Snowflake

Module 1: Introduction to Snowflake & Core Architecture

• Introduction to Data Warehousing Concepts:?
• What is a Data Warehouse? OLTP vs. OLAP.
• Traditional Data Warehousing challenges.
• Evolution to Cloud Data Warehouses.

• Introduction to Snowflake:
• What is Snowflake? Key features and benefits.
• Snowflake editions (Standard, Enterprise, Business Critical, Virtual Private Snowflake).
• Use cases for Snowflake.

• Snowflake Architecture:
• The three layers: Cloud Services, Query Processing (Virtual Warehouses), and Database Storage.
• Separation of compute and storage.
• Micro-partitions and clustering.
• Data encryption (at rest and in transit).

• Snowflake Ecosystem & Connectivity:
• Snowflake clients: Snowsight (Web UI), SnowSQL (CLI), JDBC/ODBC drivers.
• Connecting to Snowflake from various applications.

• Account and User Management:
• Creating and managing Snowflake accounts.
• Understanding Snowflake billing and credit usage.
• Overview of system usage and billing.

Module 2: Data Definition Language (DDL) and Data Modeling

• Database and Schema Management:
• Creating, altering, and dropping databases and schemas.
• Understanding logical organization of data.

• Table Management:
• Creating tables: structured data types, default values.
• Table types: Permanent, Transient, Temporary.
• Altering and dropping tables.

• Views and Materialized Views:
• Creating and using standard views.
• Understanding materialized views for performance optimization.
• Best practices for view creation.

• Virtual Warehouses:
• Creating and managing virtual warehouses (compute resources).
• Warehouse sizing and scaling (auto-suspend, auto-resume).
• Understanding multi-cluster warehouses for concurrency.
• Resource Monitors for cost control.

• Basic Data Modeling Concepts for Snowflake:
• Star Schema vs. Snowflake Schema (relevance in Snowflake).
• Denormalization strategies.
• Choosing appropriate data types for optimal performance.

Module 3: Data Loading and Unloading

• Data Staging:
• Understanding Snowflake stages: User, Table, and Named stages.
• External stages (AWS S3, Azure Blob, Google Cloud Storage).
• PUT and GET commands for staging files.

• Bulk Data Loading using COPY INTO:
• Loading structured data (CSV, TSV).
• Loading semi-structured data (JSON, Avro, Parquet, XML).
• File formats, error handling, and ON_ERROR options.
• Pattern matching for loading multiple files.

• Continuous Data Loading with Snowpipe:
• Introduction to Snowpipe for automated data ingestion.
• Configuring Snowpipe for real-time data loading.
• Monitoring Snowpipe.

• Data Unloading using COPY INTO
• Exporting data from Snowflake tables to stages.
• Unloading data in various formats.
• Downloading unloaded data to local systems.

• Data Loading Best Practices:
• Optimizing data load performance.
• Handling large files and partitioning.

Data Manipulation Language (DML) & Advanced SQL

• Basic DML Operations:
• INSERT, UPDATE, DELETE, MERGE statements.
• Working with TRUNCATE TABLE.

• Querying Data:
• SELECT statements: basic syntax, filtering (WHERE), ordering (ORDER BY).
• Aggregate functions (COUNT, SUM, AVG, MIN, MAX).
• Grouping data (GROUP BY, HAVING).
• Joining tables (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN).

• Subqueries and CTEs (Common Table Expressions):
• Understanding subqueries and their usage.
• Leveraging CTEs for complex queries and readability.

• Window Functions:
• Introduction to analytic functions (e.g., ROW_NUMBER(), RANK(), LEAD(), LAG(), NTH_VALUE()).
• Applying window functions for complex analytical tasks.

• Working with Semi-Structured Data:
• Understanding VARIANT data type.
• Flattening and parsing JSON, XML, and Avro data using FLATTEN, PARSE_JSON, GET.
• Querying nested semi-structured data.

• Time Travel and Fail-Safe:
• Querying historical data using AT and BEFORE clauses.
• Restoring dropped objects.
• Understanding data retention periods.
• Fail-safe for disaster recovery.

• Zero-Copy Cloning:
• Instant cloning of databases, schemas, and tables.
• Use cases for cloning (dev/test environments, data backups).

Module 5: Data Security and Access Control

• Understanding Snowflake's hierarchical RBAC model.
• System-defined roles and custom roles.
• Granting and revoking privileges on databases, schemas, tables, and warehouses.
• Best practices for role design.

• User Management and Authentication:
• Creating and managing users.
• User authentication methods: password, federated authentication (SSO), key-pair authentication.

• Data Masking:
• Understanding dynamic data masking for sensitive data.
• Creating and applying masking policies.

• Row Access Policies:
• Implementing row-level security for data governance.
• Creating and applying row access policies.

• Network Security:
• Network policies to restrict network access to Snowflake.
• Private Connectivity (AWS PrivateLink, Azure Private Link, Google Cloud Private Service Connect - overview).

• Auditing and Monitoring:
• Using ACCOUNT_USAGE and INFORMATION_SCHEMA for auditing.
• Monitoring user activities and data access.

Module 6: Performance Optimization and Cost Management

• Query Optimization Techniques:
• Analyzing query plans using EXPLAIN.
• Using Query Profile for detailed insights into query execution.
• Understanding data pruning and micro-partitioning.
• Best practices for writing efficient SQL queries.

• Virtual Warehouse Optimization:
• Right-sizing warehouses for different workloads.
• Managing concurrency and queuing.
• Auto-scaling strategies.

• Data Clustering:
• Understanding automatic clustering.
• Implementing clustering keys for improved query performance on large tables.

• Caching in Snowflake:
• Result caching, warehouse caching, metadata caching.
• Maximizing cache utilization.

• Search Optimization Service:
• Accelerating point lookups and equality predicates.

• Cost Management Strategies:
• Monitoring credit usage and billing.
• Implementing resource monitors for budget control.
• Utilizing transient tables and managing Time Travel retention.

Module 7: Advanced Features & Integrations

• Streams and Tasks:
• Introduction to Streams for Change Data Capture (CDC).
• Creating and managing Tasks for scheduled execution of SQL statements.
• Building simple ETL/ELT pipelines with Streams and Tasks.

• Stored Procedures and User-Defined Functions
• Creating and using SQL UDFs.
• Introduction to Python/Java/Scala UDFs and Stored Procedures using Snowpark (overview).

• Data Sharing:
• Secure data sharing: providers and consumers.
• Creating and managing shares.
• Snowflake Marketplace and Data Exchange (overview).

• External Tables:
• Querying data directly from external cloud storage without loading.
• Use cases and considerations.

• Data Integration Tools (Overview):
• Brief overview of popular ETL/ELT tools that integrate with Snowflake (e.g., Fivetran, Matillion, Talend, dbt).

• Business Intelligence (BI) Tool Integration (Overview):
• Connecting Snowflake to BI tools like Tableau, Power BI, Looker.

Module 8: Capstone Project & Best Practices

• End-to-End Data Pipeline Project:
• Design and implement a data pipeline using Snowflake, covering data ingestion, transformation, and consumption.
• Incorporate learned concepts: data loading, SQL transformations, security, and performance considerations.

• Snowflake Best Practices:
• Data governance and organization.
• Naming conventions.
• Error handling and logging.
• Monitoring and alerting strategies.

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