Join us for a FREE hands-on Meetup webinar on Deep Dive into Autoscaling in Apache Flink | Friday, March 28th, 2025 · 5:00 PM IST/ 07:30 AM EDT Join us for a FREE hands-on Meetup webinar on Deep Dive into Autoscaling in Apache Flink | Friday, March 28th, 2025 · 5:00 PM IST/ 07:30 AM EDT
Search
Close this search box.
Search
Close this search box.

Snowflake Data Engineer II

Advanced Techniques for Data Engineering: Mastering Data Modeling, Snowpark, Ingestion, and Performance Optimization on Snowflake

Duration

2 Days (8 hours per day)

Level

Advanced Level

Design and Tailor this course

Official Course

Edit Content

This two-day, role-specific course presents additional topics and a deep dive into select subjects for the Data Engineer through the lens of the data engineering lifecycle. The course covers Snowflake concepts, features, considerations, and best practices intended for stakeholders who will be accessing, developing, and querying datasets for analytic tasks, and building data pipelines in Snowflake. This course consists of data engineering concepts delivered through lectures, demos, labs, and discussions.

ACQUIRED SKILLS:

  • Develop applications for Snowflake, including comprehensive ANSI standard SQL support.
  • Identify and describe various data modeling techniques and architectures deployed on the Snowflake Platform.
  • Govern data stored and accessed in Snowflake effectively.
  • Exploit Snowflake capabilities to work effectively with structured, semi-structured, and unstructured data in Snowflake.
  • Use Snowflake’s SQL extensibility features, such as user-defined functions and stored procedures.
  • Employ the Snowpark client libraries to query and transform data in a data pipeline and build applications that process data in Snowflake without moving data to the system running the application.
  • Automate data ingestion and expand data lake capabilities using Snowflake.
Edit Content
  • Data Analysts
  • Data Engineers
  • Data Scientists
  • Database Architects
  • Database Administrators
  • Data Application Developers
Edit Content
  • Overview and Architecture Recap
  • Table Formats and Iceberg Tables
  • Iceberg Tables in Snowflake
  • Hybrid Tables in Snowflake
  • Define and Implement Hybrid Tables
  • Cost Analysis and Limitations
  • Schema Detection
  • Schema Evolution
  • Visualizing Data Ingestion
  • Developing for Snowflake Overview
  • User Defined Functions (Java and Python)
  • User Defined Table Functions (Java and Python)
  • Snowpark Stored Procedures (Java, Scala, and Python)
  • Working With Snowpark
  • Data Modeling
  • Data Vault Introduction
  • Data Governance Overview
  • Classification and Object Tagging
  • Object Dependencies
  • Access History
  • Snowflake Policies
  • Tag-based Masking Policies
  • External Tokenization
  • Search Optimization Service

  • Query Acceleration Service

  • Snowflake Python API
  • Snowflake SQL API
  • Streamlit in Snowflake
  • Scheduling Workflows with Airflow
  • Snowflake Python Task API
  • Observability on Snowflake
  • No-code Pipeline Observability Within Snowsight
  • Cost Governance Framework
  • Logging and Tracing
Edit Content
  • Successful completion of either “Snowflake Fundamentals” or “Snowflake Data Engineer” courses is recommended.

Connect

we'd love to have your feedback on your experience so far