We’re excited to release version 7 of the Datadog Agent. It has all of the same functionality as Agent 6, but it is the first version to ship with only the Python 3 runtime. With Python 2 reaching its end of life on January 1, 2020, migrating your services to Python 3 will ensure that they continue working as expected. We’ve tested all of our more than 350 integrations to ensure they work with Python 3.
Data pipelines and analytics tooling have become essential parts of modern businesses, so why are error monitoring and observability tooling for data tools still 10 years behind modern application development? Logs are just not good enough to make sure you can quickly and efficiently debug your errors.
Sometimes it’s fun to just nerd out for a moment. Python is one of the most popular scripting languages today and we love it too. That’s one reason why LogicMonitor can execute any script or programming language supported by your environment. That means can enjoy the latest features of your favorite languages in your favorite monitoring tool (right?). If Python is your vice, give Python 3.7’s Data Classes a try!
Python is a powerful and versatile higher-order programming language. Whether you’re developing a web application or working with machine learning, this language has you covered. Python does well at optimizing developer productivity. You can quickly create a program that solves a business problem or fills a practical need. However, the solutions you reach when developing quickly aren’t always optimized for python performance.
Python list comprehensions offer a concise method of interacting with each element of a list. Even though they’ve been available since Python 2.0, their syntax often demotivates people from using them. This article aims to introduce List Comprehensions in a friendly way and offer you one more Python feature to add to your scripting toolbox.
A great thing about building applications for the internet is that people from all around the world can benefit from your effort. You can gather new users from Taiwan to Colorado and meet their needs just as effectively. In this global context, it can be good to provide your users with local flavor to help them feel connected to you and your applications. It can also be useful for you to know where your users are coming from to make sure that your infrastructure is configured in the best way.
In this post, we’ll learn what Amazon Web Services (AWS) Lambda is, and why it might be a good idea to use for your next project. For a more in-depth introduction to serverless and Lambda, read AWS Lambda: Your Quick Start Guide to Going Serverless. In order to show how useful Lambda can be, we’ll walk through creating a simple Lambda function using the Python programming language. We’ll test it out, as well as take a look at what Lambda provides for metrics and logging.
Python has rapidly grown into one of the most popular languages for automation. That’s why LogicMonitor supports Python when it comes to managing your LM portal and expanding your monitoring coverage with scripts. During your scripting adventures, you may find yourself wanting to take your work to the next level. One widely-applicable improvement is credential abstraction.
We’re pleased to announce that the Lumigo serverless intelligence platform now supports the Python microframework, AWS Chalice. Chalice was created by AWS to simplify the process of writing serverless apps in Python. Similar in structure to the Flask web framework, Chalice handles much of the configuration on behalf of the user, including automated IAM role policy generation.
