate the challenges and implement robust logging strategies.

Distributed tracing is becoming a critical component of any application's performance monitoring stack. However, setting it up in-house is an arduous task, and that's why many companies prefer outside tools. Jaeger and Zipkin are two popular open-source projects used for end-to-end distributed tracing. Let us explore their key differences in this article.

This blog explores the complexities of cloud monitoring, the limitations of native tools, and how ManageEngine Applications Manager can provide a comprehensive solution.

What keeps an SRE at the top of his game? I had an insightful conversation with Jon Duarte, a Site Reliability Engineer (SRE) at Mezmo and he walked me through his role and the various tasks he manages on a typical day. Here’s Jon offering a brief glimpse into the challenges he faces, the thought processes behind his approach, and the innovative solutions SREs come up with.

Splunk has launched a GenAI summary feature in splunk.com and docs.splunk.com search platforms designed to give users a quick and accurate glance of the most pertinent information they are looking for. This GenAI feature serves up a contextual high-level summary pulled from various relevant search results on topics ranging from Splunk product and feature usage to general Splunk terminology.