Solution Architecture Practice

Comprehensive guide to software architecture frameworks, patterns, and best practices

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Data Architecture Learning Path

⚠️ Content Pending - Learning path is being developed.

Overview

Comprehensive learning path for data architecture, analytics, and data engineering.

Coming Soon

Prerequisites

  • Database fundamentals
  • SQL proficiency
  • Basic statistics
  • Data modeling concepts

Learning Objectives

  • Master data architecture principles
  • Design scalable data platforms
  • Implement data governance
  • Build analytics solutions
  • Understand modern data stack

Modules

Module 1: Data Architecture Fundamentals

  • Data architecture principles
  • Data modeling techniques
  • Normalization and denormalization
  • Data warehousing concepts
  • Data lake architecture

Module 2: Database Technologies

  • Relational databases
  • NoSQL databases
  • NewSQL databases
  • In-memory databases
  • Time-series databases
  • Graph databases

Module 3: Data Integration

  • ETL vs ELT
  • Data pipelines
  • Change data capture (CDC)
  • Real-time data integration
  • API-based integration
  • Batch processing

Module 4: Data Warehousing

  • Dimensional modeling
  • Star schema
  • Snowflake schema
  • Fact and dimension tables
  • Slowly changing dimensions
  • Data mart design

Module 5: Data Lakes & Lakehouses

  • Data lake architecture
  • Zone-based design
  • Data catalog
  • Metadata management
  • Lakehouse concepts
  • Delta Lake, Iceberg

Module 6: Big Data Processing

  • Hadoop ecosystem
  • Apache Spark
  • Stream processing (Kafka, Flink)
  • Lambda architecture
  • Kappa architecture

Module 7: Analytics & BI

  • Analytics architecture
  • OLAP vs OLTP
  • BI tool integration
  • Self-service analytics
  • Embedded analytics
  • Real-time analytics

Module 8: Data Governance

  • Data governance framework
  • Data quality
  • Master data management
  • Data lineage
  • Data catalog
  • Privacy and compliance (GDPR, CCPA)

Module 9: Cloud Data Platforms

  • AWS data services
  • Azure data services
  • GCP data services
  • Cloud data warehouses
  • Managed analytics services

Hands-On Projects

  • Design a data warehouse
  • Build ETL pipelines
  • Create a data lake
  • Implement streaming analytics
  • Set up data governance

Timeline

Recommended: 12-14 weeks, 10-15 hours per week


Status: Placeholder
Last Updated: November 2025