Operations | Monitoring | ITSM | DevOps | Cloud

If you’re using Python for search, log aggregation, or analytics, you’ve probably worked with Elasticsearch. It’s fast, scalable, and fairly complex once you go beyond the basics. The official Python client gives you raw access to Elasticsearch’s REST API. But getting it to work the way you want, especially under load, can be tricky. This blog walks through practical ways to index, query, and monitor Elasticsearch from Python code, without getting lost in the docs.

Open-source package repositories like the Python Package Index (PyPI) play a crucial role in software development. However, these platforms are also potential targets for malicious actors attempting to exploit application software vulnerabilities. The JFrog Security Research team regularly monitors open source software repositories using advanced automated tools, in order to detect malicious packages.

InfluxDB 3 is our latest time series database built for real-time analytics and high-volume data. Its Python Processing Engine lets developers run custom scripts known as plugins to process data, trigger alerts, or integrate with external systems via HTTP web requests. To demonstrate what’s possible, we’ve developed several plugins, all of which are available in the influxdb3_plugins GitHub repository. This public repo is open for anyone to use, modify, and contribute to.

Python powers critical applications across countless organizations, from data processing pipelines to web services that handle millions of requests. While Python's readability and extensive ecosystem make it a developer favorite, its performance characteristics require thoughtful monitoring. As systems grow in complexity, understanding what's happening inside your Python applications becomes increasingly important.

When your checkout flow breaks, customers disappear faster than most ‘cutting-edge’ JS metaframeworks. Thankfully, setting up observability for your critical paths—like a customer checkout—is painless with Sentry. Let's walk through how we instrumented, monitored, and fixed a major issue, with minimal effort.

Python's logging system provides powerful tools for application monitoring, debugging, and maintenance. This comprehensive guide covers everything from basic setup to advanced implementation strategies, helping you build robust logging solutions for your Python applications.

If you're working in infrastructure or operations and looking to build reliable APIs, FastAPI might be the Python framework you need. This guide will help you understand how FastAPI can fit into your automation workflows and get you started with practical examples.

Sometimes silence isn’t golden; it’s a red flag. Whether you’re monitoring IoT sensors, system logs, or application metrics, missing data can be just as critical as abnormal data. Without visibility into these gaps, you risk overlooking potential failures, security threats, or operational inefficiencies. In time series workflows, detecting silence is often the first sign of trouble—whether it’s a network issue, device failure, sensor failure, or stalled process.