Firebase and Cloud Firestore

A complete Firebase/Cloud Firestore course covering Standard and Enterprise editions, Native Core and Pipeline operations, document modeling, SDKs, realtime listeners, offline persistence, queries and indexes, transactions, Security Rules, IAM/App Check, scaling and hotspots, aggregations, vector search, MongoDB compatibility, TTL, backups/PITR, encryption/networking, observability, cost engineering, migration, and production architecture.

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27chapters

135lesson paths

Beginner → Advancedlearning level

Publishedcourse state

Coverage baselineCurrent 2026 Firestore Standard and Enterprise capabilities, including Native Core/Pipeline operations, MongoDB compatibility, vector search, Query Explain/Insights, scheduled backups, and PITR

Course brief

Design client-facing document applications safely, then understand the serverless scaling, query, security, recovery, observability, and cost mechanics behind Firestore.

This syllabus deliberately separates foundations, modeling, querying, internals, reliability, security, performance, operations, and production design so advanced material is not compressed into generic catch-all chapters.

By the end

You will be able to

Model Firestore documents/collections/subcollections for access patterns, limits, atomicity, lifecycle, and scale
Use Core queries, indexes, listeners, offline persistence, transactions, batched writes, aggregations, and vector search correctly
Secure mobile/web access with Authentication, Security Rules and App Check while separating trusted server IAM paths
Understand Enterprise Native Pipeline operations and MongoDB compatibility without assuming identical behavior to Standard Native mode or MongoDB
Operate Firestore with backups/PITR, TTL, CMEK/network controls, Query Explain/Insights, quotas, cost models, testing, and production runbooks

Complete syllabus

27 chapters · 135 lessons.

Every lesson path was reserved in the planned syllabus and is now linked because its lesson HTML is available. The sequence moves from foundations through advanced implementation, architecture, operations, reliability, security, tuning, and a production capstone.

Chapter 1

Firestore Foundations, Firebase/Google Cloud, Editions, Modes, Locations, and Lab Setup

5 lessons

What Cloud Firestore Is: Serverless Document Database, Realtime Clients, Strong Consistency, and Workload FitPublished lesson · Chapter01/Lesson1.html

Firebase and Cloud Firestore

Design client-facing document applications safely, then understand the serverless scaling, query, security, recovery, observability, and cost mechanics behind Firestore.

You will be able to

27 chapters · 135 lessons.

Firestore Foundations, Firebase/Google Cloud, Editions, Modes, Locations, and Lab Setup

Documents, Collections, Fields, References, Maps, Arrays, Timestamps, GeoPoints, and Limits

Data Modeling by Access Pattern: Embedding, Referencing, Duplication, Fan-Out, and Denormalization

CRUD with Client and Server SDKs: Reads, Writes, Updates, Deletes, Preconditions, and Converters

Queries: Filters, Ordering, Limits, Cursors, Collection Groups, and Query Constraints

Indexes: Automatic/Composite/Collection-Group/Vector, Exemptions, and Index Cost

Realtime Listeners: Snapshots, Change Events, Metadata, Latency, and Listener Lifecycle

Offline Persistence, Local Cache, Synchronization, Conflict Behavior, and Offline Indexes

Transactions, Batched Writes, Atomic Field Operations, Retries, and Contention

Atomicity Across Business Workflows: Counters, Reservations, Idempotency, and Event-Driven Consistency

Subcollections, Collection Groups, Hierarchies, Deletion, and Data Lifecycle

Security Rules Foundations: Authentication Context, Match Paths, Reads/Writes, and Validation

Advanced Security Rules, Query Compatibility, App Check, and Rules Testing

IAM, Admin/Server SDKs, Service Accounts, Authentication, and Trust Boundaries

Scaling and Hotspots: Key Distribution, Index Fan-Out, Sequential Values, and Ramp-Up

Aggregation Queries, Server-Side Count/Sum/Avg, Materialized Aggregates, and Analytics Boundaries

Vector Search: Embeddings, KNN Indexes, Distance Measures, Filters, and Retrieval Design

Firestore Enterprise Native Mode: Core vs Pipeline Operations and Advanced Querying

Firestore with MongoDB Compatibility: Drivers, MQL/BSON, Tools, and Serverless Differences

Migrating MongoDB Workloads to Firestore MongoDB Compatibility

TTL, Retention, Expiration, Backfills, and Lifecycle Automation

Backups, Point-in-Time Recovery, Restore, and Disaster Recovery

Encryption, CMEK, Network Access, Private Connectivity, and Data Governance

Observability: Query Explain, Query Insights, Key Visualizer, Metrics, Logs, and Troubleshooting

Cost Engineering, Quotas, Limits, Billing, and Capacity/Usage Forecasting

Testing, Emulator Suite, CI/CD, Index/Rules Deployment, and Schema Migration

Production Capstone: Design, Secure, Scale, Search, Recover, and Operate a Firestore Application