Snowflake: A Comprehensive Guide To Cloud Data Warehousing

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

What you'll learn

Understand data warehousing & cloud computing and Snowflake’s unique architecture.

Set up and navigate Snowflake, creating databases, schemas, and tables.

Load and manage structured & semi-structured data (JSON, ORC, Parquet).

Optimize virtual warehouses for cost and performance efficiency.

Implement data ingestion & real-time streaming using Snowpipe and staging.

Use Time Travel & Zero Copy Cloning for data recovery and replication.

Improve query performance with caching, clustering, and search optimization.

Manage secure data sharing & access control with RBAC.

Utilize advanced Snowflake features like table streams, tasks, and UDFs.

Monitor and optimize Snowflake costs using pricing and resource analysis.

Integrate Snowflake with BI & data tools like Tableau, Power BI, and Spark.

Apply security best practices including encryption and row/column-level security.

Automate workflows using Snowflake tasks.

Gain hands-on experience through assignments, labs, and real-world use cases.

Requirements

Enthusiasm and determination to make your mark on the world!

Description

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:Storage LayerStores 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.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.Cloud Services LayerManages security, metadata, query optimization, and authentication.Supports features like auto-scaling, automatic failover, and query optimization.Core Features of SnowflakeMulti-Cloud SupportRuns seamlessly on AWS, Azure, and Google Cloud, allowing businesses to choose their preferred cloud provider.Separation of Compute and StorageUsers can scale compute resources independently from storage, reducing costs by paying only for what is used.Instant & Elastic ScalabilitySnowflake automatically scales up or down based on workload demands, ensuring high performance and efficiency.Zero-Copy CloningUsers can create multiple copies of a dataset without duplicating storage, making it easier to test and develop without extra cost.Data Sharing & CollaborationSnowflake enables secure data sharing across different accounts, organizations, and cloud providers without the need to copy data.Time Travel & Fail-SafeTime Travel allows users to restore data from historical snapshots (up to 90 days).Fail-Safe provides additional protection for recovering lost data.Support for Semi-Structured DataNatively supports JSON, Avro, ORC, Parquet, and XML, allowing schema-on-read flexibility.Automatic Performance OptimizationSnowflake automatically optimizes storage and query execution without requiring manual indexing or tuning.Built-in Security & ComplianceFeatures end-to-end encryption, access control, and role-based security.Complies with GDPR, HIPAA, SOC 2, and other industry standards.Snowpark & Python SupportSnowflake's Snowpark allows developers to use Python, Java, and Scala for data transformation and machine learning.Benefits of Learning SnowflakeHigh Demand for Snowflake ProfessionalsMany companies are migrating to cloud-based data platforms, making Snowflake skills highly valuable in the job market.Better Career OpportunitiesSnowflake knowledge opens doors to roles like Data Engineer, Data Analyst, Cloud Data Architect, and Snowflake Consultant.Competitive SalariesSnowflake professionals earn high salaries, as demand exceeds supply in the job market.Ease of Learning & UseUnlike traditional databases, Snowflake requires minimal administration, making it easier for beginners to learn.Cloud & Big Data ExpertiseLearning Snowflake enhances cloud computing and data warehousing skills, which are essential in the modern data-driven industry.Integration with BI & AI ToolsSnowflake integrates with Tableau, Power BI, Looker, Python, TensorFlow, and many other analytics and AI tools.Future-Proof TechnologySnowflake 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 CurriculumSection 1: Introduction to SnowflakeOverview of Data WarehousingImportance of Cloud ComputingThe Snowflake Story: Evolution & Use CasesSection 2: Getting Started with SnowflakeSigning Up for SnowflakeExploring the Snowflake UICreating Databases, Schemas, and TablesLoading Data into a TableSetting Up Essential Snowflake ToolsAssignment: Create, Load & Query a TableSection 3: Snowflake Compute - Virtual WarehousesCreating Virtual WarehousesWarehouse Sizes & ScalabilityMaximized vs. Auto Scaling ModesMulti-Cluster Warehouse Scaling PoliciesAssignment: Create a New Virtual WarehouseSection 4: Architecture, Features & PricingSnowflake Key Concepts & ArchitectureCloud Platform Support & Global RegionsSnowflake Editions & ReleasesUnderstanding Snowflake PricingData Integration & InteroperabilityQuiz: Snowflake ConceptsSection 5: Loading & Unloading Structured DataData Ingestion Methods & Best PracticesSteps for Managing Data LoadsPreparing & Staging DataLoading Data from Internal & External StagesSnowpipe: Real-Time Data LoadingQuiz: Data Ingestion in SnowflakeSection 6: Semi-Structured Data HandlingLoading & Unloading JSON DataRunning Analytics on JSON DataWorking with ORC & Parquet FormatsAssignment: Load JSON Data from an S3 BucketSection 7: Data Transformations & StagingQuerying & Transforming Data in Staged FilesMetadata Insights for Staged FilesTransformations During Data LoadSection 8: Managing Databases, Tables & ViewsTemporary, Transient & Permanent TablesExternal Tables & Their UsesOverview of Views & Materialized ViewsTable Design ConsiderationsSection 9: Time Travel, Failsafe & Zero Copy ClonesTime Travel: Restoring to a Specific PointAssignment: Implement Time Travel & RecoveryUnderstanding Failsafe & Storage UtilizationAssignment: Analyze Storage Used by Fail-SafeZero Copy Cloning & Cloning with Time TravelQuiz: Time Travel & Zero Copy ClonesSection 10: Performance OptimizationOptimization Strategies in SnowflakeUsing Dedicated Virtual WarehousesScaling Out with Multi-Cluster Virtual WarehousesMaximizing Query Cache UtilizationLab: Query Caching in ActionClustering Large Tables for Better PerformanceLab: Implementing Cluster KeysSearch Optimization TechniquesQuiz: Performance OptimizationSection 11: Secure Data SharingSecure Data Sharing ConceptsSharing Data with Snowflake & Non-Snowflake UsersAssignment: Share a Table with Another UserLab: Sharing Schemas, Databases & ViewsQuiz: Secure Data SharingSection 12: Snowflake Access ManagementSnowflake’s Role-Based Access Control ModelRole Hierarchy: ACCOUNTADMIN, SYSADMIN, SECURITYADMINManaging Custom Roles & PermissionsLab: Assigning Privileges via Custom RolesQuiz: Snowflake Access ManagementSection 13: Advanced FeaturesChange Tracking with Table StreamsAutomating Workflows with TasksUser-Defined Functions (UDFs) & Stored ProceduresColumn-Level & Row-Level SecurityImplementing Resource Monitors

Overview

Section 1: Introduction to Data Warehouse

Lecture 1 Part 1 - Introduction to Data Warehouse

Lecture 2 Part 2 - Introduction to Data Warehouse

Section 2: Dimensional Modelling

Lecture 3 Dimensional Modelling

Section 3: ETL and ELT in Data Warehouse

Lecture 4 ETL and ELT in Data Warehouse

Section 4: Introduction to Snowflake and its Architecture

Lecture 5 Introduction to Snowflake and its Architecture

Section 5: Snowflake Database and Pricing

Lecture 6 Snowflake Database and Pricing

Section 6: Snowflake Cost Management

Lecture 7 Snowflake Cost Management

Section 7: Loading Data into Snowflake

Lecture 8 Loading Data into Snowflake

Section 8: Transformation while Loading the Data

Lecture 9 Transformation while Loading the Data

Section 9: Copy Option

Lecture 10 Copy Option

Section 10: Loading of Semi-structured Data (JSON)

Lecture 11 Loading of Semi-structured Data (JSON)

Section 11: Loading of Parquet Data and File Format Object

Lecture 12 Loading of Parquet Data and File Format Object

Section 12: Performance Optimization in Snowflake

Lecture 13 Part 1 - Performance Optimization in Snowflake

Lecture 14 Part 2 - Performance Optimization in Snowflake

Section 13: Uploading Data from AWS to Snowflake

Lecture 15 Uploading Data from AWS to Snowflake

Section 14: Unloading Data from Snowflake to AWS

Lecture 16 Unloading Data from Snowflake to AWS

Section 15: Snowpipe

Lecture 17 Snowpipe

Section 16: Stream

Lecture 18 Part 1 - Stream

Lecture 19 Part 2 - Stream

Section 17: Zero-Copy Cloning and Swapping

Lecture 20 Zero-Copy Cloning and Swapping

Section 18: Time Travel

Lecture 21 Time Travel

Lecture 22 Time Travel - Practical

Section 19: Fail Safe

Lecture 23 Fail Safe

Section 20: Types of Tables in Snowflake

Lecture 24 Types of Tables in Snowflake

Section 21: Snowflake Access Management

Lecture 25 Part 1 - Snowflake Access Management

Lecture 26 Part 2 - Snowflake Access Management

Section 22: Snowflake interview Questions

Lecture 27 Snowflake interview Questions

Data Engineers – To learn how to design and optimize Snowflake-based ETL/ELT pipelines.,Data Analysts – To perform high-performance SQL queries and optimize reporting workloads.,Data Scientists – To leverage Snowflake’s powerful compute capabilities for large-scale data analysis.,Database Administrators (DBAs) – To understand multitenancy, storage, and security features in Snowflake.,Cloud Engineers – To manage virtual warehouses, scaling policies, and cloud integrations.,Business Intelligence (BI) Developers – To work with structured and semi-structured data using tools like Tableau, Power BI, and Looker.,ETL Developers – To learn data ingestion, transformation, and loading best practices in Snowflake.,Security Engineers – To implement role-based access control (RBAC), encryption, and data governance.,IT Managers – To understand cost optimization, pricing, and scaling strategies.,Software Engineers – To learn how to use Snowflake’s UDFs, stored procedures, and automation tools.,Solution Architects – To design scalable, secure, and optimized cloud data platforms.,Aspiring Data Engineers & Analysts – To gain hands-on skills in data warehousing and cloud analytics.,IT Professionals Transitioning to Cloud & Big Data – To stay relevant in the evolving cloud-based data ecosystem.