Operations | Monitoring | ITSM | DevOps | Cloud

The Grafanaverse just got a little bit bigger. Today at its annual Build conference, Microsoft introduced Azure Monitor dashboards with Grafana, a new service that provides Azure users with Grafana dashboards natively integrated in the Azure Portal at no additional cost and with little administrative overhead required.

Cloud computing empowers organizations to access IT resources on demand, over the Internet, and on a pay-per-use basis. Thus, your company does not need to purchase, install, operate, and upgrade hardware for physical data centers. Instead, you can rent resources as needed from cloud service providers such as Amazon Web Services (AWS). For instance, AWS provides compute, storage, database, networking, machine learning, data lake, analytics, security, and IoT resources/services.

Imagine you’re running a popular online learning platform that experiences a surge in traffic during peak hours, right before exams. Students worldwide are logging in simultaneously, watching videos, submitting assignments, and taking tests. If your Oracle Cloud Load Balancer isn’t distributing traffic efficiently or back-end servers are struggling to keep up, students could face slow loading times or service outages.

Heroku and AWS offer distinct benefits for startups and mid-size companies. This guide compares pricing, scalability, security, and developer experience to help you choose the right cloud platform based on your team’s needs and growth goals.

Starting on Aug. 1, 2025, AWS will bill for the initialization (INIT) phase of Lambda functions, bringing a key change to how you are charged for serverless workloads. This billing update will impact functions using managed runtimes with ZIP archive packaging, which previously excluded the INIT phase from the billed duration. For teams that rely heavily on AWS Lambda, this is a small but significant change. The INIT phase, while short, could introduce costs that were previously invisible.

As your applications scale across cloud and SaaS providers, allocating costs and optimizing workloads become increasingly important—and challenging. Without access to cost data in their daily workflows, engineering teams can’t easily understand the cost of their resources and identify where they can reduce their spend. And while FinOps teams have access to cost data, they often review this information in silos.

As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost. But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game. Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks.

Have you ever googled yourself out of curiosity, and found out way more information than you thought? That’s because many companies, social media outlets, and apps, create a digital footprint of you anytime you visit a website or service. What is a digital footprint, you ask? It’s the data you leave behind whenever you visit a page online.