// Module 10 — Data Engineering & DS

Data Engineering & Data Science
Pipelines · Lakes · Predictive Intel

Infrastructure at scale. Architect distributed systems, build production-grade ETL, develop predictive ML models, and master stream processing across Kafka, Spark, Airflow, and Snowflake.

StackKafka · Spark · Airflow
FocusBatch + Real-Time
Milestones6 Labs
OutcomeFault-Tolerant Data Lake
TimelineMilestones: 6
Distributed SysWarehousingETL/AirflowSpark ComputeStreamingData Lakes

// Lab Roadmap — Hands-on Session View

Module Flow

Raw Streams → Corporate Goldmine

6 Deep Labs
Lab 01Phase 1

Distributed Systems — HDFS & MapReduce

  • Multi-node cluster setup
  • Terabyte-scale processing
  • Replication & fault tolerance
  • Distributed compute internals
Lab01
Lab 02Phase 2

Data Warehousing — Schema & Indexing

  • Star & Snowflake schemas
  • Corporate reporting models
  • Indexing strategies
  • Query optimization
Lab02
Lab 03Phase 3

ETL Pipelines with Airflow

  • Resilient automated DAGs
  • Extract, transform, load flows
  • Retry & SLA monitoring
  • API data orchestration
Lab03
Lab 04Phase 4

Big Data Compute with Apache Spark

  • Distributed cleaning engine
  • Data skew handling
  • Memory caching patterns
  • Job tuning at scale
Lab04
Lab 05Phase 5

Stream Processing — Kafka & Spark Streaming

  • Continuous ingestion pipelines
  • Event-driven architectures
  • Windowed aggregations
  • Real-time analytics
Lab05
Lab 06Phase 6

Data Lakes — Delta Lake & Modern Storage

  • ACID transactions on cloud
  • Time-travel querying
  • Unstructured storage layers
  • Lakehouse architecture
Lab06