Advanced

Knowledge Graphs — Advanced

Operate knowledge graphs at scale: graph partitioning and sharding, temporal/versioned graphs, graph embeddings and inference, production GraphRAG, and a platform-design capstone.

5 lessons ~255 min total
  1. 1

    Scaling Graph Databases

    What you build when one machine isn't enough: graph partitioning and the balanced min-cut problem, sharding strategies, taming supernodes, replication, and distributed-traversal cost.

    50 minGraph PartitioningShardingSupernodes
  2. 2

    Temporal & Versioned Graphs

    Modeling time in graphs: valid-time vs transaction-time (bitemporal) edges, versioning nodes and relationships, time-travel queries, and provenance and audit for regulated industries.

    50 minTemporal GraphsBitemporalProvenance
  3. 3

    Graph Embeddings & Inference

    Graph representation learning: node2vec/DeepWalk random walks, knowledge-graph embeddings (TransE/DistMult/RotatE), link prediction, ontological inference, and retrieval augmentation.

    50 minGraph Embeddingsnode2vecLink Prediction
  4. 4

    Production GraphRAG at Scale

    Agentic, iterative multi-hop traversal driven by an LLM: query planning over the graph, bounding traversal cost, caching subgraphs and embeddings, latency budgets, and failure modes.

    50 minAgentic TraversalMulti-hop ReasoningCaching
  5. 5

    Capstone: Knowledge Graph Platform Design

    The synthesis — two worked case studies walking ingestion, extraction, resolution, scaling, hybrid retrieval, and serving end-to-end, with capacity planning, cost modeling, and a launch playbook.

    55 minSystem DesignCase StudiesLaunch Playbook