Python

jfrog

JFrog detects malicious PyPI packages stealing credit cards and injecting code

Software package repositories are becoming a popular target for supply chain attacks. Recently, there has been news about malware attacks on popular repositories like npm, PyPI, and RubyGems. Developers are blindly trusting repositories and installing packages from these sources, assuming they are secure.

scout

Scout APM Announces Python Application Support for Error Monitoring Tool

Traditionally an APM tool, Scout has expanded its service offerings to now include error monitoring of Python web applications for more cohesive and actionable observability insights within a single platform. This new feature supports an overall better user experience by eliminating the need for multiple web-application monitoring services; Scout APM with Scout Error Monitoring offers performance and error insight and alerting within a single, integrated dashboard.

logz.io

Introduction to Custom Metrics in Python with the Logz.io RemoteWrite SDK

We just announced the creation of a new RemoteWrite SDK to support custom metrics from applications using several different languages. This tutorial will give a quick rundown of how to use the Python SDK. Using these integrations, Prometheus users can send metrics directly to Logz.io using the RemoteWrite protocol without sending them to Prometheus first. Each SDK, while for a separate language, is each capable of working with frameworks like Thanos, Cortex, and of course M3DB.

logz.io

Announcing the RemoteWrite SDK for Custom Metrics in Python, Go & More

We’re proud to announce the creation of a new RemoteWrite SDK to support custom metrics from applications using Golang (Go), Python, and Java, with many more on the way. Each SDK will have automatic, continuous deployment of updates. Using these integrations, Prometheus users can send metrics directly to Logz.io using the RemoteWrite protocol without sending them to Prometheus first.

dashbird

AWS Kinesis vs SNS vs SQS (with Python examples)

How to choose a decoupling service that suits your use case? In this article we’ll take you though some comparisons between AWS services – Kinesis vs SNS vs SQS – that allow you to decouple sending and receiving data. We’ll show you examples using Python to help you choose a decoupling service that suits your use case. Decoupling offers a myriad of advantages, but choosing the right tool for the job may be challenging.

grafana

Get started with distributed tracing and Grafana Tempo using foobar, a demo written in Python

Daniel is a Site Reliability Engineer at k6.io. He’s especially interested in observability, distributed systems, and open source. During his free time, he helps maintain Grafana Tempo, an easy-to-use, high-scale distributed tracing backend. Distributed tracing is a way to track the path of requests through the application. It’s especially useful when you’re working on a microservice architecture.

cloudera

Managing Python dependencies for Spark workloads in Cloudera Data Engineering

Apache Spark is now widely used in many enterprises for building high-performance ETL and Machine Learning pipelines. If the users are already familiar with Python then PySpark provides a python API for using Apache Spark. When users work with PySpark they often use existing python and/or custom Python packages in their program to extend and complement Apache Spark’s functionality. Apache Spark provides several options to manage these dependencies.