Data Architecture

This section explores data architecture patterns and design principles for building robust, scalable data systems and pipelines.

Topics Covered

🏗️ Data Pipeline Patterns

• ETL vs ELT Pipelines - Extract-Transform-Load vs Extract-Load-Transform approaches

• Medallion Architecture - Bronze-Silver-Gold data lakehouse pattern

Coming Soon

📊 Data Storage Patterns

• Data Mesh Architecture - Decentralized data ownership and domain-oriented data products

• Event Sourcing - Storing events instead of current state

• Database per Service - Microservices data isolation strategies

• Polyglot Persistence - Using different databases for different use cases

🔄 Data Processing Patterns

• Stream Processing Architecture - Real-time data processing with Kafka/Kinesis

• Batch Processing Patterns - Large-scale data processing with Spark/EMR

• Lambda Architecture - Combining batch and stream processing

• Kappa Architecture - Stream-only data processing approach

🧮 Data Integration Patterns

• Change Data Capture (CDC) - Real-time data synchronization

• Data Virtualization - Unified data access layer

• Data Federation - Distributed data query capabilities

• Master Data Management - Single source of truth for critical business data

📈 Analytics Patterns

• OLAP vs OLTP - Analytical vs transactional data processing

• Data Warehouse Patterns - Dimensional modeling and star schemas

• Data Lake Patterns - Schema-on-read and data discovery

• Feature Store Architecture - ML feature management and serving

Key Principles

• Data Quality - Ensuring accuracy, completeness, and consistency

• Data Lineage - Tracking data flow and transformations

• Data Governance - Policies, procedures, and standards

• Scalability - Handling growing data volumes and velocity

• Performance - Optimizing for query and processing speed

• Security - Data encryption, access controls, and compliance

Technology Stack

AWS Data Services

• Storage: S3, DynamoDB, RDS, Redshift

• Processing: EMR, Glue, Lambda, Kinesis

• Analytics: QuickSight, Athena, SageMaker

• Orchestration: Step Functions, Airflow

Processing Frameworks

• Batch: Apache Spark, Hadoop

• Stream: Apache Kafka, AWS Kinesis, Apache Flink

• Orchestration: Apache Airflow, Prefect, Dagster