Advanced Snowflake eLearning

The Advanced Snowflake LearningKit is designed to provide data engineers and advanced users with the skills to fully leverage Snowflake’s platform for data transformation, optimization, advanced analytics, and data governance. This comprehensive learning is divided into four key tracks, each focusing on a specialized aspect of data engineering. The curriculum emphasizes performance optimization strategies, leveraging Snowpark for complex data transformations, applying machine learning techniques, and ensuring robust data governance and security.

By the end of this learning, participants will have deep expertise in managing high-performance workloads, implementing machine learning models, and maintaining data security on Snowflake.

• 12 Months Online Access
• 25+ hours of content
• 4 Assessments
• Tips & Tricks

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.

Courses (7 hours +):

• Snowflake Performance: Scaling and Autoscaling Warehouses
• Snowflake Performance: Query Acceleration and Caching
• Snowflake Performance: Clustering and Search Optimization
• Snowflake Performance: Iceberg Tables, External Tables, and Views

Assessment

• Final Exam: Snowflake Performance Monitoring and Optimization

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.

Courses (5 hours +):

• Data Transformation Using the Snowpark API
• Snowpark pandas and User-defined Functions
• Snowpark UDTFs, UDAFs, and Stored Procedures

Assessment

• Final Exam: Data Transformation Using Snowpark

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.

Courses (4 hours +):

• Continuous Data Pipelines and Dynamic Tables in Snowflake
• Streams and Change Data Capture in Snowflake
• Using Tasks and Architecting Snowflake Data Pipelines

Assessment

• Final Exam: Continuous Data Pipelines in Snowflake

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.

Courses (9 hours +):

• Snowpark ML APIs and the Model Registry
• Snowflake Feature Store and Datasets
• Using Streamlit with Snowflake
• Anomaly Detection with Snowflake ML Functions
• Snowflake Forecasting Models and the AI & ML Studio
  Snowflake Cortex for LLMs, RAG, and Search

Assessment

• Final Exam: Advanced Analytics and Machine Learning in Snowflake