Tom Peplow's guest appearances

Tom Peplow

SOA Podcasts - Society of Actuaries

Mike Lombardi

Mike Green

Blake Hill

Katie Keith

Dave Dillon

Barbara Zvan

David Cutler

Joan Barrett

Jim Glickman

Andy Rallis

Peter Liebwein

Jennifer Marquino

Dana Bilyeu

John Toohey

Karen Shelton

OwlTail

Listen now as April Shen, FSA, CFA interviews Tom Peplow (Principal, Director of Product Development - LTS) on the interaction between actuarial modeling and big data. In this episode, Tom discusses the basic concept on data warehouse, tools and resources actuaries can use to learn more.  Show Notes and Resources: 1) Anything by Ralph Kimball – he’s the forefather of data warehouse data models. 2) Data storage tools: Databricks, Snowflake, Google BigTable, Amazon RedShift, Microsoft Synapse, Parquet 3) Streaming analytics / event processing: Kafka, Amazon Kinesis,   Azure Stream Analytics 4) Data preparation: Power BI (using Power Query), Azure Factory Data Flows, Alteryx, AWS Glue 5) Data analytics: Tableau, Power BI 6) Other tools/languages: Jupyter, Python, R, C#, .NET 7) Chapter 5 of Roy Fielding’s dissertation, “Representational State Transfer (REST).” 8) Andrew White (Gartner) “Top Data and Analytics Predictions for 2019”  https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/ 9) Data warehouse is not a use case, Jordan Tigani (Google) https://www.youtube.com/watch?v=0I7eOQpDBHQ

Listen now as April Shen, FSA, CFA interviews Tom Peplow (Principal, Director of Product Development - LTS) on the interaction between actuarial modeling and big data. In this episode, Tom discusses the basic concept on data warehouse, tools and resources actuaries can use to learn more.  Show Notes and Resources: 1) Anything by Ralph Kimball – he’s the forefather of data warehouse data models. 2) Data storage tools: Databricks, Snowflake, Google BigTable, Amazon RedShift, Microsoft Synapse, Parquet 3) Streaming analytics / event processing: Kafka, Amazon Kinesis,   Azure Stream Analytics 4) Data preparation: Power BI (using Power Query), Azure Factory Data Flows, Alteryx, AWS Glue 5) Data analytics: Tableau, Power BI 6) Other tools/languages: Jupyter, Python, R, C#, .NET 7) Chapter 5 of Roy Fielding’s dissertation, “Representational State Transfer (REST).” 8) Andrew White (Gartner) “Top Data and Analytics Predictions for 2019” <a href="https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/" rel="nofollow" target="_blank"> https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/</a> 9) Data warehouse is not a use case, Jordan Tigani (Google) <a href="https://www.youtube.com/watch?v=0I7eOQpDBHQ" rel="nofollow" target="_blank">https://www.youtube.com/watch?v=0I7eOQpDBHQ</a>

Modeling Section: Data warehouse, data lake or data swamp? Actuarial modeling in the big data era with Tom Peplow 