Paul Lappas

This session explores how SQL-based ETL can accelerate development, simplify maintenance and make data transformation more accessible to both engineers and analysts. We'll walk through how Databricks Lakeflow Declarative Pipelines and Databricks SQL warehouse support building production-grade pipelines using familiar SQL constructs.Topics include: - Using streaming tables for real-time ingestion and processing - Leveraging materialized views to deliver fast, pre-computed datasets - Integrating with tools like dbt to manage batch and streaming workflows at scale By the end of the session, you’ll understand how SQL-first approaches can streamline ETL development and support both operational and analytical use cases.

Amazon Redshift enables users to scale up to meet user demand and optimize query speed. Automation capabilities make it easy to get started and optimize Amazon Redshift without the need for administrator intervention.