Snowflake: A Comprehensive Guide to Cloud Data Warehousing

Snowflake: A Comprehensive Guide to Cloud Data Warehousing udemy course free download

Snowflake training: from beginner to advanced. Transform data strategy, build rapid data insights, accelerate analytics.

A warm welcome to the Snowflake: A Comprehensive Guide to Cloud Data Warehousing course by Uplatz.

Snowflake is a cloud-based data platform that provides data warehousing, data engineering, data lakes, and analytics capabilities. It is built on top of cloud infrastructure providers like AWS, Azure, and Google Cloud. Unlike traditional databases, Snowflake separates compute and storage, allowing for scalability, high performance, and cost efficiency.

Snowflake enables businesses to store, process, and analyze massive amounts of structured and semi-structured data in a highly secure, scalable, and serverless manner.

How Snowflake Works?

Snowflake operates using a multi-cluster shared data architecture, which consists of three key layers:

1. Storage Layer

   • Stores structured and semi-structured data in a compressed, optimized, and columnar format.

   • Data is automatically partitioned and distributed across multiple storage units for high availability.

2. Compute Layer (Virtual Warehouses)

   • Virtual warehouses (clusters) process queries and workloads independently.

   • Compute and storage are decoupled, meaning warehouses can scale up/down without affecting stored data.

   • Multiple warehouses can run concurrently, allowing multiple users and teams to work on different workloads.

3. Cloud Services Layer

   • Manages security, metadata, query optimization, and authentication.

   • Supports features like auto-scaling, automatic failover, and query optimization.

Core Features of Snowflake

1. Multi-Cloud Support

   • Runs seamlessly on AWS, Azure, and Google Cloud, allowing businesses to choose their preferred cloud provider.

2. Separation of Compute and Storage

   • Users can scale compute resources independently from storage, reducing costs by paying only for what is used.

3. Instant & Elastic Scalability

   • Snowflake automatically scales up or down based on workload demands, ensuring high performance and efficiency.

4. Zero-Copy Cloning

   • Users can create multiple copies of a dataset without duplicating storage, making it easier to test and develop without extra cost.

5. Data Sharing & Collaboration

   • Snowflake enables secure data sharing across different accounts, organizations, and cloud providers without the need to copy data.

6. Time Travel & Fail-Safe

   • Time Travel allows users to restore data from historical snapshots (up to 90 days).

   • Fail-Safe provides additional protection for recovering lost data.

7. Support for Semi-Structured Data

   • Natively supports JSON, Avro, ORC, Parquet, and XML, allowing schema-on-read flexibility.

8. Automatic Performance Optimization

   • Snowflake automatically optimizes storage and query execution without requiring manual indexing or tuning.

9. Built-in Security & Compliance

   • Features end-to-end encryption, access control, and role-based security.

   • Complies with GDPR, HIPAA, SOC 2, and other industry standards.

10. Snowpark & Python Support

    • Snowflake's Snowpark allows developers to use Python, Java, and Scala for data transformation and machine learning.

Benefits of Learning Snowflake

1. High Demand for Snowflake Professionals

   • Many companies are migrating to cloud-based data platforms, making Snowflake skills highly valuable in the job market.

2. Better Career Opportunities

   • Snowflake knowledge opens doors to roles like Data Engineer, Data Analyst, Cloud Data Architect, and Snowflake Consultant.

3. Competitive Salaries

   • Snowflake professionals earn high salaries, as demand exceeds supply in the job market.

4. Ease of Learning & Use

   • Unlike traditional databases, Snowflake requires minimal administration, making it easier for beginners to learn.

5. Cloud & Big Data Expertise

   • Learning Snowflake enhances cloud computing and data warehousing skills, which are essential in the modern data-driven industry.

6. Integration with BI & AI Tools

   • Snowflake integrates with Tableau, Power BI, Looker, Python, TensorFlow, and many other analytics and AI tools.

7. Future-Proof Technology

   • Snowflake is growing rapidly as enterprises shift to cloud-based, scalable solutions.

By mastering Snowflake, professionals can position themselves at the forefront of cloud data warehousing, analytics, and big data processing, making it a valuable skill for future-proofing careers in data engineering, analytics, and cloud computing.

Snowflake: A Comprehensive Guide to Cloud Data Warehousing - Course Curriculum

Section 1: Introduction to Snowflake

• Overview of Data Warehousing

• Importance of Cloud Computing

• The Snowflake Story: Evolution & Use Cases

Section 2: Getting Started with Snowflake

• Signing Up for Snowflake

• Exploring the Snowflake UI

• Creating Databases, Schemas, and Tables

• Loading Data into a Table

• Setting Up Essential Snowflake Tools

• Assignment: Create, Load & Query a Table

Section 3: Snowflake Compute - Virtual Warehouses

• Creating Virtual Warehouses

• Warehouse Sizes & Scalability

• Maximized vs. Auto Scaling Modes

• Multi-Cluster Warehouse Scaling Policies

• Assignment: Create a New Virtual Warehouse

Section 4: Architecture, Features & Pricing

• Snowflake Key Concepts & Architecture

• Cloud Platform Support & Global Regions

• Snowflake Editions & Releases

• Understanding Snowflake Pricing

• Data Integration & Interoperability

• Quiz: Snowflake Concepts

Section 5: Loading & Unloading Structured Data

• Data Ingestion Methods & Best Practices

• Steps for Managing Data Loads

• Preparing & Staging Data

• Loading Data from Internal & External Stages

• Snowpipe: Real-Time Data Loading

• Quiz: Data Ingestion in Snowflake

Section 6: Semi-Structured Data Handling

• Loading & Unloading JSON Data

• Running Analytics on JSON Data

• Working with ORC & Parquet Formats

• Assignment: Load JSON Data from an S3 Bucket

Section 7: Data Transformations & Staging

• Querying & Transforming Data in Staged Files

• Metadata Insights for Staged Files

• Transformations During Data Load

Section 8: Managing Databases, Tables & Views

• Temporary, Transient & Permanent Tables

• External Tables & Their Uses

• Overview of Views & Materialized Views

• Table Design Considerations

Section 9: Time Travel, Failsafe & Zero Copy Clones

• Time Travel: Restoring to a Specific Point

• Assignment: Implement Time Travel & Recovery

• Understanding Failsafe & Storage Utilization

• Assignment: Analyze Storage Used by Fail-Safe

• Zero Copy Cloning & Cloning with Time Travel

• Quiz: Time Travel & Zero Copy Clones

Section 10: Performance Optimization

• Optimization Strategies in Snowflake

• Using Dedicated Virtual Warehouses

• Scaling Out with Multi-Cluster Virtual Warehouses

• Maximizing Query Cache Utilization

• Lab: Query Caching in Action

• Clustering Large Tables for Better Performance

• Lab: Implementing Cluster Keys

• Search Optimization Techniques

• Quiz: Performance Optimization

Section 11: Secure Data Sharing

• Secure Data Sharing Concepts

• Sharing Data with Snowflake & Non-Snowflake Users

• Assignment: Share a Table with Another User

• Lab: Sharing Schemas, Databases & Views

• Quiz: Secure Data Sharing

Section 12: Snowflake Access Management

• Snowflake’s Role-Based Access Control Model

• Role Hierarchy: ACCOUNTADMIN, SYSADMIN, SECURITYADMIN

• Managing Custom Roles & Permissions

• Lab: Assigning Privileges via Custom Roles

• Quiz: Snowflake Access Management

Section 13: Advanced Features

• Change Tracking with Table Streams

• Automating Workflows with Tasks

• User-Defined Functions (UDFs) & Stored Procedures

• Column-Level & Row-Level Security

• Implementing Resource Monitors