Document modeling, aggregation, and Spring Data
Day 1
Document Modeling, Queries & Spring Data
- BSON document model: embedding vs referencing — the core trade-off
- Schema design patterns: bucket, outlier, extended reference, computed
- Query operators: $eq, $in, $regex, $elemMatch, $expr, array operators
- Update operators: $set, $push, $pull, $inc, findOneAndUpdate
- Aggregation pipeline: $match, $group, $project, $lookup, $unwind, $facet
- Index types: single field, compound, multikey, text, TTL, partial
- EXPLAIN and index usage analysis
- Spring Data MongoDB: @Document, MongoRepository, @Aggregation
- MongoTemplate for complex operations
- Transactions: multi-document ACID transactions in replica sets
- Change streams: real-time document change notifications
- Atlas Search: full-text search with Lucene on MongoDB Atlas
What your team walks away with
Developers who model MongoDB documents correctly for their query patterns — not just translating relational schemas into JSON and hoping for the best.
- Model MongoDB documents with embedding and referencing decisions driven by query patterns
- Write and optimize aggregation pipelines for complex data transformations
- Index correctly for the queries the application runs
- Use Spring Data MongoDB for clean data access with MongoRepository and MongoTemplate
Book the MongoDB training
A focused one-day course — can be extended to include Atlas Search and MongoDB Atlas operational features.
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