Q&A with Marek Tihkan, CTO at Dashbird: Leading and managing a Developer team

As we enter into our 4th year, we've decided to get up close and personal with our team to share with you their passion, drivers, lessons learned and significant moments of the past year. We're a young company dedicated to adding value in all corners that we reach, so we hope you find the upcoming series useful! Hey Marek, so can you tell us how long you’ve been at Dashbird and where you were before? M: I’ve been at Dashbird for two years now.


How We Use Quarkus With Kafka in Our Multi-Tenant SaaS Architecture

At LogicMonitor, we deal primarily with large quantities of time series data. Our backend infrastructure processes billions of metrics, events, and configurations daily. In previous blogs, we discussed our transition from monolith to microservice. We also explained why we chose Quarkus as our microservices framework for our Java-based microservices. In this blog we will cover.


The Ultimate Guide to Microservices Logging

Microservice architecture is widely popular. The ease of building and maintaining apps, scaling CI/CD pipelines, as well as the flexibility it offers when it comes to pivoting technologies are some of the main reasons companies like Uber and Netflix are all in on this approach. As the amount of services in a microservice architecture rises, complexity naturally also rises.

How Farmer's Fridge Scales its Microservices with Observability from Epsagon

For retailers, core business changes and fluctuations in demand significantly impact site and application performance, customer experience, and inventory management. All retailers, and especially those that are e-commerce-based with modern microservices infrastructures, need visibility into their environments. They need to understand how their systems are operating and quickly identify and address issues.

Observability For Your Microservices Using Kong, Kubernetes, and Prometheus

In this video, Kevin Chen, Developer Advocate at Kong, will explain how to set up Prometheus monitoring with Kong Gateway to get black box metrics and observability for all of your services deployed on Kubernetes. This guide can also be applied to other solutions like StatsD, Datadog, Graphite, InfluxDB etc.

Deploying AWS Microservices

There has been increasing buzz in the past decade about the benefits of using a microservice architecture. Let’s explore what microservices are and are not, as well as contrast them with traditional monolithic applications. We’ll discuss the benefits of using a microservices-based architecture and the effort and planning that are required to transition from a monolithic architecture to a microservices architecture.


Microservices vs. Service Oriented Architecture (SOA)

Technology has a way of circling around to the same ideas over time, but with different approaches that learn from previous iterations. Service Oriented Architecture (SOA) and Microservices Architecture (MSA) are such evolutionary approaches. Where lessons learned made sense, they were reused; and where painful lessons were learned, new methods and ideas were introduced.

Containers, Microservices, and Kubernetes

Faster application development requires more agile application infrastructure. Containers started the transformation of modern application architectures which now are dominated by microservices running on Kubernetes. In this episode of Dissecting DevOps find out how cloud infrastructure has changed, how the modern architectures make application development easier, and the unique challenges introduced by microservices and Kubernetes.

How We Used JMH to Benchmark Our Microservices Pipeline

At LogicMonitor, we are continuously improving our platform with regards to performance and scalability. One of the key features of the LogicMonitor platform is the capability of post-processing the data returned by monitored systems using data not available in the raw output, i.e. complex datapoints. As complex datapoints are computed by LogicMonitor itself after raw data collection, it is one of the most computationally intensive parts of LogicMonitor’s metrics processing pipeline.