Even with dedicated database staff (often with impressive technical skills), businesses need help understanding how to address database issues. IT managers and database specialists are keen to try different approaches to keeping databases working as efficiently as possible. One popular approach is ‘DevOps’. As companies navigate the complexities of digital transformation and a growing trend toward cloud migration, IT environments have become extraordinarily complex.

The SolarWinds IT Trends Report 2024 – AI: Friend or Foe? indicated tension in the IT industry’s outlook on artificial intelligence (AI). While there is excitement about its potential to drive productivity, concerns linger around data quality, security, and regulation. At the recent SolarWinds Day event, I sat down with Krishna Sai, Senior Vice President of Engineering, and Cullen Childress, Senior Vice President of Product Management.

In cooking, ingredients must be properly prepared by a trainee chef so that the master chef can craft the perfect dish. In carpentry, lumber is precisely cut and sanded by an apprentice so that an expert carpenter can build sound structures. A similar principle applies in artificial intelligence (AI), where IT professionals must ensure the correctness and consistency of data for AI systems to produce accurate insights. However, the SolarWinds IT Trends Report 2024 — AI: Friend or Foe?

In the ever-evolving IT landscape, the Observability Dilemma casts a strategic shadow: To SaaS or not to SaaS, a question being dealt with by many IT professionals today. As organizations grapple with the complexities of maintaining system health and performance, navigating change while staying secure, choosing between the allure of cloud-based services and the on-premises sanctuary becomes pivotal.

Logging in a Python application is straightforward. When you have good logs, you have better visibility into application health. You can monitor performance and track user activity. You’re better equipped to debug errors. Life is good. The challenges come when your application grows more complex. Perhaps your Python code is part of a broader application, or you have services distributed across multiple machines or clouds.