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Treating data as a product works when you fuse continuous discovery habits (Teresa Torres: outcomes, opportunity solution trees, assumption testing) with a platform that enforces contracts, SLOs, and governance (catalogs,…
In the age of ever-growing datasets, the ability to scale databases efficiently is a core architectural concern. One of the most powerful techniques for handling large-scale workloads is data sharding…
You want your friends and colleagues to be consistent in the way they interact with you. Consistency is also a key concern when choosing a database. Consistency models define the…
In modern organisations, data moves across teams, tools, and systems faster than ever before. With this speed comes a persistent risk: the moment a producer changes the shape, meaning, or…
Data Mesh 2.0 builds on the original principles of Data Mesh — decentralizing data ownership, treating data as a product, providing self-serve infrastructure, and enabling federated governance, but takes them…
I have various posts on this site that talk to indexes. But Snowflake’s indexing and search approach is quite different from traditional approaches like B-trees. It doesn’t maintain B-tree indexes…
When I first came across this algorithm I was intrigued. What did it mean? Working/playing with vector database like Milvus, Pinecone, Weaviate, or Vespa, I saw HNSW mentioned in the…
The encryption protecting your data today relies on mathematical problems that are practically impossible for classical computers to solve. Factoring large numbers, computing discrete logarithms, and similar hard problems form…
A conceptual model is an abstract representation of business concepts and the relationships between them. It is used to convey meaning to stakeholders and technical teams. The term itself dates…
Let’s look at some hypothetical examples you may have actually witnessed with your engineering teams. We have one server, but probably need two. Let’s use Kubernetes. We need to count…
Another stroll back in time here to talk about the way we order data and how it affects how efficiently we can store, retrieve, and analyze it. Sorting a single-dimensional…
In data management, semantic models address the challenge of understanding meaning and relationships within distributed data. They bridge business language and machine-readable data, enhancing interoperability, compliance, and self-service analytics. By defining concepts and semantics, they clarify terminology across systems, supporting AI integration and enabling better data governance and insights.
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