Trino and Starburst Training Series

11:00am ET - 12:00pm ET

Register

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

The trainings will take place from 11:00am – 12:00pm EST on: 

  • January 31st – You will receive the recording when you sign up for the series!
  • February 14th – You will receive the recording when you sign up for the series!
  • February 28th
  • March 13th
  • March 27th

Sign up for one or all sessions! You will receive the session 1 and 2 recording when you sign up for the series! Details below. 

Sign up for the training series!

Session 1:

Creating & querying data lake tables

  • Details: This session has passed. Sign up to receive the recording!
  • Agenda:
    • 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 

  • Details: This session has passed. Sign up to receive the recording!
  • Agenda:
    • 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

  • Details: February 28 at 11:00am ET
  • Agenda: 
    • 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

  • Details: March 13 at 11:00am ET
  • Agenda:
    • 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

  • Details: March 27 at 11:00am ET
  • Agenda:
    • 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

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Starburst Galaxy

Up to $500 in usage credits included

  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
For more deployment options:
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