Introduction

The SnowPro® Advanced Data Analyst Training is designed for professionals seeking to master data analytics, reporting, and performance optimization in Snowflake. This expert-level course focuses on advanced querying techniques, data visualization, machine learning integration, and best practices for data governance and security. By leveraging Snowflake’s powerful analytics capabilities, participants will gain hands-on experience in optimizing queries, handling large datasets, and integrating BI and AI-driven analytics.

Prerequisites of SnowPro®

• SnowPro Core Certification (recommended but not mandatory)
• Strong knowledge of SQL and data analysis techniques
• Experience with BI tools such as Tableau, Power BI, or Looker
• Familiarity with Snowflake’s architecture, performance tuning, and security models

Table of Contents

1. Advanced SQL for Snowflake Analytics

• 1.1 Window Functions and Analytical Queries
• 1.2 Common Table Expressions (CTEs) and Recursive Queries
• 1.3 JSON, Semi-Structured Data, and Variant Data Handling

2. Query Performance and Optimization

• 2.1 Using Query Profile for Performance Insights
• 2.2 Best Practices for Indexing and Partitioning
• 2.3 Optimizing Queries with Materialized Views and Clustering

3. Data Warehousing and ELT Best Practices

• 3.1 Modern Data Warehouse Concepts in Snowflake
• 3.2 ELT vs. ETL: Best Strategies for Snowflake Integration
• 3.3 Automating Data Pipelines for Large-Scale Processing

4. Business Intelligence (BI) and Data Visualization

• 4.1 Integrating Snowflake with Tableau, Power BI, and Looker
• 4.2 Building Interactive Dashboards with Live Data
• 4.3 Optimizing Performance for BI Query Workloads

5. Data Governance and Security for Analysts

• 5.1 Implementing Role-Based Access Control (RBAC)
• 5.2 Data Masking, Row-Level Security, and Secure Views
• 5.3 Ensuring Compliance with Audit Logging and Data Policies

6. Time-Series and Predictive Analytics

• 6.1 Handling Time-Series Data in Snowflake(Ref: SnowPro® Advanced Administrator Training)
• 6.2 Predictive Modeling with SQL and Snowflake ML Functions
• 6.3 Anomaly Detection and Trend Analysis

7. Machine Learning and AI with Snowflake

• 7.1 Using Snowpark for Advanced Data Processing
• 7.2 ML Model Training and Deployment within Snowflake
• 7.3 Integrating Snowflake with Python, TensorFlow, and Scikit-Learn

8. Cost Optimization and Performance Monitoring

• 8.1 Warehouse Sizing Strategies for Cost Efficiency
• 8.2 Monitoring Query Costs and Resource Utilization
• 8.3 Automating Cost Controls with Snowflake Functions

9. Real-Time Data Processing and Streaming Analytics

• 9.1 Using Snowpipe for Continuous Data Ingestion
• 9.2 Processing Streaming Data with Kafka and Snowflake
• 9.3 Implementing Event-Driven Architectures for Analytics

10. Case Studies and Best Practices

• 10.1 Real-World Use Cases of Advanced Analytics in Snowflake
• 10.2 Troubleshooting Common Performance and Data Issues
• 10.3 Best Practices for Scalable and Efficient Data Analysis

Conclusion

The SnowPro® Advanced Data Analyst Training equips professionals with the skills to perform expert-level data analysis, optimize query performance, and integrate BI and AI tools with Snowflake. By completing this training, analysts can drive data-driven decision-making, enhance business intelligence capabilities, and maximize the value of Snowflake’s data platform.

Reference