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Azure Data Engineering services used to analyze data Kevin Chant
Overview to some of the more popular Azure Data Engineering services used to analyze data
Building the Lakehouse Architecture with Azure Synapse Analytics Mike Dobing
A intro session showing the whys, how's and what's for building a Data Lakehouse in Azure Synapse Analytics
Data Engineering the Hard Way: Developing Spark Applications on Kubernetes Gavin Campbell
Self-hosting Spark in Containers
Data Science and Analytics: Real-World Experience from Diverse voices in the field Jennifer Stirrup (She/Hers)
In this session, we will cut through the marketing buzzwords to share experiences, tips, and tricks on how to be successful with Data Science and Analytics in the real world. Tune in to hear the team share real-world experience and get takeaways from industry insiders on real projects with impact. We will also discuss the ethics and fairness of Data Science and Analytics projects and how we can be more inclusive from a technology, people, and process standpoint.
The video is not available to view online.
Implementing a Data Quality Framework in Purview Ust Oldfield
Building a Data Quality implementation in Purview using the Glossary and Databricks
Leveraging Apache Spark for Efficient Data Encryption at Scale Daniel Harrington
This session will discuss a custom solution we have built to solve the problem of row level encryption in a highly complex data lake.
Migrate traditional DW transformations to Spark/Scala Sergiy Lunyakin
Join me to learn how to migrate a traditional DW transformation process to Spark/Scala based platform and reuse existed experience
Overview of Azure Synapse Analytics Dave Ruijter
Curious what Azure Synapse Analytics brings to the table? Bring your scuba gear, as we take a dive and explore everything it has to offer!
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