There are a few reasons you might need to convert a date to a string in SQL Server. Maybe you need a date in a specific format for a report. Maybe you’re concatenating it with other text. Maybe an external system expects dates as strings. Whatever the reason, SQL Server gives you several ways to do it, and the right one depends on what you’re trying to achieve.
This article covers four functions: FORMAT(), CONVERT(), CAST(), and STR().
A knowledge graph database stores information as a network of connected entities rather than rows and columns. Instead of asking “what data do we have?”, it asks “how does this data relate to everything else?” That shift in thinking is what makes it useful in situations where traditional databases start to struggle.
Feature engineering is the process of taking raw data and transforming it into inputs that help a machine learning model learn effectively. The model doesn’t see the world the way you do. It sees numbers. Feature engineering is the work of translating your data into a numerical form that carries the right information for the problem you’re trying to solve.
The term “AI database” gets used loosely, and that’s partly because it describes a moving target. It’s not one specific product or technology. Rather, it’s a broad shift in how databases are being designed, extended, and used as AI becomes central to how software works.
To make sense of it, it helps to look at the different ways AI and databases are intersecting right now. Because there are several, and they’re quite different from each other.
When a query has multiple execution plans in Query Store, comparing their performance can help you identify which plan performs best and understand why performance may have regressed. This comparison can be very useful for troubleshooting queries that suddenly became slower after a plan change.
Data is the fuel that powers machine learning. The more of it you have, the better your models tend to perform. But real-world data comes with a lot of baggage. Privacy concerns, legal restrictions, high collection costs, and sometimes, just plain scarcity. Synthetic data is how the industry is working around that problem.
Simply put, synthetic data is artificially generated data that mimics real data without actually being real.
It’s not collected from users, scraped from the web, or pulled from production systems. It’s created by algorithms, statistical models, or AI systems that have learned the patterns and structure of real data well enough to produce convincing imitations of it.
As AI has become central to how software is built, the database industry has responded in two ways. Some databases have added AI features on top of their existing architecture. Vector search here, a natural language query interface there. Others have been built from scratch with AI workloads as the primary design constraint.
That second category is what we mean by “AI-native”.
Ontology-based data storage is a way of organizing data using a formal model that defines what things are and how they relate to each other. The model itself, the ontology, sits at the center of how everything is stored and queried. Rather than treating data as rows and values, it treats data as a web of typed, rule-governed relationships that the system can reason with directly.
Databases are everywhere. Every app you use, every website you visit, every transaction you make is backed by a database. But keeping a database running well has always required a lot of human expertise. Expertise for things like tuning performance, managing storage, applying patches, backing up data, scaling up when traffic spikes. For decades, this was just the cost of doing business. You hired database administrators, and they kept the lights on.
A self-driving database is one that handles most of that work itself.