Spring Data MongoDB: From Repositories to Aggregations
Spring Data MongoDB breaks down fast once CRUD meets production—real queries, actual data volumes, analytics.
What looks simple at first quickly turns into unreadable repository methods, overfetching, and slow queries.
In this video, I walk through building a production-style Spring Boot application using Spring Data MongoDB — starting with basic setup and repositories, then moving into indexing, projections, custom queries, and aggregation pipelines.
You'll see how MongoDB's document model changes data design compared to SQL, when embedding helps, and when it becomes a liability. We cover where repository method naming stops scaling, how to use @Query safely, when to switch to MongoTemplate, and how to reduce payload size with projections and DTOs.
Finally, we implement real MongoDB aggregations to calculate analytics directly in the database and test everything against a real MongoDB instance using Testcontainers.
This is not another MongoDB overview.
It's a practical guide to actually using Spring Data MongoDB in production without fighting the database.
00:00 — MongoDB with Spring Boot
00:15 — Setup: What We're Building and Why
00:56 — Prerequisites and Project Creation
01:55 — Data Modeling: SQL Brain vs Mongo Brain
04:00 — Advanced Annotations: Indexes, Geospatial, Auditing
05:23 — Repositories: The Spring Way to Talk to Mongo
06:24 — CRUD: Save, Update, Read, Delete
06:47 — When Method Names Aren't Enough: @Query and MongoTemplate
07:46 — Projections and DTOs: Fetch Less, Leak Less
09:07 — Aggregations: Do Analytics in the Database
11:05 — Testing: @DataMongoTest and Testcontainers
12:19 — Recap / Next Steps
NeuroWatch — demo application:
https://github.com/code-with-bellsoft...