Introduction to Data Architecture:
Definition and significance of data architecture Role and responsibilities of a data architect Historical evolution: Traditional databases to big data and beyond Foundational Data Concepts:
Data models: relational, hierarchical, network, object-oriented, etc. Data schemas: star schema, snowflake schema, and galaxy schema Data normalization and denormalization Relational Databases & SQL:
Basics of relational databases SQL (Structured Query Language) deep dive ACID properties (Atomicity, Consistency, Isolation, Durability) NoSQL Databases:
Types: Document, Key-Value, Column-family, Graph CAP theorem (Consistency, Availability, Partition tolerance) Use cases and best practices for NoSQL Data Warehousing & Big Data:
Data warehousing concepts: ETL vs. ELT Big Data ecosystems: Hadoop, Spark, and others Data lakes: concepts, benefits, and challenges Data Mesh & Decentralized Data Systems:
The concept of treating data as a product Domain-oriented decentralized data infrastructure Real-time Data Processing:
Stream processing fundamentals Tools like Kafka, Apache Flink, and Apache Storm Data Governance & Quality:
Data governance frameworks Data lineage, metadata management, and cataloging Data quality metrics, processes, and tools Data Security & Privacy:
Encryption, masking, and tokenization Data access controls and permissions Compliance considerations (GDPR, CCPA, etc.) Master Data Management (MDM):
Definitions and importance MDM processes, tools, and best practices Data Integration & Interoperability: Data lakes, hubs, and marts Integration techniques: batch, real-time, virtual, etc. Cloud Data Platforms: Cloud data storage solutions Data processing in the cloud: serverless, managed services Data Modeling Tools & Techniques: ER diagrams, UML, and other modeling tools Data dictionary and metadata management Emerging Data Trends & Technologies: AI and machine learning’s role in data architecture Graph databases and knowledge graphs Edge computing and data considerations Optimizing for Performance & Scalability: Indexing, partitioning, and sharding Cache strategies and technologies Case Studies & Real-world Scenarios: Analyzing real-world data challenges and solutions Success stories and lessons from data project failures