Performance Engineering & DevOps Virtual Conference – Feb 25th

Segue anúncio da Conferência virtual promovida pelo CMG Internacional na próxima quinta-feira.

CMG will host industry experts for keynote presentations and technical demonstrations designed to help companies understand the why, when, and how to integrate performance engineering and performance testing into their DevOps practice.

Check out the agenda and register prior to the event for access to the live event and session recordings for 1 year, here

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On February 25, CMG will host industry experts for keynote presentations and technical demonstrations designed to help companies understand the why, when, and how to integrate performance engineering and performance testing into their DevOps practice.

The conference kicks off at 9:30 am eastern time (11:30 in Brazil) with peer to peer live networking followed by the sessions, with Q&A and networking time in-between. Join us for these informative sessions and interactive time with fellow industry practitioners. 

Newly Added Session

Optimize for Performance using MATLAB Profiler with Flame Graph

See a walk through of the redesigned MATLAB® Profiler, now featuring a flame graph. Use the Profiler to measure the time it takes to run your code and identify opportunities for performance optimization.

Presented by


Sindhuja works as a Performance Engineer at MathWorks and is involved in diverse activities concerning speeding up customer workflows. She is passionate about performance pipeline optimization, profiling tools, data analysis, application of artificial intelligence to improve product’s performance and about the ethical implications associated with AI in the world of technology.


 Neeraj has been a Performance Engineer at MathWorks for past 6 years working to speed up MATLAB by improving MATLAB Startup and technical computing workflows. In his current role, he works on the development of performance tools to improve MATLAB users workflow.

More sessions


In 2020, Andi helped several teams analyze performance and architectural issues in their distributed applications. In this session, he will present a handful of the common patterns we found – such as N+1 Call & Query, Too Granular or Tight Coupling, and Inefficient Dependencies. He will then show you how to derive SLIs & SLOs out of these patterns and have them automatically detected using the CNCF open source project Keptn as part of your DevOps process automation.


Andi Grabner
Andreas Grabner has been a developer, tester, architect, and product evangelist for the past 18 year. In his current role he helps companies injecting metrics into the end‐to‐ end delivery pipeline to make better decisions on feature and code changes and with that also closing the feedback  loops between Ops, Biz and AppDev. 


Parker Edwards
As Manager, Solutions Engineering at Lightstep, Edwards leads the Solutions Engineering group at Lightstep. A long time monitoring and observability enthusiast, Parker works with our customers to design, implement, and improve modern, scalable observability practices using Lightstep and OpenTelemetry.


Slow dashboards got you down? With our latest release of Change Intelligence, the Lightstep platform can do wonders for you and your team’s performance (not to mention, your sanity). Join us as we dive into change intelligence – the easiest way for DevOps and SRE teams to understand complex systems. Change intelligence is a means of monitoring for and diagnosing anomalies in complex modern architectures, and can deliver the answer to the question What caused that change?


This talk introduces OpenTelemetry to developers and operators. First, learn about each OpenTelemetry component, the core concepts, and how they all fit together to provide flexible and robust observability. Then learn the easiest way to set up and deploy OpenTelemetry across your entire system.


Austin Parker
Austin Parker is an Open Source Software Engineer at LightStep, where he works as a core contributor and maintainer to the OpenTracing project. Prior to LightStep, he was a Software Architect at Apprenda building enterprise platforms using Kubernetes. Google Books



Join the conversation with CMG on Slack.

Scaling Multi-Cloud with Infrastructure as a Code por André Rocha Agostinho (SindicoNet/Magnadev) – Nova versão da apresentação revista e ampliada

Na era do DevOps, a operacionalização de serviços na nuvem, cada vez mais, vem sendo automatizada para atender demandas emergentes de negócios a qual exige resposta rápida à mudanças e capacidade em se escalar. Automatizações como CI/CD (Continuous Integration e Continuous Development) permitem em grande parte atender cenários diversos onde é necessário reduzir ou simplesmente liquidar operações manuais de Deployment por meio de passos automatizados intermediados por um agente robô. Em contrapartida, existe a necessidade em se ter o mínimo necessário de infraestrutura como pré-requisito, o que obriga equipes a investirem tempo e esforço na criação desses ambientes, nos quais, em alguns casos, a complexidade é multiplicada pelo uso de serviços distintos de computação na nuvem, a Multi-Cloud. O termo “Infrastructure as a Code” é um assunto emergente o qual trata infraestrutura como código versionado, um asset do projeto onde o seu objetivo não é apenas reduzir esforço operacional mas também poder compartilhar conhecimento e engajar membros de equipes. Esta apresentação tem como objetivo introduzir “Infrastruscture as a Code” assim como o seu potencial para cenários Multi-Cloud.

Cloud Continuous Integration –  A distributed approach using distinct services

André Rocha Agostinho –

Em serviços de computação na nuvem a capacidade em compartilhar e disponibilizar serviços, escalar recursos computacionais e distribuir armazenamento de dados e arquivos exige um processo de implantação alinhado à agilidade e escalabilidade. Na era do “DevOps” a integração contínua possibilita um processo automatizado com o objetivo de reduzir o esforço operacional de equipes de desenvolvimento que se empenham em equilibrar entregas com qualidade e reduzir o “Time-to-Market”. Com o crescente aumento de serviços distintos de computação na nuvem, a integração contínua necessita atender diferentes plataformas, o que torna o processo de implantação ainda mais complexo. Este artigo tem como proposta demonstrar uma abordagem de integração contínua distribuída para diferentes tipos serviços de computação na nuvem cobrindo desde a configuração do processo à apresentação dos resultados em ambiente teste.


Architecture performance using micro services

José Junior Santana –

Implementing an inadequate architecture can lead to multiple performance problems, capacity, and unnecessary resource allocation. In this sense, the objective is to present how the use of an architecture oriented to micro services is able to meet diverse needs, from the use in “small projects” to large projects, with a very complex infrastructure. This type of architecture was implemented and tested in the “Predictor” system, and its results in terms of performance, integration with the DevOps methodology and ease of allocation of infrastructure resources show the benefits of it. With this, it was possible to verify how “vulnerable” applications can be if the architectural modeling is ignored or not respected by developers. The great challenge, however, is to avoid duplication of code and also the control of generated artifacts that require great attention and control.

Key words: micro services, DevOps, Predictor, architeture