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.
Database Concepts
What is a Self-Driving Database?
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.
What is a Data Fabric?
Data fabric is a term that gets used a lot in enterprise tech circles, but it’s often explained in ways that are either too vague or too technical to be useful. Here’s a plain-language breakdown of what it actually means.
Understanding High-Dimensional Vector Search
High-dimensional vector search is a foundational way AI systems find similar or relevant items across large datasets when the data has been converted into vectors. If you’ve used semantic search, gotten eerily accurate recommendations, or worked with a retrieval-augmented AI tool, this is often the mechanism running underneath.
What is Data Stewardship?
You might have seen “data steward” in a job description or heard it mentioned alongside data governance and wondered what it actually means in practice. It’s one of those roles that’s easy to overlook but plays a surprisingly important part in keeping an organization’s data trustworthy and usable.
Semantic Retrieval Explained
Semantic retrieval is a way of finding information based on meaning rather than matching exact words. You ask a question or describe what you need, and the system finds relevant results even if they use completely different wording. That gap between what someone types and what they actually mean is exactly what semantic retrieval is designed to close.
What Is an Embedding?
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.
Data Quality Management Explained
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.
What is Retrieval-Augmented Generation (RAG)?
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.
What is an Embedding Model?
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.