This post was written by Mercy Kibet, a full-stack developer with a knack for learning and writing about new and intriguing tech stacks. In today’s digital world, software applications are becoming increasingly complex and distributed, making it more challenging than ever to diagnose and troubleshoot issues when they arise.

Python is a highly versatile language. From software engineering to machine learning and data analysis, it’s everywhere. As a multipurpose scripting and programming language, it’s often utilized for manipulating and working with data. So, when you’re working with Python, whether you’re analyzing data or writing scripts, you’re likely to encounter dates and time stamps.

In the eternal hunt for elusive bugs, logging is an indispensable aid. By recording the events and messages that occur during the execution of your program, logging opens the door to unparalleled debugging and performance monitoring capabilities. It all starts with Python’s built-in logging module. However, the true power of Python logging is unlocked not merely by using it, but by mastering it.

Microservices architecture has become the new norm for modern applications due to its numerous advantages compared to traditional monolithic architecture. However, microservices also come with several challenges. Especially when it comes to observability, traditional monitoring tools and techniques can no longer handle microservices’ distributed and dynamic nature.

Elasticsearch is a popular search engine that can be used to swiftly and almost instantly store, explore, and analyze huge volumes of data. It offers a distributed, multitenant full-text search engine with an HTTP web interface and schema-free JSON documents on top of Apache Lucene. In this tutorial, we will demonstrate how to communicate with an Elasticsearch cluster using a Python Elasticsearch client.

Logging is the process of keeping records of activities and data of a software program. It is an important aspect of developing, debugging, and running software solutions as it helps developers track their program, better understand the flow and discover unexpected scenarios and problems. The log records are extremely helpful in scenarios where a developer has to debug or maintain another developer’s code.

How can you test an empty data pipeline? Well, you can't, really. Read on and let's walk you through creating pretend streaming data using Python and Faker.

Recently InfluxData announced SQL support in InfluxDB Cloud, powered by IOx. Users can now use familiar SQL queries to explore and analyze their time series data. The SQL support was introduced along with the usage of Apache Arrow. Apache Arrow is an open source project used as the foundation of InfluxDB’s SQL support. Arrow provides the data representation, storage format, query processing, and network transport layers. Apache Flight SQL provides a method for interacting with Arrow via SQL.

A couple months ago, we launched Profiling in alpha for users on Python and Node.js SDKs — today, we’re moving Profiling for Python and Node.js to beta. Profiling is free to use while in beta — more updates to come when we near GA. Profiling is a critical tool for helping catch performance bottlenecks in your code. Sentry’s profiler gets you down to the exact file/line number in your code that is causing a slow-running query.