SQL for Snowflake AI/ML Engineers: Data Wrangling, Feature Engineering, and Optimization
Master SQL for Snowflake to extract, transform, and optimize data for AI/ML, covering data wrangling, feature engineering, governance, and performance tuning.
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Introduction to Snowflake for AI/ML
Unit 1: Snowflake Fundamentals
Unit 2: Setting Up Your Snowflake Environment
Unit 3: Data Management Basics
SQL Fundamentals in Snowflake
Unit 1: Basic SQL Queries
Unit 2: Filtering and Sorting Data
Unit 3: Limiting Results and Data Types
Data Extraction and Loading
Unit 1: Introduction to Data Loading in Snowflake
Unit 2: Loading Data with COPY INTO
Unit 3: Loading Specific Data Formats
Unit 4: Advanced Data Loading Techniques
Data Filtering and Cleaning
Unit 1: Filtering Data with WHERE Clause
Unit 2: Advanced Filtering Techniques
Unit 3: Handling Missing Data
Data Type Conversion
Unit 1: Understanding Data Types in Snowflake
Unit 2: Explicit Data Type Conversion with CAST and CONVERT
Unit 3: Implicit Data Type Conversion and Error Handling
String Manipulation
Unit 1: Basic String Functions
Unit 2: Advanced String Manipulation
Unit 3: Regular Expressions
Date and Time Functions
Unit 1: Extracting Date Components
Unit 2: Extracting Time Components
Unit 3: Formatting Dates and Times
Unit 4: Date and Time Arithmetic
Aggregate Functions
Unit 1: Introduction to Aggregate Functions
Unit 2: Grouping Data with GROUP BY
Unit 3: Filtering Groups with HAVING
Joining Tables
Unit 1: Introduction to Joins
Unit 2: Outer Joins
Unit 3: Advanced Join Techniques
Unit 4: Optimizing Joins
Subqueries
Unit 1: Subquery Fundamentals
Unit 2: Correlated vs. Non-Correlated Subqueries
Unit 3: Advanced Subquery Techniques
Window Functions: Introduction
Unit 1: Understanding Window Functions
Unit 2: Basic Window Function Examples
Unit 3: Window Frames: ROWS and RANGE
Ranking Functions
Unit 1: Introduction to Ranking Functions
Unit 2: RANK Function
Unit 3: DENSE_RANK Function
Unit 4: ROW_NUMBER Function
Value Functions
Unit 1: LAG Function Fundamentals
Unit 2: LEAD Function Fundamentals
Unit 3: Advanced Value Function Applications
Statistical Aggregate Functions
Unit 1: Understanding Basic Statistical Aggregates
Unit 2: Percentiles and Quartiles
Unit 3: Outlier and Anomaly Detection
Advanced Filtering Techniques
Unit 1: Exists and Not Exists
Unit 2: All and Any Operators
Unit 3: Case Statements
Working with Semi-Structured Data (JSON)
Unit 1: JSON Fundamentals in Snowflake
Unit 2: Querying JSON Data
Unit 3: Flattening JSON Data
Creating and Managing Tables
Unit 1: Table Creation Fundamentals
Unit 2: Constraints and Keys
Unit 3: Altering and Managing Tables
Creating and Managing Views
Unit 1: Introduction to Views
Unit 2: Advanced View Techniques
Unit 3: View Management and Security
Data Partitioning and Clustering
Unit 1: Understanding Partitioning and Clustering
Unit 2: Implementing Partitioning and Clustering
Unit 3: Advanced Considerations and Best Practices
Data Governance and Security: Introduction
Unit 1: Data Governance Fundamentals
Unit 2: Data Security and Privacy
Unit 3: Access Control and Governance Implementation
Role-Based Access Control (RBAC)
Unit 1: Understanding RBAC in Snowflake
Unit 2: Creating and Managing Roles
Unit 3: Granting and Revoking Privileges
Unit 4: Assigning Roles to Users and Groups
Unit 5: Advanced RBAC Concepts
Data Masking
Unit 1: Introduction to Data Masking
Unit 2: Implementing Data Masking in Snowflake
Unit 3: Role-Based Data Masking and Advanced Techniques
Data Encryption
Unit 1: Encryption Fundamentals in Snowflake
Unit 2: Key Management and Certificates
Unit 3: Compliance and Security Best Practices
Data Auditing
Unit 1: Introduction to Data Auditing in Snowflake
Unit 2: Configuring Data Auditing
Unit 3: Monitoring User Activity
Unit 4: Analyzing Audit Logs
Unit 5: Advanced Auditing Techniques
SQL Optimization: Introduction
Unit 1: Understanding SQL Optimization
Unit 2: Deep Dive into Bottlenecks
Unit 3: EXPLAIN PLAN in Detail
Indexing Strategies
Unit 1: Understanding Indexing
Unit 2: Creating and Managing Indexes
Unit 3: Avoiding Over-Indexing
Query Rewriting Techniques
Unit 1: Introduction to Query Rewriting
Unit 2: Common Table Expressions (CTEs)
Unit 3: Subquery Optimization
Unit 4: Advanced Techniques
Join Optimization
Unit 1: Understanding Join Types
Unit 2: Advanced Join Techniques
Unit 3: Optimizing Joins
Filtering and Aggregation Optimization
Unit 1: Filtering Optimization
Unit 2: Aggregation Optimization
Unit 3: Advanced Optimization
Understanding Snowflake's Query Profile
Unit 1: Introduction to Snowflake's Query Profile
Unit 2: Navigating the Query Profile Interface
Unit 3: Identifying Performance Bottlenecks
Unit 4: Using Query Profile for Optimization
Data Sampling Techniques
Unit 1: Introduction to Data Sampling
Unit 2: Random Sampling with TABLESAMPLE
Unit 3: Stratified Sampling
Unit 4: Advanced Sampling Techniques
Unit 5: Practical Considerations
Feature Engineering with SQL: Introduction
Unit 1: Understanding Feature Engineering
Unit 2: Identifying Relevant Features
Unit 3: Transforming Raw Data
Creating Indicator Variables (One-Hot Encoding)
Unit 1: Introduction to Indicator Variables
Unit 2: Creating Indicator Variables with CASE Statements
Unit 3: One-Hot Encoding in Snowflake SQL
Unit 4: Handling High-Cardinality Features
Unit 5: Best Practices and Considerations
Binning Numerical Features
Unit 1: Introduction to Binning
Unit 2: Binning with CASE Statements
Unit 3: Binning with WIDTH_BUCKET
Unit 4: Advanced Binning Techniques
Feature Scaling and Normalization
Unit 1: Understanding Feature Scaling
Unit 2: Min-Max Scaling
Unit 3: Standardization (Z-Score)
Unit 4: Normalization Techniques
Unit 5: Applying Scaling in ML Workflows
Creating Interaction Features
Unit 1: Introduction to Interaction Features
Unit 2: Creating Interaction Features in SQL
Unit 3: Identifying Meaningful Interactions
Unit 4: Advanced Interaction Techniques
Text Feature Extraction
Unit 1: String Functions for Feature Eng
Unit 2: Word Count and Character Analysis
Unit 3: Regex for Text Feature Extraction
Date and Time Feature Engineering
Unit 1: Extracting Date Components
Unit 2: Time-Based Feature Creation
Unit 3: Calculating Time Differences
Geospatial Feature Engineering
Unit 1: Introduction to Geospatial Features
Unit 2: Distance Calculations
Unit 3: Proximity to Landmarks and Regions
Handling Missing Data in Feature Engineering
Unit 1: Understanding Missing Data
Unit 2: Basic Imputation Techniques
Unit 3: Indicator Variables
Unit 4: Advanced Imputation Techniques
Unit 5: Evaluating and Refining
Feature Selection Techniques
Unit 1: Introduction to Feature Selection
Unit 2: Filter Methods in Detail
Unit 3: Wrapper Methods
Unit 4: Embedded Methods & Considerations
Creating Feature Stores in Snowflake
Unit 1: Introduction to Feature Stores
Unit 2: Designing a Feature Store in Snowflake
Unit 3: Implementing and Managing the Feature Store
Unit 4: Advanced Feature Store Concepts
Data Validation and Quality Checks
Unit 1: Introduction to Data Validation
Unit 2: SQL Constraints for Validation
Unit 3: Data Profiling Techniques
Unit 4: Resolving Data Quality Issues
Automating Data Pipelines with Snowflake Tasks
Unit 1: Introduction to Snowflake Tasks
Unit 2: Creating and Scheduling Tasks
Unit 3: Advanced Task Management
Using Snowflake's External Functions for Feature Engineering
Unit 1: Introduction to External Functions
Unit 2: Setting Up External Functions
Unit 3: Advanced Use Cases & Optimization
Snowflake's Data Marketplace for AI/ML
Unit 1: Introduction to Snowflake Data Marketplace
Unit 2: Integrating Marketplace Data with Internal Data
Unit 3: Enhancing Feature Engineering with External Data
Advanced SQL Optimization Techniques
Unit 1: Materialized Views Deep Dive
Unit 2: Advanced Query Optimization
Unit 3: Snowflake Specific Optimizations
Resource Monitoring and Management
Unit 1: Introduction to Resource Monitoring
Unit 2: Virtual Warehouse Management
Unit 3: Scaling Policies
Unit 4: Optimizing Resource Allocation
Cost Optimization Strategies
Unit 1: Understanding Snowflake Cost Factors
Unit 2: Storage Cost Reduction
Unit 3: Compute Cost Optimization
Unit 4: Advanced Cost Management
Working with Large Datasets
Unit 1: Understanding Large Datasets in Snowflake
Unit 2: Data Partitioning and Clustering
Unit 3: Data Sampling Techniques
Parallel Processing in Snowflake
Unit 1: Understanding Parallelism
Unit 2: Optimizing Queries for Parallel Execution
Unit 3: Monitoring and Troubleshooting
Data Compression Techniques
Unit 1: Introduction to Data Compression
Unit 2: Snowflake Compression Algorithms
Unit 3: Compression by Data Type
Unit 4: Optimizing Compression
Automated Data Optimization
Unit 1: Introduction to Automated Optimization
Unit 2: Implementing Auto-Clustering
Unit 3: Leveraging Auto-Tuning
Unit 4: Managing and Monitoring Automated Optimization
Unit 5: Advanced Topics and Best Practices
Integrating SQL with Python for AI/ML
Unit 1: Setting Up the Environment
Unit 2: Executing SQL Queries
Unit 3: Data Analysis with Pandas
Unit 4: Machine Learning with Scikit-learn
Unit 5: Advanced Techniques
Using Snowpark for Python
Unit 1: Introduction to Snowpark
Unit 2: DataFrames in Snowpark
Unit 3: Feature Engineering with Snowpark
Unit 4: Advanced Snowpark Techniques
Building Machine Learning Pipelines with Snowpark
Unit 1: Introduction to Snowpark ML Pipelines
Unit 2: Training Models with Snowpark & Scikit-learn
Unit 3: Model Deployment and Management
Unit 4: Advanced Snowpark ML Pipelines
Data Visualization with SQL and Python
Unit 1: Introduction to Data Visualization
Unit 2: Basic Visualizations with SQL
Unit 3: Python Viz: Matplotlib
Unit 4: Python Viz: Seaborn
Unit 5: Advanced Visualization Techniques
Real-time Data Processing with Snowflake Streams
Unit 1: Introduction to Real-time Data Processing
Unit 2: Setting Up Snowflake Streams
Unit 3: Working with Snowflake Tasks
Unit 4: Real-time Feature Engineering Pipeline
Unit 5: Monitoring and Best Practices
Building a Real-time Feature Engineering Pipeline
Unit 1: Introduction to Real-time Feature Engineering
Unit 2: Setting Up the Real-time Pipeline
Unit 3: Implementing Real-time Feature Engineering
