One of the hardest things about building AI systems is that the things humans care about (words, sentences, images, ideas, etc) aren’t naturally something a computer can do math on. A computer doesn’t inherently know that “happy” and “joyful” are similar, or that a photo of a dog and the word “dog” are related. It just sees raw data.
Embeddings are the solution to that problem.
Bad data is more common than most organizations want to admit. And more costly. Decisions get made on outdated numbers, reports contradict each other, and engineers spend hours tracking down why a dashboard looks wrong. Data quality management is how you prevent all of that from becoming the norm.
Dates in SQL Server can be surprisingly tricky if you’re new to the game. The way dates are stored is not always the way you want them displayed, and figuring out how to convert one to the other is one of those things every beginner eventually Googles. So let’s walk through it clearly.
Large language models are impressive, but they have a fundamental limitation in that they only know what they were trained on. Ask a model about something that happened after its training cutoff, or about a document sitting in your company’s internal knowledge base, and it either makes something up or tells you it doesn’t know.
Retrieval-augmented generation, almost always shortened to RAG, is the approach the industry has settled on to fix this.
The idea is pretty straightforward. Instead of relying purely on what the model has memorized, you give it the ability to pull in relevant information from an external source, then use that information to generate a response.
Computers are good at numbers. They’re not naturally good at understanding that “dog” and “puppy” are related, that a photo of a beach and the phrase “summer vacation” share something in common, or that a five-star review and the sentence “this product is amazing” mean roughly the same thing.
Embedding models are how we bridge that gap.
JSON_ARRAYAGG() is one of the new features introduced in SQL Server 2025. This is an aggregation function that allows you to combine multiple row values into a single JSON array directly within your SQL queries.
JSON_ARRAYAGG() simplifies the process of generating structured JSON output from relational data, which makes it easier to build APIs, export data, and integrate with modern applications that rely on JSON. Instead of manually constructing JSON with string operations or complex subqueries, JSON_ARRAYAGG() provides a clean, efficient way to transform sets of rows into well-formed JSON arrays as part of standard SQL aggregation.
Data mesh is one of the newer ideas in the data world. And it’s generated a lot of confusion. Unlike data lakes or data warehouses, it’s not a technology you buy or install. Rather, it’s a way of organizing how your company thinks about and manages data.
If you’ve been hearing “edge database” thrown around and aren’t totally sure what it means, you’re not alone. The term often gets used loosely, so let’s break it down clearly.
Data governance isn’t the most exciting term in the data world, but it might be one of the most important. Companies that ignore it tend to find out why it matters the hard way. Often this is through a compliance failure, a data breach, or a boardroom argument about whose numbers are right.
If you’re getting an error that reads “JSON aggregates do not support order-by within group when specified with grouping sets, cubes and rollups. Try your query without order-by within group.” it looks like you’re trying to use the ORDER BY clause inside a JSON aggregate function when using GROUPING SETS, CUBE, or ROLLUP.
To fix this error you’ll need to remove ORDER BY from the aggregate function when using those clauses.