Operations | Monitoring | ITSM | DevOps | Cloud

Continuing education is incredibly important in IT as the landscape of technology continues to expand. And as an IT leader, you have a vested interest in making sure that your employees are well-informed and educated in an array of tech topics. In this stream, we’ll be chatting with three IT leadership experts about continued education and how certifications can impact both your IT organization and your team members’ career paths.

Steve, Grant and Ryan return to Simple Talks to discuss SQL Server and Postgres. The advocates share their experiences with both systems, highlighting what works and what doesn’t. From time series and Query Store to extended events, tooling and log shipping - this is a quick-fire deep dive into the pros, cons and differences between the two. There’s also conversation around making the transition from one system to the other, and Ryan reflects on his challenging introduction to Postgres…on the first day of a new job.

Selector's customer base includes 50 deployments across service providers as well as large enterprises in retail, media distribution, colocation services, and multi-cloud networking services. These customers aim to correlate events across their network, applications, and infrastructure; eliminate the need for human intervention in RCS and remediation; and democratize access to insights using conversational natural language interfaces. Selector delivers on these outcomes, while accelerating incident remediation through smart, actionable alerting and a GenAI-based conversational interface.

Selector translates English phrases to SQL queries through the use of an LLM. Each SQL query includes the table, or data set to be searched, along with filters, or conditions which prune the search results. We walk through a number of SQL queries and sample search results, before considering the LLM-based translation of a sample English phrase processed by Selector.

An LLM differs from a function in that it takes output and imputes, or infers, a function and its arguments. We first consider how this process works within Selector for an English phrase converted to a query. We then step through the design of Selector's LLM, which relies on a base LLM trained with English phrases and SQL translation, then fine-tuned, on-premises, with customer-specific entities. In this way, each of Selector's deployments relies on an LLM tailored to the customer at hand.