On-demand Webinar

Trino and Starburst Training Series

This 5-part training series takes you through designing high-performance data lake table structures and exploring Apache Iceberg features. We cover additional performance benefits of materialized views and leveraging our Warp Speed acceleration engine. Finally, we focus on data engineering pipeline options of SQL and Python.

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Session 1: Creating & querying data lake tables

Session details:
- Leverage Starburst Galaxy to create a catalog and schema aligned to an AWS S3 object store.
- Construct external tables to existing datasets.
- Utilize optimized columnar file formats to create new tables.
- Design partitioned tables to improve performance.
- Federate data lake queries to join with traditional data sources.

Session 2: Modern table formats & Apache Iceberg

Session details:
- Move beyond Hive to understand the benefits of modern table formats such as Apache Iceberg.
- Run ACID compliant transactions to modify your data and understand how this creates new table versions.
- Explore time-travel queries and table rollbacks.
- Modify the partitioning strategy without rebuilding your table.

Session 3: Data pipelines, views & data products

Session details:
- Learn how to create views and materialized view in Starburst Galaxy.
- Understand the refresh options for materialized views.
- Define popular strategies to include ETL/ELT, CDC, and SCD.
- Build a SQL-based pipeline to spans the reference architecture’s land, structure, and consume layers.
- Create and secure granular data products for your downstream consumers.

Session 4: Experience Warp Speed in action (Demo only)

Session details:
- Understand the architecture of this autonomous data lake acceleration technology.
- Query tables on standard and accelerated clusters to showcase the performance gains from the smart indexing and caching that occurs.
- Explore the web UI’s visualizations on how beneficial Warp Speed was for your queries.

Session 5: Transformation processing with PyStarburst

Session details:
- Differentiate SQL-based data engineering from programming-based.
- Understand how PyStarburst implements lazy execution with Starburst Galaxy.
- Explore the DataFrame API.
- Write Python code to perform analytical questions and transformation processing.

Meet the speaker:

Lester Martin

Educational Engineer at Starburst