Loading…
Timezone
July 13, 2022 | London, England
View More Details & Registration Information

Please note: This schedule is automatically displayed in British Summer Time, UTC +1. To see the schedule in your preferred timezone, please select from the drop-down menu to the right, above "Filter by Date." The schedule is subject to change.
Back To Schedule
Wednesday, July 13 • 2:30pm - 3:00pm
Consuming Legend Data Models & Services Using BI, Python/ML & other Tools - Andrew Ormerod & John Pentelow, Goldman Sachs

Sign up or log in to save this to your schedule, view media, leave feedback and see who's attending!

Tweet Share
Feedback form is now closed.
Join Andrew Ormerod and John Pentelow from Goldman Sachs to learn about consuming Legend data models & services using Business Intelligence (BI), Machine Learning (ML), and other tools. The whole world is not model based, which is why we need tabular slices of data to integrate with those tools. This session will focus on Legend's capability to project slices of data in order to integrate with business intelligence and analytics tools. Andrew and John will discuss the TDS protocol, how it translates to query execution plans, and how it is being used within the firm. They will also demo integrations with Pandas, Trino, and other custom integrations.
Speakers
AO

Andrew Ormerod

Managing Director, Goldman Sachs
Andrew manages the global Business Intelligence (BI) Platform team at Goldman Sachs, which is responsible for operating and building the ecosystem of tooling for the firm's core BI products. He is also responsible for the Data Governance tooling platform within the Legend ecosystem... Read More →
JP

John Pentelow

Vice President, Goldman Sachs
John is a Vice President and senior developer in the Legend Engineering team at Goldman Sachs and is responsible for a number of the applications and protocols within the Legend ecosystem. John joined the firm in 2012 after earning a Bsc from the University of Bath.

Wednesday July 13, 2022 2:30pm - 3:00pm BST
Enterprise 1
  Data / Risk / Models