Database Engine Internals and Implementation
Data Systems
Implementation-focused deep dive into the single-node database engine substrate: workload shape, page and record layout, buffer pools, B-tree and LSM access paths, WAL, checkpoints, crash recovery, and the transaction boundary that builds on durable storage.
Data Architecture and Platforms
Data Systems
Architecture-level design of data platforms: source-of-truth boundaries, derived-data contracts, regional placement, serving planes, reliability controls, governance handoffs, and platform operating models.
Analytical Query Engines and Warehouses
Data Systems
Columnar storage, execution engines, warehouse architecture, vectorization, and the internals of large-scale analytical query systems.
Data Integration, CDC, and Pipelines
Data Systems
Change capture, ingestion contracts, backfills, schema drift, and the operational trade-offs of moving data through modern pipelines.
Data Systems Foundations
Data Systems
Data models, storage trade-offs, batch versus streaming, analytical versus transactional systems, and the basic mental models for modern data stacks.
Data Lakehouse and Storage Formats
Data Systems
Draft track for columnar formats, table metadata layers, schema evolution, compaction, and lakehouse architecture.
Metadata, Lineage, and Catalog Systems
Data Systems
Schemas, ownership, lineage graphs, discovery surfaces, and the metadata infrastructure that makes data platforms governable.
Streaming Data Infrastructure
Data Systems
Streaming ingestion, stateful processors, watermarks, checkpoints, exactly-once claims, backpressure, replay, and the platform patterns behind low-latency data movement.
In-Memory Data Systems and Redis
Data Systems
In-memory system design through Redis as the concrete case study: event loops, data structures, persistence, replication, clustering, caching, queues, locks, and operations.
NoSQL and Distributed Data Stores
Data Systems
Key-value, document, wide-column, graph, and search-oriented data stores with partitioning, replication, consistency, compaction, indexing, and operations.
Backend Database Operations and Query Performance
Data Systems
Operational database depth for backend engineers: connection pools, isolation, query planning, index health, sharding, replicas, failover, and split-brain prevention.
PostgreSQL Internals and Operations
Data Systems
PostgreSQL-specific depth for production systems: MVCC, WAL, locks, planner evidence, indexes, vacuum, replication, pooling, migrations, security, and operational debugging.
Transaction Manager Implementation
Data Systems
Single-node transaction-manager implementation: transaction state, isolation anomalies, lock managers, MVCC, optimistic validation, serializable snapshot isolation, undo, WAL integration, recovery, index consistency, testing, and observability.