We’ve got some outstanding news to share in the arena of security partnerships: Logz.io® Cloud-based SIEM has officially achieved Amazon Web Services (AWS) Security Competency! This designation within the Logging, Monitoring, SIEM, Threat Detection, and Analytics category further demonstrates Logz.io’s proven commitment to delivering best-in-class security.

Welcome to the second post in a series of “How to Scale your AWS Infrastructure”. In the first post, we talked about horizontal scaling, autoscaling, CI/CD, infrastructure automation, containerization, etc. In this post, we will continue the discussion around databases, loose coupling, caching, CDN, etc. Let’s start the discussion with database scaling.

As we push more data to the cloud, avoidable mistakes are hampering migration. The biggest culprit: messy data with inadequate security and integration.Data transfers seem to be the easiest part of cloud migration. After all, migrating applications is the biggest pain in the neck. Data replication and migration should be simple, something that’s done during the last step of the application and data migration process. Right?

If you have worked in any part of the application delivery lifecycle long enough, there’s a very good chance you have directly used or been a consumer of Nagios. For a period of time in the early 2000s, it was the go-to solution for basic server monitoring and alerts. Fast forward 20 years and you might be surprised how many organizations still rely on Nagios for mission critical workloads — although not without a fair amount of duct tape and super glue challenges.

When designing a solution, you should keep future needs in mind. If the number of users increases dramatically in a short period of time, the solution should be scalable enough to handle the new growth. Making systems scalable on cloud is relatively easier as compared to scaling on-premises infrastructure. AWS has provided excellent tools/services to enable your applications for as much scalability as you want.

As a framework for goal-setting, the Objectives and Key Results (OKRs) methodology is an incredibly useful tool when implemented properly. It can help your engineering team better plan and stay aligned toward common objectives through the duration of the development process. On an individual level, OKRs can also encourage each team member to make personal progress toward their own goals or common team goals that would benefit the company.

Jupyter Notebook is a web-based interactive computational environment for creating notebook documents. It supports programming languages – such as Python, Scala, R – and is largely used for data engineering, data analysis, machine learning, and further interactive, exploratory computing. Think of notebooks like a developer console or terminal, but with an intuitive UI that allows for efficient iteration, debugging or exploration.

Are you surprised? Are you under where are the first four tips? I start this series of blog posts on my blog, and you can see and read the previous Best practices, Tips, and Tricks here: And I will be sharing some of them here and others on my blog. So stay tuned for both blogs. Of course, the most recurring task is adding comments to our triggers and actions, but it is always good to know you to delete them. Some of you may be thinking that is a trivial task, simple like adding a comment.

Heroku is a platform for deploying, running, and managing applications, which is written in a variety of programming languages, including Python, Java, C#, JavaScript, PHP, and others. Heroku's goal is to free you up to focus on your applications rather than infrastructure management. Logging is usually included in infrastructure management. Heroku provides a high-level log maintenance tool. In this Heroku logging article, we'll learn how to get the most out of Heroku logs.