Analytics on Large Graphs in S3: Tools, Approaches and Formats for OLTP and OLAP
summary
Examines tools and formats for high-performance analytics on very large graphs stored in S3, focusing on workflows for OLTP and OLAP use cases. Describes approaches and tool choices for fast hypothesis testing and prototyping when graphs exceed Python’s capacity and graph databases are impractical, including storage and processing patterns that leverage S3.
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chapters (17)
- 0:00Introduction
- 2:18Graph analysis problem
- 4:45What are graphs
- 7:16OLTP and OLAP scenarios
- 10:30Scaling challenges and tools
- 13:41Python and Spark experience
- 15:51Graph databases usage
- 17:40Graph query languages
- 20:12Solution comparison and benchmarks
- 21:55Grahscope architecture
- 24:04Results and model improvements
- 26:58Hypothesis testing and anomalies
- 29:07GraphAr format
- 32:55Integration with DAGDB and DAC PGQ
- 33:16Conclusions and recommendations
- 34:20Q&A
- 44:25Conclusion
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