- Introduction to Snowflake
- Overview of cloud data warehousing
- Understanding Snowflake’s architecture (separation of storage, compute, and services)
- Comparison with traditional data warehouses and other cloud platforms
- Snowflake Architecture
- Key components:
- Virtual Warehouses (compute)
- Databases (storage)
- Metadata Services
- Cloud deployment options: AWS, Azure, and Google Cloud
- Understanding the multi-cluster architecture
- Data Loading and Unloading
- Loading structured and semi-structured data (e.g., CSV, JSON, Parquet)
- Using Snowpipe for continuous data ingestion
- Copy commands and bulk loading
- Unloading data to external storage
- Querying Data with SQL
- Basics of SQL in Snowflake
- Advanced SQL concepts:
- Window functions
- Common table expressions (CTEs)
- Time travel and data cloning
- Performance optimization and query tuning
- Data Sharing and Collaboration
- Secure Data Sharing with Snowflake
- Using Data Marketplace for third-party datasets
- Cross-region and cross-cloud data sharing
- Snowflake’s Advanced Features
- Streams and Tasks (for change data capture and automation)
- Materialized Views
- Stored Procedures and scripting
- External Functions (integrating with cloud services)
- Security and Governance
- Role-based access control (RBAC)
- Data encryption and masking
- Auditing and logging activities
- Compliance with data privacy regulations (GDPR, HIPAA)
- Performance Optimization
- Best practices for warehouse sizing and scaling
- Query performance tuning
- Resource monitors and cost control
- Integration and Ecosystem
- Integrating with BI tools (Tableau, Power BI, Looker)
- Connecting with ETL tools (Informatica, Talend, Apache Spark)
- APIs and connectors (JDBC, ODBC, Python)
- Hands-On Labs and Projects
- Building a cloud data warehouse
- Creating and optimizing ETL pipelines
- Real-world analytics and reporting scenarios
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