Title

Session 6: Parallel and Distributed Query Engine for Federated Searching

Presenter Information/ Coauthors Information

Kyle Putnam, Query.AI

Presentation Type

Invited

Track

Other

Abstract

Network and IT data volumes used to be manageable and were housed in data lakes inside corporate networks. Fast forward to today, with the explosion of Cloud and SaaS, data volumes are enormous, getting larger daily, and highly distributed. For any organization, its data now resides outside its perimeter, across multiple cloud providers and 3rd party SaaS vendors. This makes querying and correlating user, device and application data a big challenge. We built an efficient and cost–effective distributed query engine that lets organizations seamlessly query their data and get the relevant answers and context they desire. Our distributed query engine is built to auto-scale, run parallel queries, perform contextual lookups, and find optimal query execution plans. Queries are executed over vendors’ APIs. End-user applications like federated searching are built leveraging a GraphQL API interface to our query engine. Operational use-cases include analyzing phishing, suspicious logins, and other cybersecurity threats to the organization.

Start Date

2-7-2023 11:00 AM

End Date

2-7-2023 12:00 PM

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Feb 7th, 11:00 AM Feb 7th, 12:00 PM

Session 6: Parallel and Distributed Query Engine for Federated Searching

Pheasant Room 253 A/B

Network and IT data volumes used to be manageable and were housed in data lakes inside corporate networks. Fast forward to today, with the explosion of Cloud and SaaS, data volumes are enormous, getting larger daily, and highly distributed. For any organization, its data now resides outside its perimeter, across multiple cloud providers and 3rd party SaaS vendors. This makes querying and correlating user, device and application data a big challenge. We built an efficient and cost–effective distributed query engine that lets organizations seamlessly query their data and get the relevant answers and context they desire. Our distributed query engine is built to auto-scale, run parallel queries, perform contextual lookups, and find optimal query execution plans. Queries are executed over vendors’ APIs. End-user applications like federated searching are built leveraging a GraphQL API interface to our query engine. Operational use-cases include analyzing phishing, suspicious logins, and other cybersecurity threats to the organization.