GraphGen: Conducting Graph Analytics over Relational Databases
summary
Claims a bolt-on graph extraction layer over SQL databases that avoids ETL, exposes a Domain-specific datalog-like DSL, and returns an in-memory graph object that can be serialized to GML/GraphSON/GraphML. Implements a Java core with a Python 2 wrapper, a web Explorer (D3/Bootstrap) for visual sampling, and a vertex-centric API; runs NetworkX algorithms (demo: PageRank in six lines). Works with Postgres MySQL and SQLite, but is single-node and loads graphs into memory.
AI-written summary · about
chapters (14)
- 0:00Welcome
- 1:49Graph analytics importance
- 5:17Graph analytics use cases
- 7:22Graph analytics systems trade-offs
- 13:06Graph data storage today
- 17:22GraphGen vision
- 19:49GraphGen Explorer
- 23:43GraphGen with Python
- 24:48GraphGen Java API
- 27:18GraphGen backend efficiency
- 28:04GraphGen DSL explained
- 30:31GraphGen research goals
- 31:56Download GraphGen
- 32:40Q&A
related talks
49mModern Graph Databases
26mGraph Databases will change your (freakin') life
46mGraph Databases: Talking about your Data Relationships with Python
19mUnraveling the Web of Relationships: Understanding Graph Data
29mAll about graphs in Database
30mAnalyze connections and find patterns in data with graphs
55mA Practical Guide to Graph Databases
29mTigerGraph, Connecting the World