Chapter 29: MongoDB Syllabus

What is the actual MongoDB syllabus What should a serious learner really master — from zero to “I can confidently use MongoDB in real projects / job interviews / freelance work”?

I’m going to give you the real-world, practical syllabus that I would personally teach if you were sitting in front of me in Hyderabad for 8–12 weeks (assuming 4–6 hours/week of focused practice).

This is not a copy-paste from MongoDB University marketing page. This is what actually matters in 2025–2026 when companies are hiring, when side projects need to scale, and when you want to stop feeling “I know some MongoDB” and start feeling “I own MongoDB”.

MongoDB Syllabus 2026 – Realistic Roadmap (Beginner → Job-Ready)

Level 0 – Before you even touch MongoDB (1–2 days)

• What is a document database vs relational (rows vs documents)
• Why schemaless is powerful and dangerous
• BSON vs JSON (very quick – 10 min)
• Install MongoDB Compass + mongosh (or use Atlas free tier from day 1)

Level 1 – Core CRUD & Basic Queries (1–2 weeks)

1. Databases & collections – lazy creation (use, db.createCollection)
2. Insert: insertOne, insertMany, custom _id
3. Find: equality, comparison operators ($gt, $in, $regex, $exists, $type)
4. Logical operators ($and, $or, $not, implicit AND)
5. Array operators: $all, $elemMatch, $size, implicit contains
6. Projection (include/exclude fields, _id: 0)
7. Sort, limit, skip (pagination basics)
8. Update: updateOne, updateMany, important operators ($set, $unset, $inc, $push, $pull, $addToSet)
9. Delete: deleteOne, deleteMany, findOneAndDelete
10. .explain(“executionStats”) – first look at COLLSCAN vs IXSCAN

Mini-project after Level 1: Todo list / Blog posts / Student marks collection → full CRUD API endpoints

Level 2 – Intermediate Queries & First Indexes (2–3 weeks)

11. Dot notation for nested fields (“address.city”)
12. Array positional operator ($) and filtered positional ($[elem])
13. $expr – compare two fields in same document
14. Text search – basic $text index + $meta: “textScore”
15. Geospatial – 2dsphere index + $near, $geoWithin
16. Indexing deep dive:
    • Single field, compound (ESR rule: Equality → Sort → Range)
    • Multikey (arrays), sparse, partial, TTL
    • .getIndexes(), .dropIndex(), index usage in .explain()
17. Common performance mistakes (sorting without index, $regex without anchor, large $skip)

Mini-project: Product catalog with search, filter, sort, pagination + proper indexes

Level 3 – Aggregation Pipeline Mastery (3–5 weeks – this is the heart)

18. Pipeline structure + order matters
19. $match (early filtering – most important stage)
20. $group + all accumulators ($sum, $avg, $push, $addToSet, $first, $last, $min, $max)
21. $project vs $addFields (when to use which)
22. $unwind (preserveNullAndEmptyArrays!)
23. $sort + $limit + $skip (top-N, pagination)
24. $lookup – classic + let+pipeline syntax
25. $out vs $merge (materialized views)
26. $facet – multiple pipelines in parallel
27. Date operators ($dateToString, $dateAdd, $dateDiff)
28. Conditional logic ($cond, $switch, $cond in $group)
29. Array operators inside aggregation ($map, $filter, $reduce, $zip)
30. Debugging pipelines – $limit:1 early, explain plans

Mini-project: Sales dashboard:

• Revenue per city / product category
• Top 5 customers by total spend
• Monthly trend line
• Join orders + users + products
• Save summary with $out

Level 4 – Production & Advanced Topics (3–6 weeks)

31. Schema Validation – JSON Schema ($jsonSchema, required, enum, pattern, bsonType, moderate vs strict)
32. Transactions – multi-document ACID (startSession, withTransaction)
33. Change Streams – real-time listening (watch(), resume tokens)
34. Atlas Search – full-text, fuzzy, autocomplete, synonyms, vector search (very hot in 2026)
35. Data API – HTTPS REST interface (when you cannot use driver)
36. MongoDB Atlas features:
    • Triggers (serverless functions on change)
    • App Services (custom endpoints, authentication)
    • Charts (live dashboards – no code)
    • Online Archive (cold storage)
    • Search Nodes (dedicated for Atlas Search)
37. Performance tuning:
    • Profiler, slow query log
    • Covered queries
    • Index intersection vs compound
    • allowDiskUse, memory limits
38. Sharding basics – when & why, shard key choice, hashed vs ranged
39. Driver best practices (Node.js / Python / Java / Go):
    • Connection pooling
    • Retry logic
    • Bulk writes
    • Error handling patterns

Mini-project / Capstone: Choose one (realistic production-style):

• E-commerce backend (products, orders, users, cart, search, recommendations)
• Blog / CMS with full-text search + comments + categories
• Real-time analytics dashboard (views, likes, shares per post)
• IoT sensor data → aggregated hourly/daily summaries + alerts

Realistic Timeline (assuming 5–8 hours/week serious practice)

• Level 1 → 1–2 weeks
• Level 2 → 2–3 weeks
• Level 3 → 4–6 weeks (aggregation is the biggest jump)
• Level 4 → 4–8 weeks (depending on depth)

Total → 3–6 months to become confidently employable / independent with MongoDB.

Understood beta?

Now tell me honestly:

• Which level are you currently at?
• Which topic do you want to practice with exercises right now? (CRUD, indexing, aggregation basics, $lookup joins, schema validation, Atlas Search, etc.)

Or do you want a custom 10-question exercise set on a specific topic?

Just say the word — we’ll start solving real problems together right now! 🚀❤️

Class is waiting for your answer 😄