Series / Databases
Retrieval architecture across pgvector, hybrid search, embeddings, GPU databases, and the operational boundaries around AI database access.
Engineers building RAG pipelines, semantic search, or recommendation systems who need to make real architectural decisions — index type, embedding model, hybrid vs pure vector, and when to move off Postgres.
You know what an embedding is and have at least experimented with semantic search. Familiarity with PostgreSQL is helpful.
How CPU, GPU, and TPU architectures differ in ways that matter for databases and AI workloads — and which compute class to reach for when adding vector search, embedding generation, or GPU-accelerated analytics.
How CPU, GPU, and TPU architectures differ in ways that matter for databases and AI workloads — and which compute class to reach for when adding vector search, embedding generation, or GPU-accelerated analytics.
How pgvector adds vector storage and similarity search to PostgreSQL, what the three distance operators do, and the index you must create before you hit 100K rows.
A DBA-friendly explanation of SIMD and SIMT using query execution, vectorized processing, and GPU mental models instead of hardware jargon.
A practical, DBA-friendly explanation of why modern analytical databases are increasingly using GPUs for scans, joins, aggregations, and AI-adjacent workloads.
A structured runbook for identifying which cost dimension is driving your AWS RDS or Aurora bill before making any changes.
A DBA-friendly walkthrough of how modern GPU databases execute large analytical SQL queries using columnar storage, parallel scans, and GPU aggregation.
A DBA-friendly explanation of how vector search works, why GPUs help, and where vector retrieval fits inside modern database and AI systems.
How tree-based retrieval can improve DB runbooks, schema docs, and incident knowledge over chunked vector search.
Why PostgreSQL and MySQL use B-trees while Cassandra and RocksDB use LSM trees — the read/write tradeoff that determines which storage engine fits your workload.
Isolating the OCI Autonomous Transaction Processing write path from catalog and analytics load using GoldenGate replication and Object Storage offloading.
Session cache versus durable cart: the recovery semantics that determine data survival across session loss, browser closure, and checkout failure.
Cloud cost triage across compute, storage, data transfer, logs, and managed services — a repeatable workflow for finding runaway spend before the bill arrives.
Why a PostgreSQL double write buffer prototype failed despite compiling, and what it reveals about AI-assisted systems design.
The second wave of March 2026 breakouts: an agent that learns from every conversation, a Rust vector index that outperforms FAISS at a fraction of the memory, and a Kubernetes-native agent control plane.