Learn Advanced Snowflake covering data transformation, performance optimization, Snowpark implementation, machine learning techniques and data governance strategies.

Syllabus

Track 1: Performance Monitoring and Optimization

This track equips learners with the tools and techniques needed to optimize Snowflake performance for large-scale data engineering tasks. You will explore the strategies for scaling workloads with virtual and multi-cluster warehouses, query optimization through data clustering and caching, and monitoring performance with query profiling and resource utilization tracking. Learners will also explore handling geospatial and semi-structured data, working with transient and dynamic tables, and optimizing queries through secure and materialized views.

Track 2: Data Transformation Using Snowpark

In this in-depth track, learners dive into Snowpark, Snowflake’s powerful framework for scalable data manipulation and transformation. Through hands-on experience with Snowpark DataFrames and integration with external systems like Kafka and Spark, learners will master tasks such as filtering, aggregating, and joining data. The track also covers the creation and management of user-defined functions (UDFs) and stored procedures, as well as data quality assurance using Soda and real-time data ingestion techniques.

Track 3: Continuous Data Pipelines

This track introduces learners about continuous data pipelines in Snowflake. Participants will learn how to create and configure dynamic tables and the usage and internal workings of streams for change data capture (CDC), stream types, and standard stream contents during insert, update, and delete operations. The final section of this track will be exploring continuous data processing tasks, creating and execute scheduled serverless and user-managed scheduled tasks, and implementing task graphs and child tasks.

Track 4: Advanced Analytics and Machine Learning

This track introduces learners to the world of machine learning within Snowflake. Participants will learn to design and deploy ML models using Snowpark and popular tools like scikit-learn. The track covers key areas such as data preprocessing, model training, hyperparameter tuning, and deployment through MLOps. Learners will also explore the application of large language models (LLMs) in Snowflake Cortex for tasks like sentiment analysis, translation, and summarization, as well as advanced techniques like time series forecasting and anomaly detection.