Virtual on-demand series:
Trino and Starburst training series
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.
