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Getting Data Ready for Data Science with Delta Lake and MLflow

Denny Lee. Developer Advocate at Databricks
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.

Series Details

This session is part of the Getting Started with Delta Lake series with Denny Lee and the Delta Lake team.

Session Abstract

One must take a holistic view of the entire data analytics realm when it comes to planning for data science initiatives. Data engineering is a key enabler of data science helping furnish reliable, quality data in a timely fashion. Delta Lake, an open-source storage layer that brings reliability to data lakes can help take your data reliability to the next level.

In this session you will learn about:

  • The data science lifecycle
  • The importance of data engineering to successful data science
  • Key tenets of modern data engineering
  • How Delta Lake can help make reliable data ready for analytics
  • The ease of adopting Delta Lake for powering your data lake
  • How to incorporate Delta Lake within your data infrastructure to enable Data Science

What you need:
Sign up for Community Edition here and access the workshop presentation materials and sample notebooks.

Advanced: Diving Into Delta Lake

Dive through the internals of Delta Lake, a popular open source technology enabling ACID transactions, time travel, schema enforcement and more on top of your data lakes.

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