Apache Iceberg Lakehouse
Open table format for huge analytic datasets, providing ACID transactions, time-travel queries, partition evolution, and vendor independence.
Edge of Data & Intelligent Systems
Navigating the Frontier of
Modern Data & Agentic AI
CliffData is an open technical portal curating battle-tested patterns in Apache Iceberg lakehouses, event-driven streaming, hybrid retrieval, and multi-agent reasoning systems.
Lakehouse
Iceberg & Delta Lake Core
< 100ms
Sub-Second Stream Ingestion
Hybrid RAG
Dense + Sparse Vector Search
Multi-Agent
Cooperative Reasoning Graphs
Technical Matrix
Core Technology Ecosystem
Explore the foundational components powering contemporary data platforms and agentic cognitive architectures.
Real-Time Event Streaming
Stateful event stream processing utilizing Apache Kafka and Apache Flink for continuous aggregation, low-latency deduplication, and anomaly detection.
Agentic Workflow Loops
Autonomous multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen) with cyclical state machines, human-in-the-loop, and external tool execution.
Hybrid Retrieval & Vector DBs
Combining dense vector embeddings (HNSW) with sparse lexical search (BM25) via cross-encoder rerankers to eliminate hallucinations in enterprise RAG.
ClickHouse & DuckDB
High-performance columnar analytical engines for sub-second aggregations over billions of rows, in-process embedded analytics, and fast interactive dashboards.
LLM Observability & Evals
Continuous tracing, prompt regression benchmarking, latency tracking, semantic hallucination checks, and token cost attribution at runtime.
Reference Blueprint
Event-Driven Real-Time RAG
How production pipelines synchronize streaming event sources with semantic vector indices without manual batch runs.
Production Pattern #01
Change Data Capture to Vector Index Pipeline
When transactional databases update, CDC events trigger asynchronous embedding workers via Kafka topics. Embedding models vectorize text deltas and update HNSW indices incrementally with zero downtime.
- ✓ Sub-5 second freshness from Postgres write to Vector Search
- ✓ Dead-letter queue (DLQ) for rate-limited embedding API calls
- ✓ Hybrid rerank stage to maintain high precision
[Postgres OLTP]
│ (WAL Capture)
▼
[Debezium CDC] ──▶ [Kafka Topic: cdc.events]
│
▼
[Flink / Python Stream Worker]
│
┌─────────────┴─────────────┐
▼ ▼
[Text Chunking] [Metadata Filter]
│ │
▼ │
[Embeddings API] │
│ │
└─────────────┬─────────────┘
▼
[Qdrant / pgvector HNSW]
▲
│ Semantic Retrieval
[LLM Agent Query]