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Dive Deeper into Data Engineering on Databricks

On Demand

Type

  • Session

Format

  • Hybrid

Track

  • Data Engineering

Room

  • Moscone South | Level 2 | 205

Duration

  • 35 min
Download session slides

Überblick

To derive value from data, engineers need to collect, transform, and orchestrate data from various data types and source systems. However, today’s data engineering solutions support only a limited number of delivery styles, involve a significant amount of hand-coding, and have become resource-intensive. Modern data engineering requires more advanced data lifecycle for data ingestion, transformation, and processing. In this session, learn how the Databricks Lakehouse Platform provides an end-to-end data engineering solution — ingestion, processing and scheduling — that automates the complexity of building and maintaining pipelines and running ETL workloads directly on a data lake, so your team can focus on quality and reliability to drive valuable insights.

Session Speakers

Frank Munz

Databricks

Paul Lappas

Staff Product Manager

Databricks

Headshot of Jason Shiverick

Jason Shiverick

Principal Data Scientist

Rivian

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