Data modeling is designing how data is structured, named and related, so it can be stored efficiently and queried correctly. It applies to both application databases and analytics platforms.
A data warehouse is a central database built for analysis, holding cleaned, structured data from many systems. Analysts and BI tools query it for reports, dashboards and planning.
Domain-driven design (DDD) is an approach to building complex software that models code closely on the business domain. Engineers and domain experts share one language and split large systems into clear bounded contexts.
SQL (Structured Query Language) is the standard language for defining, querying and changing data in relational databases. It is used to filter, join, aggregate and update rows stored in tables.
Database indexing is the practice of creating data structures that let a database find rows quickly without scanning a whole table. It speeds up reads at the cost of extra storage and slower writes.
The difference is the data model: SQL databases keep related tables under a fixed schema, and NoSQL databases use flexible documents, key-value pairs or graphs. SQL is strongest at joins and transactions; NoSQL often scales out more easily.
dbt is an open-source tool for transforming data inside a warehouse with version-controlled SQL models, tests and documentation. It covers the transform step of ELT.
A clear proven process, that delivers
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