DSPT#94: Data Lakehouse for Simpler, Faster and More Integrated Data Science

Our #94 Webinar Meetup will be hosted by Carlos Costa and dedicated to the topic of "The Data Lakehouse for Simpler, Faster and More Integrated Data Analysis and Data Science". This Meetup will be held exclusively online.

The schedule for the webinar is the following:
• 18:30 - 18:35: Opening the meeting
• 18:35 - 19:15: The Data Lakehouse for Simpler, Faster and More Integrated Data Analysis and Data Science by Carlos Costa
• 19:15 - 19:40: Q&A
• 19:40: Networking

Title:
The Data Lakehouse for Simpler, Faster and More Integrated Data Analysis and Data Science

Abstract:
Adequately supporting Data Analysis and Data Science activities have long been the end-goal for data architectures, being them a Data Warehouse, a Data Lake, or, recently, a Data Lakehouse. The Data Lakehouse is a recently coined term to describe a data architecture that aims to combine the benefits of the Data Warehouse and the Data Lake, while trying to surpass the challenges that both have been facing for several years and while including several new features that can be considered very attractive for Data Analysis and Data Science. In this talk, we will try to explain the concept of Data Lakehouse, the motivation for its appearance, the main technological players and open source projects related to this data architecture, and a brief demonstration of its capabilities. The goal of this presentation is to disseminate the Data Lakehouse architecture as potentially relevant for many projects and organizations targeting scalable, fast, flexible and simplified Data Analysis and Science activities.

Short-bio:
Carlos is a Big Data and Data Science enthusiast who is constantly looking to push the current barriers of knowledge and technological innovation, being always hungry for new challenges. He is currently the Director of Data Engineering Foundations at Adidas and an Invited Professor at the University of Minho. He has several years of combined experience in Big Data and Data Science at different professional levels, including hands-on practice, team leading, project planning, research, and teaching. Moreover, he has been using and developing several innovative techniques and technologies related to distributed data storage, processing, and analytics. The results of his work are frequently shared with the scientific and technical Big Data community through open source projects, scientific publications, conferences, keynotes, and workshops.