Haitao Wang
VP of Engineering at Tacnode
Haitao Wang is VP of Engineering at Tacnode, where he leads the team building the Tacnode Context Lake™. He spent nearly two decades at Microsoft, eight years of it on the SQL Server engine, working on T-SQL programmability, the plan cache, engine performance and columnstore, and later managed the big data and shared data platform teams behind Bing Ads and Skype for Business. He writes about streaming ingestion, incremental materialized views, and what it takes to keep derived state current for decisions made in production.
Posts by Haitao (6)
![Incremental Materialized View: How to Keep Derived State Fresh in Real Time](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
Incremental Materialized View: How to Keep Derived State Fresh in Real Time
Standard materialized views recompute everything on a schedule. Incremental materialized views apply only the delta — bounding refresh cost to what actually changed. Here's how they work across Postgres, ClickHouse, Databricks, streaming databases, and Tacnode, what each approach can and can't do, and where they all hit the same structural limit.
Haitao Wang|Mar 24, 2026ETL Pipelines: What They Are, How They Work, and When to Eliminate Them
ETL pipelines extract data from source systems, transform it into a usable format, and load it into a destination. This guide covers how ETL pipelines work, common architectures, tools, failure modes, and when streaming and CDC approaches eliminate the need for batch ETL entirely.
Haitao Wang|Mar 13, 2026Streaming Database: What It Is, How It Works, and When You Need One
A streaming database replaces the batch query model with continuous computation — materialized views maintained incrementally as data arrives. Here's how they work, when they help, and where they run out of runway.
Haitao Wang|Mar 5, 2026LLM Model Staleness: Why Models Go Stale After Training
Your LLM answered correctly last month—now it's confidently wrong. This isn't hallucination. It's model staleness: a structural limitation that fine-tuning and RAG can't solve. Here's the architectural fix that actually works.
Haitao Wang|Jan 9, 2026Postgres Materialized Views: Create, Refresh, and Optimize
How to create, refresh, and optimize a Postgres materialized view — plus the structural limit every team hits when reads need fresh derived state under concurrency.
Haitao Wang|Apr 29, 2026Postgres CDC: Logical Replication, Debezium, and Change Data Capture Options
Postgres CDC explained: how change data capture works in PostgreSQL, with a runnable example, trigger and polling approaches, logical decoding with replication slots and publications, how to enable it on self-hosted, RDS, Aurora, Cloud SQL, Azure and Supabase, the replication slot risks that take databases down, and how native replication, Debezium, Flink CDC and managed services compare.
Haitao Wang|Sep 30, 2026