500
Chapters
20
Modules
50%
Hands-on Labs
∞
Templates
Curriculum
|
01
Foundations of Serverless & Modern Data Architectures (Ch 1–25)
Foundations of Serverless & Modern Data Architectures (Ch 1–25)
1. Evolution of Data Architectures: DW → Data Lake → Lakehouse
2. What Is Serverless? Definitions & Principles
3. Benefits of Serverless: Scale, Cost, Agility
4. Serverless Limitations & Anti-patterns
5. Core Concepts: Stateless Compute, Event-driven, On-demand
6. Cloud Providers: AWS, Azure, GCP — Serverless Overview
7. Storage vs Compute Separation — Impact
8. Serverless Data Patterns: Ingestion, ELT, Analytics
9. Cloud Object Storage (S3/ADLS/GCS) as Data Backbone
10. Data Lifecycle in Serverless Environments
11. Batch vs Streaming in Serverless
12. Reliability & Fault Tolerance Concepts
13. Serverless Microservices for Data Processing
14. Container vs Serverless Compute Comparison
15. Pricing & Cost Behavior in Serverless Systems
16. Concurrency & Autoscaling Fundamentals
17. Identity, IAM & Least-privilege Access
18. Multi-tenant Serverless Architectures
19. Geo-distributed Design for Global Workloads
20. Serverless Data Mesh Concepts
21. Serverless vs Kubernetes — When & Why
22. Event-driven Orchestration Basics
23. Serverless Security Shifts
24. Serverless Observability Fundamentals
25. Lab: Build a Serverless Data Flow Diagram
02
Databricks Lakehouse Foundations (Ch 26–50)
Databricks Lakehouse Foundations (Ch 26–50)
26. Databricks Workspace Overview
27. Compute Models: Jobs, Clusters, SQL Warehouses
28. Unity Catalog Overview
29. Delta Lake — Core Concepts
30. ACID Transactions in Data Lakes
31. Schema Enforcement & Evolution
32. Time Travel & Versioning
33. Checkpointing & Transaction Logs
34. Databricks File System (DBFS) Architecture
35. Managed vs External Tables
36. Partitioning Strategies in Delta
37. Z-Ordering & Clustering
38. Optimize & Vacuum Operations
39. Medallion Architecture — Bronze, Silver, Gold
40. Auto Loader: File Ingestion Serverlessly
41. Databricks SQL Query Engine Basics
42. Serverless SQL Warehouse
43. Photon Engine Overview
44. Unity Catalog Lineage & Governance
45. Token authentication & Access Patterns
46. Serverless Job Scheduling
47. Delta Live Tables — Declarative ETL
48. Notebook-based CI/CD Concepts
49. Monitoring & Cost Optimization
50. Lab: Build a Bronze-to-Gold Pipeline
03
Azure Databricks / AWS Databricks Deep Dive (Ch 51–75)
Azure Databricks / AWS Databricks Deep Dive (Ch 51–75)
51. Regional Architecture & Control Planes
52. VNet Injection vs Serverless Networking
53. External Storage Mounts & Identity Federation
54. Databricks IAM & Instance Profiles
55. Private Link & Secure Connectivity
56. DBR Runtime Versions
57. Autoscaling Modes & Spot Usage
58. Cluster Policies & Governance
59. Data Exfiltration Protection
60. Workspace Security Model
61. Serverless Job Clusters — Behavior & Limits
62. Multi-cloud Deployment Strategies
63. Cross-region Table Replication
64. Encryption at Rest & In Transit
65. Secret Management & Key Vault Integration
66. Serverless REST APIs & Automation
67. ML Runtime vs SQL Runtime in Serverless Context
68. Pushdown Optimizations in Databricks
69. Adaptive Query Execution
70. Serverless Patterns for ML Inference
71. Infrastructure-as-Code for Databricks
72. Secure Credential Passthrough
73. Using Git Repos & Versioning
74. Troubleshooting Serverless ETL
75. Lab: Deploy Secure Serverless Databricks Workspace
04
Delta Lake Advanced Internals (Ch 76–100)
Delta Lake Advanced Internals (Ch 76–100)
76. Delta Transaction Log Anatomy
77. Delta Commit Protocol
78. Checkpoint Compression & Metadata Handling
79. Data Skipping & Predicate Pushdown
80. File Compaction Strategies
81. Merging Slowly Changing Dimensions (SCD)
82. MERGE Performance Tuning
83. Streaming in Delta — Triggers & Modes
84. Watermarking & Late-arrival Data
85. CDC Capture & Apply Patterns
86. Delta Sharing Internals
87. Auto Optimize & Auto Compaction
88. Column Mapping Modes
89. Change Data Feed (CDF) Deep Dive
90. Delta for ML Feature Storage
91. Column-level Lineage
92. Cross-cloud Delta Interoperability
93. Table Constraints & Expectations
94. Delta Clones: Shallow vs Deep
95. Distributed Transaction Guarantees
96. Delta Lake on Apache Spark Internals
97. Large-scale Metadata Management
98. Multi-region Delta Architecture
99. Delta + Serverless Pitfalls
100. Lab: Build a CDC Pipeline Using CDF
05
Serverless ETL/ELT at Scale (Ch 101–125)
Serverless ETL/ELT at Scale (Ch 101–125)
101. ELT vs ETL: Architectural Decisions
102. Batch ETL Patterns in Serverless
103. Streaming ETL Patterns
104. Auto Loader Advanced Features
105. File-format Optimization (Parquet, ORC, Delta)
106. Ingestion from APIs, Databases, Event Streams
107. Multi-hop Transformations
108. Error Handling & Dead-letter Queues
109. Idempotent ETL Pipelines
110. Cross-tenant Ingestion
111. Large-scale File Ingestion Patterns
112. Adaptive Pipeline Orchestration
113. Serverless ETL for ML Use Cases
114. A/B Testing for ETL Logic
115. CI/CD for ETL Pipelines
116. Testing Frameworks for ETL Units
117. Serverless Storage Lifecycle Management
118. Audit Logging & Data Quality Logging
119. Orchestrating Multi-tier ETL
120. Backfill vs Streaming Sync Logic
121. Dependency Management in Serverless
122. Metadata-driven ETL
123. 100% Serverless ELT Blueprint
124. Data Contract-based ETL Design
125. Lab: Build a Full Serverless ELT Pipeline
06
Snowflake Serverless Architecture (Ch 126–150)
Snowflake Serverless Architecture (Ch 126–150)
126. Snowflake Cloud Services Layer
127. Storage Layer Architecture
128. Virtual Warehouses & Autoscaling
129. Serverless Tasks & Pipelines
130. Snowpipe & Continuous Ingestion
131. Micro-partitioning Internals
132. Clustering Keys
133. Snowflake Time Travel & Fail-safe
134. COPY INTO & COPY FROM Patterns
135. External Tables
136. Snowpark Python & Serverless Processing
137. Cost Behavior in Snowflake
138. Query Profile Analysis
139. Materialized Views & Refresh Behavior
140. Dynamic Tables
141. Serverless User-defined Functions (UDFs)
142. Snowflake Marketplace & Sharing
143. Governance: Access, Roles, Row Policies
144. Security Best Practices
145. Multi-cluster Warehouse Patterns
146. Snowflake + Databricks Interop
147. ML in Snowflake: Feature Store Basics
148. Scaling with Serverless Tasks
149. Cross-region Replication
150. Lab: Build Serverless Ingestion with Snowpipe
07
Google BigQuery Serverless Architecture (Ch 151–175)
Google BigQuery Serverless Architecture (Ch 151–175)
151. BigQuery Storage & Compute Separation
152. Capacitor Storage Format
153. Slots, Reservations & Autoscaling
154. Query Optimizer Internals
155. BigQuery Data Transfer Service
156. BigQuery Materialized Views
157. Partitioned & Clustered Tables
158. BigQuery SQL Engine Deep Dive
159. BI Engine & Acceleration
160. Ingestion Patterns for GCS
161. Streaming Inserts
162. BigQuery ML Serverless Training
163. BigQuery ML Models Overview
164. Federated Queries
165. BigLake Tables
166. Object Table Architecture
167. Row-level Security & Policies
168. Serverless Orchestration on GCP
169. Pub/Sub → BigQuery Streaming
170. Google Cloud Functions for ETL
171. Serverless Spark on BigQuery
172. Cross-cloud BigQuery Integration
173. Cost Control Strategies
174. Access Manager Patterns
175. Lab: Build Serverless Ingestion Using BigQuery
08
Serverless Streaming Architectures (Ch 176–200)
Serverless Streaming Architectures (Ch 176–200)
176. Fundamentals of Event Streams
177. Kafka vs Kinesis vs Pub/Sub
178. Structured Streaming Internals
179. Event-time vs Processing-time Semantics
180. Watermarking & Lag
181. Streaming Joins & Windows
182. Reprocessing vs Replay Design
183. Streaming ETL Patterns
184. Change Data Capture → Streams
185. Multi-cluster Streaming
186. Streaming Orchestration
187. Exactly-once Guarantees
188. Consuming from Message Queues
189. Throughput & Latency Tradeoffs
190. Serverless Data Freshness SLAs
191. State Management in Stateless Systems
192. Stream Enrichment
193. High-throughput Ingestion Best Practices
194. Fault Tolerant Checkpointing
195. Debugging Streaming Jobs
196. Streaming Analytics
197. Stateful vs Stateless Streams
198. Streaming + ML Predictions
199. End-to-end Streaming Architecture
200. Lab: Real-time ETL Pipeline
09
Serverless Data Modeling (Ch 201–225)
Serverless Data Modeling (Ch 201–225)
201. Modern Data Modeling Principles
202. Dimensional Modeling for Lakehouse
203. Data Vault in Serverless Architectures
204. Wide-table vs Star-schema
205. Transaction-only Modeling
206. Surrogate Key Strategies
207. Slowly Changing Dimensions (SCD) at Scale
208. Modeling for Streaming Workloads
209. Modeling for Machine Learning
210. Columnar Modeling for Analytics
211. Semantic Layer Principles
212. Serverless Fact Table Design
213. Metric Stores
214. Feature Store Integration
215. Denormalization Patterns
216. Schema Evolution & Compatibility
217. Referential Integrity in Serverless
218. Snapshotting Techniques
219. Gold Layer Modeling
220. Dataset Versioning
221. Multi-tenant Data Models
222. Data Contracts & Schemas
223. Cross-platform Modeling
224. Architecture Anti-patterns
225. Lab: Build a Serverless Semantic Model
10
Serverless Governance, Security & Compliance (Ch 226–250)
Serverless Governance, Security & Compliance (Ch 226–250)
226. Authentication & Authorization
227. RBAC vs ABAC in Serverless
228. Unity Catalog Governance
229. Column-level Security
230. Row-level Filtering
231. Encryption Key Management
232. Sensitive Data Classification
233. Secret Scopes & Vaults
234. Token-based Access
235. Zero Trust Architecture
236. Serverless Perimeter Controls
237. Data Loss Prevention
238. Governance for ML Features
239. Compliance Mapping (GDPR, SOC2, HIPAA)
240. Cross-border Data Residency
241. Multi-cloud Identity Federation
242. Table & Catalog Governance
243. Lineage & Impact Analysis
244. Audit Logging at Scale
245. Securing Streaming Systems
246. Vendor Risk in Serverless
247. Serverless Governance Maturity
248. Policy Automation
249. Governance Breakdown Scenarios
250. Lab: Build Governed Delta Tables
11
Advanced Serverless Performance Engineering (Ch 251–275)
Advanced Serverless Performance Engineering (Ch 251–275)
251. Query Planning Internals
252. Adaptive Query Execution (AQE)
253. Skew Mitigation
254. Shuffle Optimization
255. File Size Optimization
256. Caching Strategies
257. Cluster Autoscaling Tuning
258. Adaptive Parallelism
259. Compute vs I/O Tradeoffs
260. Photon Runtime Optimization
261. Snowflake Query Optimization
262. BigQuery Query Best Practices
263. Partition Pruning
264. Data Skipping
265. Large-scale Joins Optimization
266. UDF Performance
267. Storage Format Cost Impact
268. Minimizing Shuffle Overhead
269. Maximizing Throughput
270. Cold Start Optimization
271. Execution Debugging Tools
272. Optimizing Orchestration Overheads
273. Performance Regression Detection
274. Benchmarking Serverless Workloads
275. Lab: Performance Tuning Challenge
12
Orchestration & CI/CD for Serverless Data (Ch 276–300)
Orchestration & CI/CD for Serverless Data (Ch 276–300)
276. Orchestration Principles
277. Databricks Workflows
278. Airflow vs Workflows vs Serverless Pipelines
279. Trigger-based Orchestration
280. Scheduled vs Event-driven
281. CI/CD for Notebooks
282. GitOps for Data Pipelines
283. Infrastructure-as-code (Terraform)
284. Secrets & Config Handling
285. Canary Deployments
286. Blue-Green ETL Deployments
287. Migration Automation
288. Linting & Code Quality
289. Notebook Testing Frameworks
290. Pipeline Promotion Steps
291. Rollback Strategies
292. Automated Documentation
293. Cross-platform Deployments
294. End-to-end Pipeline Testing
295. Audit-ready Deployments
296. Multi-environment Workflows
297. GitHub vs Azure DevOps CI/CD
298. Debugging CI/CD Failures
299. Build-test-deploy Blueprints
300. Lab: CI/CD for a Delta Pipeline
13
Monitoring, Observability & Data Quality (Ch 301–325)
Monitoring, Observability & Data Quality (Ch 301–325)
301. Observability Foundations for Serverless
302. Metrics, Logs & Traces — Serverless Changes
303. Monitoring Databricks Jobs & Workflows
304. Monitoring Snowflake Virtual Warehouses
305. Monitoring BigQuery Slot Usage & Performance
306. Event-driven Pipeline Observability
307. Data Quality Dimensions for Serverless Pipelines
308. Expectations Frameworks (DQ Rules, SLAs)
309. Great Expectations on Serverless Platforms
310. Delta Expectations & Validation Libraries
311. Data Freshness & Timeliness Metrics
312. Pipeline-level SLOs, SLIs & RCIs
313. Autoloader Event Logs & Checkpoint Monitoring
314. Streaming Lag & Throughput Dashboards
315. Detecting Schema Drift Automatically
316. CDC Data Quality Monitoring
317. Record-level vs Aggregate-level Validation
318. ML-based Anomaly Detection for Data Quality
319. Drift Detection in Features & Metrics
320. Data Quality Telemetry in Lakehouse
321. Alerts & Incident Response Playbooks
322. Automated Reconciliation Across Layers
323. Auditability & Traceability in Serverless Systems
324. Observability Anti-patterns & Failure Signatures
325. Lab: Build a Serverless Observability Dashboard
14
Machine Learning & Feature Engineering in Serverless (Ch 326–350)
Machine Learning & Feature Engineering in Serverless (Ch 326–350)
326. ML Lifecycle Overview for Serverless Platforms
327. Feature Store Concepts & Patterns
328. Feature Engineering at Ingestion Time
329. Online vs Offline Features
330. Serving Features from Delta & Snowflake
331. Training Pipelines on Serverless Compute
332. Hyperparameter Tuning in Serverless
333. Model Registries & Versioning
334. MLflow, SageMaker, Vertex AI Integrations
335. Model Evaluation & Bias Testing
336. Explainability for ML Models
337. Online Inference Patterns & Serverless APIs
338. Batch Scoring on Delta Tables
339. Monitoring Model Health & Drift
340. Retraining Strategies & Data Slices
341. Model Failover & Canary Inference
342. MLOps in Multi-cloud Serverless Environments
343. Security & Privacy for ML Workflows
344. Feature Lineage & Explainability
345. Edge & Nearline Inference Patterns
346. Resource-efficient ML on Serverless
347. Responsible AI & Governance for ML
348. Cost-optimised Model Serving
349. Case Study: Productionizing Recommender on Delta
350. Lab: Build a Feature-store backed Model Pipeline
15
APIs, Data Products & Data-as-a-Service (Ch 351–375)
APIs, Data Products & Data-as-a-Service (Ch 351–375)
351. Defining Data Products & APIs
352. Designing Data APIs on Serverless Platforms
353. API Gateways & Rate-limiting Patterns
354. Data Contracts & Schema Governance
355. Cataloging Data Products with Unity Catalog
356. Data-as-a-Service Architectures
357. Multi-tenant Data Product Design
358. API Security & Authentication
359. Caching Strategies for Data APIs
360. Real-time APIs for Streaming Data
361. Versioning & Deprecation Strategies
362. SLA Design for Data Products
363. Observability for Data APIs
364. Monetizing Data Products
365. Legal & Compliance Considerations
366. API SDKs & Developer Experience
367. Data Product Marketplaces & Sharing
368. Data Mesh Patterns for Product Teams
369. Distributed Governance for Data Products
370. Testing & Contract Validation (PACT)
371. Data Catalog Automation & Discovery
372. Self-serve Data Platforms & Onboarding
373. Data Product Roadmapping & Prioritization
374. Migration Strategies for Legacy APIs
375. Lab: Build a Serverless Data API
16
Advanced Architectures: Edge, IoT, and Hybrid Serverless (Ch 376–400)
Advanced Architectures: Edge, IoT, and Hybrid Serverless (Ch 376–400)
376. Edge vs Cloud Serverless Patterns
377. IoT Ingestion & Protocols (MQTT, AMQP)
378. Data Aggregation at the Edge
379. Event Routing & Filtering for IoT
380. Offline-first Data Patterns
381. Edge ML Inference & Model Updates
382. Syncing Edge Data to Delta Lake
383. Hybrid Architectures: On-prem + Cloud
384. Latency-sensitive Design Patterns
385. Security at the Edge & Device Identity
386. Cost Modeling for Edge Deployments
387. Compliance for Cross-border Edge Data
388. Data Reduction & Compression Techniques
389. Event-driven Edge Orchestration
390. High-availability Edge Clusters
391. Data Governance for Edge Data
392. Observability & Telemetry from Devices
393. Firmware & Data Schema Evolution
394. Edge Sandbox & Testing Pipelines
395. Case Study: IoT Fleet Data Platform
396. Migration Checklist to Hybrid Serverless
397. Best Practices for Low-bandwidth Environments
398. Federated Learning Patterns at Edge
399. Sustainability Considerations for Edge
400. Lab: Build an Edge → Delta Ingestion Pipeline
17
Tools, Ecosystem & Third-party Integrations (Ch 401–425)
Tools, Ecosystem & Third-party Integrations (Ch 401–425)
401. Overview of Ecosystem Tools (Airflow, dbt, Prefect)
402. Data Quality Tools (Great Expectations, Deequ)
403. Observability Tools (Prometheus, Grafana, Datadog)
404. ML Tooling (MLflow, Seldon, BentoML)
405. Feature Store Implementations
406. Data Catalogs & Lineage Tools
407. Security Tools & Secrets Management
408. Cost Management & FinOps Tools
409. Data Privacy & Masking Tools
410. CI/CD Toolchains for Data (GitHub Actions, CircleCI)
411. Terraform Modules & IaC Libraries
412. Connector Ecosystem (Kafka, Kinesis, Pub/Sub)
413. ELT Tools (Fivetran, Matillion, Airbyte)
414. BI Tools & Semantic Layer Integrations
415. Data Sharing & Marketplace Tools
416. Synthetic Data & Privacy Tools
417. Testing & Mocking Frameworks for Data
418. Red-team Tools for Data Security
419. Vendor Evaluation & RFP Templates
420. Integration Patterns & Best Practices
421. Open-source vs Commercial Tradeoffs
422. Building Internal Tooling & OSS Contributions
423. Tooling Maturity Models
424. Case Study: Tooling Stack for a Retail Platform
425. Lab: Build a Tooling Integration Blueprint
18
Case Studies & Industry Patterns (Ch 426–450)
Case Studies & Industry Patterns (Ch 426–450)
426. Case Study: Fintech Real-time Risk Platform
427. Case Study: Retail Personalization at Scale
428. Case Study: Telecom Network Analytics
429. Case Study: Energy Grid Forecasting
430. Case Study: Healthcare Data Lakehouse
431. Case Study: Manufacturing Predictive Maintenance
432. Case Study: Media Recommendation Pipelines
433. Case Study: Logistics & Fleet Analytics
434. Case Study: AdTech Real-time Bidding Data Platform
435. Case Study: Public Sector Data Platforms
436. Cross-industry Architecture Patterns
437. Migration Journeys: Monolith to Serverless
438. Cost-saving Case Studies & Analysis
439. High-throughput Benchmarks & Lessons
440. Scaling Teams & Operating Models
441. Governance Stories — Success & Failures
442. Operational Incident Post-mortems
443. Privacy-preserving Deployments: Case Studies
444. Real-world Data Contract Implementations
445. Edge & IoT Case Study: Fleet Monitoring
446. Case Study: Multi-cloud Replication
447. Lessons from Large-scale Serverless Deployments
448. Architecture Decision Records & Playbooks
449. Preparing for Audits & Compliance Reviews
450. Module 18 Capstone: Industry Playbook
19
Capstones, Templates & Enterprise Deliverables (Ch 451–475)
Capstones, Templates & Enterprise Deliverables (Ch 451–475)
451. Capstone Overview & Assessment Criteria
452. Architecture Diagram Template Pack
453. Terraform Modules & IaC Examples
454. Databricks Notebook Templates
455. Delta Table Design Templates
456. CI/CD Pipeline Examples
457. Observability Dashboard Templates
458. Data Contract & Schema Templates
459. Security Policy Templates
460. Cost Estimation Worksheets
461. Migration Runbooks & Checklists
462. Feature Store Templates
463. Model Registry & Serving Templates
464. SLA & SLO Templates for Pipelines
465. Data Product Playbooks
466. Vendor Evaluation RFP Templates
467. Governance Playbook & RACI Templates
468. Incident Response & Runbooks
469. Training Curriculum for Teams
470. Executive Brief & Board Pack Template
471. Example Project: End-to-end Serverless Platform
472. Example Project: Real-time Analytics Pipeline
473. Example Project: Feature Store Implementation
474. Example Project: Edge Data Ingestion System
475. Module 19 Capstone: Deliver Enterprise Pack
20
Graduation, Certification & Next Steps (Ch 476–500)
Graduation, Certification & Next Steps (Ch 476–500)
476. Capstone Submission Guidelines
477. Assessment Rubrics & Grading
478. Certification Exam Blueprint
479. Badging & Credentialing Pathways
480. Continuing Education & Micro-credentials
481. Building an Internal Training Program
482. Hiring & Team Structure Recommendations
483. Career Paths: Serverless Data Architect Roles
484. Job-ready Portfolio Building Tips
485. Open-source Contributions & Community
486. Updating Skills for New Platform Releases
487. Maintaining Governance & Compliance Over Time
488. Roadmap for Enterprise Adoption (90/180/365 days)
489. Playbooks for Scaling from PoC to Prod
490. Licensing & Pricing for Corporate Customers
491. Support & SLA Options for Course Participants
492. Template Library Access & Maintenance
493. Alumni Community & Mentorship
494. Research & Innovation Topics to Explore
495. Contributing Back: Case Study Submissions
496. Course Feedback & Continuous Improvement
497. Future Modules & Add-ons (Edge, GenAI, etc.)
498. How to Request a Custom Corporate Delivery
499. Final Project: Deploy a Production Serverless Pipeline
500. Graduation Ceremony & Certificate Issuance
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