Operations | Monitoring | ITSM | DevOps | Cloud

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.

Many people writing about AWS Lambda view Node as the code-default. I’ve been guilty of this in my own articles, but it’s important to remember that Python is a ‘first-class citizen’ within AWS and is a great option for writing readable Lambda code. Take a look at these two starter examples of writing functionality in Python.

Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.

This past week some of the Scout team had the opportunity to hang out at PyCon USA in Cleveland. This was the first time the Scout APM team had attended PyCon. It was great to spend some time with an awesome swath of the Python community. With a great booth location situated across the aisle from the innovative and fun Slack booth, we had fun getting to know everyone with a solid amount of traffic heading past our booth over the exhibition days.

The 2019 edition of PyCon USA takes place over the next few days in Cleveland, Ohio. Scout is delighted to be there, sharing our APM tool with the Python community. Plus, we'll have great t-shirts and stickers for you, and we love to get geeky - one of our lead product engineers, plus two of our smart support engineers, are working the booth, ready to help you figure out your Python performance problems.

This post demonstrates a *basic* example of how to build a deep learning model with Keras, serve it as REST API with Flask, and deploy it using Docker and Kubernetes. This is NOT a robust, production example. This is a quick guide for anyone out there who has heard about Kubernetes but hasn’t tried it out yet. To that end, I use Google Cloud for every step of this process.

Python’s built-in logging module is designed to give you critical visibility into your applications with minimal setup. Whether you’re just getting started or already using Python’s logging module, this guide will show you how to configure this module to log all the data you need, route it to your desired destinations, and centralize your logs to get deeper insights into your Python applications.