Data Brew. Let’s talk data.
Welcome to Data Brew by Databricks with Denny and Brooke! In this series, we explore various topics in the data and AI community and interview subject matter experts in data engineering/data science. So join us with your morning brew in hand and get ready to dive deep into data + AI!
For this first season, we will be focusing on lakehouses – combining the key features of data warehouses, such as ACID transactions, with the scalability of data lakes, directly against low-cost object stores.
Jules Damji and Tathagata Das guide us through their journey in big data and the evolution of data architecture in the past 30 years. They discuss some of the biggest changes in industry they’ve seen, as well as trends to look forward to in the coming years. This is a fun episode connecting all four authors of the Learning Spark, 2nd Edition book.
Ellissa Verseput, ML Engineer at Quby, joins Denny and Brooke to discuss how Quby leverages ML to extract additional value from their data lake and how they manage this process.
Lara Minor, Senior Enterprise Data Manager at Columbia Sportswear, discusses how her team achieved a 70% reduction in pipeline creation time. This reduced ETL workload times from four hours with previous data warehouses to minutes using Azure Databricks, hence enabling near real-time analytics.
Join Michael Armbrust, Spark PMC Member and Engineering Lead for Structured Streaming & Delta Lake at Databricks, to explore the journey building data lake technology.
Join Ali Ghodsi, CEO and co-founder of Databricks, and David Meyer, SVP of Product at Databricks, for a detailed tour of the Lakehouse architecture.
Join this panel of data warehousing luminaries of Barry Devlin, Susan O’Connell, and Donald Farmer to discuss the evolution of data warehouses, data lakes, and lakehouses.
Brooke Wenig is a Machine Learning Practice Lead at Databricks. She leads a team of data scientists who develop large-scale machine learning pipelines for customers, as well as teach courses on distributed machine learning best practices. Previously, she was a Principal Data Science Consultant at Databricks. She received an MS in Computer Science from UCLA with a focus on distributed machine learning. She speaks Mandarin Chinese fluently and enjoys cycling.
Denny Lee is a Developer Advocate at Databricks. He is a hands-on distributed systems and data sciences engineer with extensive experience developing internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He also has a Masters of Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise Healthcare customers. His current technical focuses include Distributed Systems, Apache Spark, Deep Learning, Machine Learning, and Genomics.
Contact the Data Brew team here: firstname.lastname@example.org
Brooke & Denny are two of the co-authors of Learning Spark, 2nd edition.