Data Architecture
Database selection, data modeling, and data platform patterns — from OLTP to analytics, and from single-node databases to distributed lakehouses.
Relational Databases
Structured, transactional data with SQL semantics — when strong consistency and joins are required.
NoSQL Databases
Flexible data models optimized for specific access patterns — types, trade-offs, and selection criteria.
Document Databases
JSON-like hierarchical documents for aggregate-shaped data and flexible schemas.
Key-Value Databases
Ultra-fast lookup by key — the simplest and most performant data model.
Column-Family Databases
Wide, sparse datasets for high-write and analytical workloads like Cassandra and HBase.
Graph Databases
Relationship-centric modeling for highly connected data and traversal-intensive queries.
Time-Series Databases
Time-indexed measurements and telemetry for monitoring, IoT, and metrics workloads.
Data Lakes
Raw, large-scale storage for diverse data types supporting exploration and analytics.
Data Warehouses
Curated analytical stores optimized for BI, reporting, and governed analytics.
Lakehouse Architecture
Hybrid lake + warehouse combining flexibility with ACID transactions and governance.
Replication Strategies
Copying data for availability and read scaling — synchronous, asynchronous, and multi-master.
Data Partitioning
Splitting data for scalability and locality when a single node is insufficient.
Indexing Strategies
Accelerating read patterns with B-trees, hash indexes, covering indexes, and partial indexes.
Schema Evolution
Managing changes to data shape over time when producers and consumers evolve independently.
Data Retention Policies
Lifecycle management for old data — compliance, cost, archival, and TTL strategies.