• Understand Data Warehousing Fundamentals – Gain foundational knowledge of data warehousing, including concepts and principles, and how they relate to Snowflake.
• Master Data Modeling Techniques – Learn best practices for designing efficient data models to optimize performance and storage in Snowflake.
• Explore Snowflake Architecture – Understand Snowflake’s unique architecture and how it supports cloud-native data warehousing and analytics.
• Create and Manage Data Warehouses in Snowflake – Develop practical skills in creating and managing data warehouses using Snowflake’s interface and tools.
• Efficiently Load and Unload Data – Learn to load and unload data using various methods, including from external storage solutions (AWS, Azure, GCP).
• Effectively Manage Complex Data Formats – Handle complex formats like JSON and Parquet using Snowflake.
• Implement Data Transformations – Gain expertise in performing transformations during the data loading process to clean and structure data efficiently.
• Learn Snowflake’s performance optimization features – caching, clustering and resource monitoring to ensure cost-effective and high-performance data operations.
• Leverage Time Travel, Fail Safe, and Zero-Copy Clone – Explore Snowflake’s advanced features like Time Travel, Fail Safe, and Zero-Copy Clone for data recovery.
• Manage Secure Data Sharing – Learn how to securely share data within and outside of Snowflake environments, including with non-Snowflake users.
• Implement Best Practices for Snowflake Administration – Master account administration, access management, and apply best practices for efficient Snowflake usage

A warm welcome to the Snowflake: End-to-End Cloud Data Warehousing & Analytics course by Uplatz.

Snowflake is a cloud-based data warehousing platform designed to handle massive volumes of structured and semi-structured data. It’s built from the ground up to leverage cloud infrastructure, offering scalability, performance, and ease of use. Snowflake is not tied to any specific cloud provider; it runs on AWS, Microsoft Azure, and Google Cloud Platform (GCP), providing flexibility for businesses to use their preferred cloud platform.

Snowflake’s architecture, scalability, and advanced features make it a powerful platform for modern data warehousing, analytics, and data engineering. Its flexibility to handle massive datasets, structured and semi-structured data, and multi-cloud capabilities has positioned it as a preferred choice for businesses looking to leverage cloud-native data platforms.

How Snowflake Works

Snowflake operates using a unique architecture that separates storage and compute, allowing for independent scaling of resources. Key methodology in its working involves:

1. Data Storage: Snowflake stores data in a compressed, columnar format on cloud storage. Data is logically organized into databases, schemas, and tables, but physically, Snowflake manages how data is stored and optimized on the backend.

2. Compute Layer (Virtual Warehouses): Compute resources, called virtual warehouses, are independent clusters of resources that process queries and workloads. Virtual warehouses can be scaled up or down based on performance needs and can run multiple, parallel queries without interfering with each other.

3. Cloud Services Layer: This layer manages metadata, optimization, security, and query parsing. It handles authentication, query planning, and transaction management, allowing Snowflake to offer features like automated scaling, data sharing, and access controls.

The separation of storage and compute makes Snowflake highly flexible. You can store large volumes of data without worrying about compute costs when the data is not being queried. Conversely, you can scale compute resources for demanding queries without impacting the storage cost.

Core Features of Snowflake

1. Separation of Storage and Compute: Snowflake allows independent scaling of compute resources (virtual warehouses) and storage. This flexibility helps optimize costs and performance based on workload requirements.

2. Multi-Cloud Availability: Snowflake runs on all major cloud platforms (AWS, Azure, GCP), offering cross-cloud functionality and flexibility in choosing cloud providers.

3. Instant Elasticity: Snowflake can instantly scale compute resources up or down based on workload demands. You can run multiple queries simultaneously without performance degradation.

4. Data Sharing: Snowflake offers secure data sharing across organizations or between Snowflake accounts without moving or copying data. This feature allows real-time data collaboration.

5. Support for Structured and Semi-Structured Data: Snowflake natively supports a wide range of data formats, including JSON, Parquet, Avro, and XML, making it easier to load and query semi-structured data alongside structured data.

6. Zero-Copy Cloning: This feature allows you to create a copy of databases, tables, and schemas instantly without duplicating the data. It enables quick testing or development without additional storage costs.

7. Time Travel and Fail-Safe: Time Travel allows users to access historical data versions for up to 90 days, facilitating recovery from accidental data changes or deletions. Fail-Safe provides an additional data recovery mechanism for a defined period.

8. Automatic Scaling and Concurrency: Snowflake automatically manages concurrency, allowing multiple users to query data simultaneously without affecting performance, and automatically scales up or down depending on demand.

9. Security and Compliance: Snowflake includes robust security features such as end-to-end encryption, role-based access controls, and multi-factor authentication (MFA). It complies with industry standards like GDPR, HIPAA, and SOC 2.

10. Snowpipe: Snowpipe is Snowflake’s continuous data ingestion tool that automates loading data from external sources (such as AWS S3, Azure Blob, GCP Storage) into Snowflake in near real-time.

Snowflake – Course Curriculum

1. Introduction to Data Warehouse – part 1

2. Introduction to Data Warehouse – part 2

3. Data Modelling – part 1

4. Data Modelling – part 2

5. Introduction to Snowflake and Architecture

6. Create Datawarehouse in Snowflake

7. Load Data in a Table

8. Snowflake Pricing and Resource Monitor

9. Loading Data from External Storage

10. Transformations while Loading

11. Copy Options and File Formats – part 1

12. Copy Options and File Formats – part 2

13. Loading of JSON

14. Loading of Parquet

15. Data Unloading

16. Performance Optimizations in Snowflake

17. Caching and Clustering

18. Loading Data from AWS External Storage

19. Snowpipe in AWS

20. Loading Data from Azure Cloud

21. Snowpipe in Azure

22. Loading and Uploading Data from GCP

23. Time Travel – part 1

24. Time Travel – part 2

25. Fail Safe and Types of Tables

26. Zero Copy Clone

27. Data Sharing – part 1

28. Data Sharing – part 2

29. Data Sharing with non-Snowflake Users – part 1

30. Data Sharing with non-Snowflake Users – part 2

31. Secure vs Normal View

32. Data Sampling

33. Scheduling Tasks

34. Materialized View – part 1

35. Materialized View – part 2

36. Dynamic Data Masking

37. Access Management and Account Administration – part 1

38. Access Management and Account Administration – part 2

39. Best Practices in Snowflake

Who this course is for:

• Data Engineers – Looking to build data pipelines and handle data transformations within Snowflake.
• Data Analysts – Aiming to perform data querying, reporting, and analysis on cloud-based data platforms.
• Beginners and newbies aspiring for a career in Cloud Data Warehousing and Snowflake.
• Data Architects – Designing scalable cloud data architectures using Snowflake’s features.
• Solution Architects – Architecting comprehensive solutions that integrate Snowflake for efficient data management and analytics.
• Cloud Engineers – Implementing and managing cloud infrastructure for data operations on Snowflake.
• Business Intelligence Professionals – Using Snowflake for analytics, reporting, and generating business insights.
• ETL Developers – Handling Extract, Transform, Load (ETL) processes using Snowflake for efficient data movement and transformation.
• Data Scientists – Working with large datasets, performing advanced analytics, and leveraging Snowflake for machine learning workflows.
• Cloud Data Engineers – Focused on developing data solutions in the cloud using Snowflake’s infrastructure.
• Database Administrators (DBAs) – Transitioning to cloud data platforms like Snowflake for managing and optimizing data storage and access.
• Developers – Needing to integrate Snowflake with other applications and services.
• IT Professionals – Transitioning to cloud-based data solutions and aiming to broaden their cloud data expertise.