Xiaowei Jiang
CEO & Chief Architect at Tacnode
Xiaowei Jiang is CEO and Chief Architect at Tacnode, where he designed the Context Lake architecture from first principles. He previously built distributed query engines at Meta and Microsoft, working at petabyte scale across some of the largest data systems in production. His formal analysis of decision coherence — the Composition Impossibility Theorem — is published on arXiv (2601.17019) and provides the theoretical foundation for the Context Lake as a system category. He writes about database architecture, AI agent infrastructure, and the structural limitations of composed data stacks.
Posts by Xiaowei (14)
![OLTP vs OLAP: The False Choice for the Agentic Era](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
OLTP vs OLAP: The False Choice for the Agentic Era
Every architecture guide frames OLTP vs OLAP as a choice: optimize for transactions or optimize for analytics. But automated decision systems — fraud checks, credit approvals, agent actions — need both transactional consistency and analytical power at the same moment. The Composition Impossibility Theorem proves you can't stitch separate OLTP and OLAP systems together to get there. Here's what comes after the tradeoff.
Xiaowei Jiang|Mar 17, 2026Apache Kafka vs Apache Flink: The Real Comparison Is Flink vs Kafka Streams
Most people comparing Kafka and Flink are actually asking which stream processing layer do I need? The real architectural choice is Apache Flink vs the Kafka Streams API — and understanding the difference changes how you build.
Xiaowei Jiang|Mar 2, 2026What Retrieval Really Means for AI Agents
AI retrieval is not one operation. Production decisions require exact and semantic retrieval patterns used together: point lookups, range scans, filters, joins, aggregations, and similarity search.
Xiaowei Jiang|Feb 18, 2026What Is Derived Context?
Why data freshness matters for AI decisions: derived context is state computed from events that must be current at decision time. When feature freshness degrades, decisions fail—not from bad models, but stale context.
Xiaowei Jiang|Feb 13, 2026Retrieval Is More Than Vector Search
RAG architecture needs more than embeddings. Real AI agent memory requires hybrid search: point lookups, aggregations, filters, joins—plus semantic search. When retrieval is vector-only, agents miss the structured context that determines correctness.
Xiaowei Jiang|Feb 9, 2026Context Silos: When the System Knows But the Decision-Maker Doesn't
Why AI agent memory fails even when data exists: context silos prevent agents from accessing knowledge computed elsewhere. The fraud pattern was detected—but the checkout agent couldn't see it. Stale context isn't always old. Sometimes it's just unreachable.
Xiaowei Jiang|Feb 6, 2026What Is Context Engineering? The Discipline Behind Effective AI Agents
Context engineering is the discipline of designing how AI agents receive, manage, and act on information. It goes far beyond prompt engineering — covering context windows, tool calls, memory architecture, and the retrieval systems that determine whether an agent makes good decisions or bad ones.
Xiaowei Jiang|Feb 3, 2026ClickHouse JOINs Are Slow: Here's Why (And What To Do About It)
If your ClickHouse JOINs are killing query performance, you're not alone. Here's why columnar databases struggle with JOINs, what join algorithms are available, how to read the query plan, and when it's time to consider alternatives.
Xiaowei Jiang|Feb 5, 2026AI Agent Memory Architecture: The Three Layers Production Systems Need
AI agents need more than a vector database. Production systems require three distinct memory layers — episodic, semantic, and state. Here's what each layer does and why it matters.
Xiaowei Jiang|Feb 4, 2026Semantic Operators: Run LLM Queries Directly in SQL
Classify, summarize, and extract data using LLM reasoning inside your database. No external pipelines, no data movement — just SQL.
Xiaowei Jiang|Jan 28, 2026Join Tacnode at Current 2025: Putting Context in Motion
Context Lake comes to the Big Easy.
Xiaowei Jiang|Oct 14, 2025Context Lake: The Infrastructure Imperative for Real-Time AI
The next evolution from Data Lake to Context Lake.
Xiaowei Jiang|Aug 16, 2025Tacnode Context Lake is now available in the new AWS Marketplace AI Agents and Tools category
Helping usher in a new category of real-time AI solutions.
Xiaowei Jiang|Jul 16, 2025The Decision-Time System Model
Kafka + ClickHouse solves streaming analytics—but not AI decision-making. Here's why teams searching for Kafka alternatives or a streaming database still hit walls: split state, temporal misalignment, and consistency gaps that break automated decisions.
Xiaowei Jiang|Feb 14, 2026