Operations | Monitoring | ITSM | DevOps | Cloud

As the number of organizations moving their ML projects to production is growing, the need to build reliable, scalable architecture has become a more pressing concern. According to BCG (Boston Consulting Group), only 6% of organizations are investing in upskilling their workforce in AI skills. For any organization seeking to reach AI maturity, this skills gap is likely to cause disruption.

In today’s hybrid infrastructure landscape, migrating applications to the cloud unlocks significant financial and technological benefits. Whether internal or external, these applications require robust, efficient infrastructure. Cloud providers like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure offer frameworks to help organizations build better systems.

Syslog is the backbone of logging in many Linux and Unix-based systems, playing a crucial role in monitoring, debugging, and auditing. But not all syslog messages are created equal. Depending on your system, software, and logging configuration, syslog messages may follow different formats. This guide walks you through the different syslog formats, why they matter, and how to work with them effectively.

Logs are the backbone of debugging, security, compliance, and performance monitoring. But if you don’t manage retention properly, you’ll either drown in unnecessary data or lose critical insights too soon. Log retention is all about striking a balance between keeping what’s necessary and discarding what’s not.

Cardinality refers to the number of unique values in a dataset column. A column with many distinct values—like a user ID or timestamp—has high cardinality, while a column with limited distinct values—like a boolean flag (true/false) or a category with a few possible options—has low cardinality. For example, consider a database of an e-commerce platform.

Software deployment isn’t always as simple as it sounds. Keeping applications up to date, avoiding disruptions, and managing deployments efficiently requires the right approach. Some tools automate the process, making frequent releases easier, while others focus on security, compliance, and stability across different environments. The best software deployment tool depends on how you work.

When considering or advocating for an Internal Developer Portal (IDP) within your organization, assessing potential impact is an exciting, but sometimes challenging endeavor, especially considering the broad set of use cases IDPs support and the lack of context and visibility before the presence of an IDP. Maybe you understand the inherent value of an IDP, but need to quantify the estimated savings/impact to justify the spend.

Redgate's new book, 'Introduction to PostgreSQL for the data professional', is now available for free download. Hear from authors Ryan Booz & Grant Fritchey about their inspiration for the book and the challenges they faced along the way. While the documentation around PostgreSQL is detailed and technically rich, finding a simple, clear path to learning what it is, what it does, and how to use it can be challenging.

For DevOps teams, ideal cloud deployment tools mean automation, consistency, and operational reliability. For CTOs, they ensure faster time to market, scalability, and efficiency. And for CFOs, cost-effectiveness and healthy margins are the name of the game. This is our hand-picked list to help you choose the right cloud deployment tools for your organization’s specific needs.