Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Treating MongoDB like a relational database — splitting every entity into its own collection and chaining $lookup stages — throws away the performance benefits of a document store. Conversely, embedding unbounded arrays inside a single document leads to write amplification and 16 MB BSON limit errors. This course teaches how to model schemas around application access patterns and write fast queries.

It covers BSON types, embedding vs referencing trade-offs, MongoDB schema design patterns (Subset, Bucket, Computed, Polymorphic, Outlier), multi-stage Aggregation Pipelines, index design with the ESR (Equality, Sort, Range) rule and explain() analysis, multi-document ACID transactions, Change Streams, Replica Sets (Write/Read Concerns), and Sharding/Shard Key selection.

Who Should Attend?

  • Backend and full-stack developers building applications on MongoDB or MongoDB Atlas
  • Data and software architects modelling catalogues, content, user profiles, IoT or event data
  • DBAs and DevOps engineers responsible for MongoDB performance and scaling
  • Prerequisite: basic database familiarity and working knowledge of JSON

Key Takeaways

  • Balance embedding and referencing based on cardinality, document growth and read/write ratios
  • Build expressive analytical pipelines with $match, $group, $lookup, $facet and $setWindowFields
  • Design compound, multikey and partial indexes following the ESR (Equality, Sort, Range) rule
  • Configure Read Concerns, Write Concerns and multi-document ACID transactions for consistency requirements
  • Manage Replica Set elections and choose a high-cardinality Shard Key that avoids hot shards

Curriculum

MongoDB Architecture, BSON and Atomic CRUD Operations
  • The WiredTiger storage engine, cache sizing, compression and BSON types (ObjectId, Decimal128, Date)
  • Expressive query operators, array modifiers ($push, $addToSet, $pull, arrayFilters) and atomic findOneAndUpdate
  • Enforcing collection-level structural rules with JSON Schema Validation
Document Data Modelling Patterns
  • Moving from relational normalisation to workload-driven design: Embedding vs Referencing
  • Modelling 1-to-N and N-to-M relationships and avoiding the unbounded-array anti-pattern
  • Applying the Polymorphic, Attribute, Bucket, Outlier, Computed and Extended Reference patterns
Data Processing and Analytics with the Aggregation Framework
  • Core stages: $match, $project, $unwind, $group, $sort, $limit and pipeline pushdown rules
  • Joining collections with $lookup, multi-faceted search with $facet and window analytics with $setWindowFields
  • Maintaining pre-aggregated summary collections with $merge and $out
Indexing Strategies, the ESR Rule and Query Performance
  • Single-field, Compound, Multikey (array), Wildcard, TTL and Partial indexes
  • Ordering compound index fields with the ESR (Equality, Sort, Range) rule and Covered Queries
  • Reading explain('executionStats') output: COLLSCAN vs IXSCAN and totalKeysExamined vs nReturned
ACID Transactions, Change Streams, Replica Sets and Sharding
  • Multi-document ACID transactions, Write Concerns (w: majority), Read Concerns and Read Preferences
  • Building event-driven integrations with real-time Change Streams
  • Replica Set elections/oplog sizing, Sharded Cluster architecture and choosing an effective Shard Key

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