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Course Content
Module 1: Introduction to Data Engineering and Snowflake
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1.1 Fundamentals of Data Engineering
:: Overview of Data Engineering: Roles and Responsibilities
:: Key Concepts: Data Warehousing, ETL/ELT, Data Lakes, and Real-time Data Processing
:: The Importance of Scalability, Reliability, and Performance in Data Engineering
1.2 Introduction to Snowflake
:: Overview of Snowflake: Cloud Data Platform Features and Benefits
:: Understanding Snowflake’s Architecture: Virtual Warehouses, Databases, and Storage
:: Key Snowflake Concepts: Micro-partitions, Clustering, and Data Sharing
:: Snowflake Editions: Standard, Enterprise, Business Critical, and Virtual Private Snowflake
1.3 Setting Up Your Snowflake Environment
:: Creating and Configuring a Snowflake Account
:: Overview of Snowflake’s Web Interface: UI, Worksheet, and Admin Console
:: Managing Snowflake Roles, Users, and Access Control
:: Setting Up Virtual Warehouses for Compute Resources
Module 2: Data Modeling and Schema Design in Snowflake
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2.1 Understanding Data Modeling
:: Introduction to Data Modeling Concepts: OLAP vs. OLTP
:: Normalization vs. Denormalization in Data Warehousing
:: Star Schema, Snowflake Schema, and Hybrid Models
2.2 Schema Design in Snowflake
:: Creating and Managing Databases, Schemas, and Tables
:: Best Practices for Designing Efficient Snowflake Schemas
:: Handling Semi-structured Data: JSON, Avro, and Parquet
:: Using VARIANT Data Type for Semi-structured Data
2.3 Data Partitioning and Clustering
:: Understanding Snowflake’s Micro-partitioning Technology
:: Implementing Clustering for Query Performance Optimization
:: Automating Clustering and Managing Cluster Keys
2.4 Real-time Scenario: Designing a Data Warehouse Schema in Snowflake
:: Defining Data Requirements and Business Logic
:: Implementing a Star Schema for a Retail Analytics Solution
:: Managing Semi-structured Data in a Hybrid Schema
Module 3: Data Loading and Integration
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3.1 Data Loading Techniques
:: Overview of Data Loading Options in Snowflake
:: Loading Data from CSV, JSON, Avro, and Parquet Files
:: Using Snowflake’s COPY Command for Bulk Data Loading
:: Data Validation and Error Handling during Loading
3.2 Data Integration with External Sources
:: Integrating Snowflake with Cloud Storage: Amazon S3, Azure Blob, Google Cloud Storage
:: Loading Data from External Tables and Stages
:: Using Snowpipe for Continuous Data Ingestion
:: Integrating with REST APIs and Web Services
3.3 Data Transformation and ELT in Snowflake
:: Introduction to Snowflake SQL for Data Transformation
:: Using Streams and Tasks for Continuous Data Transformation
:: Implementing ELT Pipelines with Snowflake’s Stored Procedures
:: Real-time Scenario: Implementing an ELT Pipeline for Customer Data
3.4 Real-time Scenario: Data Loading and Integration Project
:: End-to-end Data Loading from Multiple Sources
:: Automating Data Ingestion and Transformation Processes
:: Handling Data Quality and Integrity Issues
Module 4: Advanced Data Engineering with Snowflake
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4.1 Advanced SQL Techniques in Snowflake
:: Working with Window Functions, CTEs, and Subqueries
:: Advanced Data Aggregation and Grouping Techniques
:: Optimizing SQL Queries for Performance
:: Real-time Scenario: Complex SQL Queries for Business Analytics
4.2 Snowflake Data Sharing and Collaboration
:: Introduction to Secure Data Sharing in Snowflake
:: Creating and Managing Data Shares
:: Cross-cloud and Cross-region Data Sharing
:: Use Cases: Collaborative Data Analysis and Data Monetization
4.3 Snowflake Data Pipelines and Automation
:: Orchestrating Data Pipelines with Snowflake and Apache Airflow
:: Automating Data Workflows with Streams and Tasks
:: Real-time Data Processing with Snowflake and Kafka
:: Real-time Scenario: Building an Automated Data Pipeline for Real-time Analytics
4.4 Real-time Scenario: Implementing a Complex Data Pipeline
:: Designing a Data Pipeline for a Global E-commerce Platform
:: Integrating Real-time Data Sources and Batch Data Processing
:: Ensuring Pipeline Scalability, Reliability, and Performance
Module 5: Performance Tuning and Optimization
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5.1 Query Performance Optimization
:: Understanding Snowflake’s Query Execution and Caching Mechanisms
:: Best Practices for Writing Efficient Queries
:: Using Query Profiling and Performance Monitoring Tools
:: Real-time Scenario: Optimizing Query Performance for Large Datasets
5.2 Storage and Compute Optimization
:: Optimizing Data Storage with Compression and Clustering
:: Managing Compute Resources: Virtual Warehouse Scaling and Concurrency
:: Cost Optimization Techniques for Compute and Storage in Snowflake
:: Real-time Scenario: Implementing Cost-saving Strategies for Data Processing
5.3 Monitoring and Troubleshooting in Snowflake
:: Using Snowflake’s Query History and Performance Views
:: Monitoring Data Pipelines and Workloads
:: Troubleshooting Common Issues in Snowflake Environments
:: Real-time Scenario: Debugging and Optimizing a Production Data Pipeline
Module 6: Security, Compliance, and Governance
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6.1 Security Best Practices in Snowflake
:: Understanding Snowflake’s Security Architecture
:: Managing Access Control with Roles and Privileges
:: Implementing End-to-End Encryption and Data Masking
:: Real-time Scenario: Implementing a Secure Data Architecture in Snowflake
6.2 Compliance and Data Governance
:: Ensuring Compliance with Industry Standards: GDPR, HIPAA, SOC 2
:: Implementing Data Lineage and Auditing in Snowflake
:: Data Classification and Data Privacy in Snowflake
:: Real-time Scenario: Designing a Compliance-ready Data Platform
6.3 Data Governance Tools and Integrations
:: Integrating Snowflake with Data Catalogs and Governance Tools
:: Managing Data Quality and Lineage with Third-party Tools
:: Implementing Data Governance Policies and Best Practices
:: Real-time Scenario: Building a Data Governance Framework
Module 7: Machine Learning and Advanced Analytics with Snowflake
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7.1 Introduction to Machine Learning with Snowflake
:: Overview of Snowflake’s Integration with Machine Learning Tools
:: Using Snowflake with Python and R for Data Science
:: Data Preparation and Feature Engineering in Snowflake
7.2 Integrating Snowflake with Machine Learning Platforms
:: Integrating Snowflake with DataRobot, H2O.ai | Convergence of The World’s Best Predictive & Generative AI , and other ML Platforms
:: Building Predictive Models using Snowflake and Python (with Pandas, Scikit-learn)
:: Deploying and Managing Machine Learning Models in Snowflake
7.3 Real-time Scenario: Implementing an End-to-End ML Pipeline
:: Designing an ML Pipeline with Snowflake and Google Vertex AI
:: Training and Deploying a Predictive Model for Sales Forecasting
:: Real-time Scoring and Batch Prediction in Snowflake
7.4 Advanced Analytics with Snowflake
:: Implementing Time Series Analysis, Regression, and Classification Models
:: Using Snowflake’s Native SQL Functions for Analytics
:: Real-time Scenario: Advanced Customer Segmentation Analysis
Module 8: Project and Real-world Applications
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8.1 Project: End-to-End Data Engineering Solution on Snowflake
:: Project Definition: Identifying a Real-world Business Problem
:: Designing and Implementing a Data Architecture on Snowflake
:: Building and Automating Data Pipelines for Data Ingestion and Transformation
:: Implementing Advanced Analytics and Reporting Solutions
8.2 Case Studies of Snowflake in Industry
:: Real-world Applications of Snowflake in Retail, Finance, Healthcare, and More
:: Analysis of Successful Snowflake Implementations
:: Challenges and Solutions in Deploying Snowflake at Scale
8.3 Career Development and Certification Preparation
:: Overview of Snowflake Certification Programs: SnowPro Core, Advanced Certifications
:: Exam Objectives and Key Topics Review
:: Practice Questions and Mock Exams
:: Tips and Strategies for Passing Snowflake Certifications
8.4 Career Development and Job Search Strategies
:: Building a Portfolio of Data Engineering Projects on GitHub
:: Crafting a Resume and Preparing for Data Engineering Interviews
:: Networking and Staying Updated with Snowflake and Data Engineering Trends
Module 9: Future Trends and Research in Data Engineering
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9.1 The Future of Data Engineering with Snowflake
:: Emerging Trends in Cloud Data Warehousing and Data Engineering
:: The Role of AI and ML in the Future of Data Engineering
:: Snowflake’s Roadmap and Upcoming Features
9.2 Continuing Education and Lifelong Learning
:: Recommended Resources: Books, Courses, and Research Papers
:: Participating in Snowflake Community and Conferences
:: Contributing to Open Source Projects and Collaborating with Peers
Snowflake
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