Snowflake for Business Intelligence and Analytics Professionals

Uplatz offers this comprehensive course on Snowflake for Business Intelligence and Analytics Professionals. It is a self-paced course with video lectures. You will be awarded Course Completion Certificate at the end of the course.

Snowflake is a cloud-based data platform that offers data warehousing, analytics, and data integration services. It's a Software-as-a-Service (SaaS) product designed to manage and analyze large volumes of data in a scalable, cost-efficient, and easy-to-use manner. Snowflake is distinct from traditional data warehouses due to its cloud-native architecture, enabling seamless scalability and performance.

Key Features of Snowflake:

1. Cloud-Native Architecture: Operates on platforms like AWS, Google Cloud, and Azure, with no need for hardware or software installation.
2. Separation of Compute and Storage: Enables independent scaling of compute resources and storage, optimizing cost and performance.
3. Multi-Cluster Shared Data Architecture: Supports multiple workloads without contention, allowing diverse teams to access data simultaneously.
4. Support for Semi-Structured Data: Easily handles JSON, Parquet, and Avro formats.
5. Secure and Governed Data Sharing: Shares data across accounts without duplication.
6. Pay-as-You-Go Model: Costs are incurred only for resources used.

How Does Snowflake Work?

Snowflake uses a three-layer architecture:

1. Cloud Services Layer

   • Manages metadata, security, query optimization, and infrastructure management.
   • Handles authentication, query parsing, and governance tasks seamlessly.

2. Compute Layer (Virtual Warehouses)

   • Consists of virtual warehouses, which are clusters of compute resources that process queries.
   • Compute can be scaled up or down independently, and multiple virtual warehouses can operate simultaneously.

3. Storage Layer

   • Data is stored in a compressed, columnar format in cloud storage.
   • The storage layer is shared across all compute clusters, ensuring a single source of truth for all users.

Workflow of Snowflake:

1. Data Ingestion

   • Data can be loaded into Snowflake from sources such as files, databases, or streaming pipelines using Snowpipe (Snowflake's continuous data ingestion service).
   • Supports batch and streaming data ingestion.

2. Data Storage

   • Snowflake automatically optimizes, compresses, and organizes the data for efficient querying.

3. Query Processing

   • Users run SQL queries through a virtual warehouse.
   • The compute resources in the virtual warehouse process the data while the storage layer remains untouched.

4. Data Sharing and Collaboration

   • Snowflake’s secure data sharing capabilities allow seamless sharing of live data with other Snowflake accounts or external users without duplicating the data.

5. Scalability and Performance Optimization

   • Snowflake auto-scales resources based on workload and can instantly resume suspended compute resources for efficient cost management.

Advantages of Snowflake:

• Ease of Use: Simple interface, SQL-based queries, and minimal administrative tasks.
• Scalability: Dynamic scaling to handle workloads of any size.
• Flexibility: Supports structured, semi-structured, and unstructured data.
• Cost Efficiency: Pay only for resources used, thanks to its separation of compute and storage.
• Interoperability: Integrates with BI tools, ETL pipelines, and machine learning frameworks.

Reed Courses Certificate of Completion

Digital certificate - Included

Uplatz Certificate of Completion

Digital certificate - Included

• Snowflake - all lectures 22:49:33

  • 1: Part 1 - Introduction to Data Warehouse 51:53
  • 2: Part 2 - Introduction to Data Warehouse 37:10
  • 3: Dimensional Modelling Preview 1:06:35
  • 4: ETL and ELT in Data Warehouse 1:08:43
  • 5: Introduction to Snowflake and its Architecture 1:10:55
  • 6: Snowflake Database and Pricing 45:33
  • 7: Snowflake Cost Management 47:08
  • 8: Loading Data into Snowflake Preview 55:03
  • 9: Transformation while Loading the Data 39:23
  • 10: Copy Option 45:26
  • 11: Loading of Semi-structured Data (JSON) 44:57
  • 12: Loading of Parquet Data and File Format Object 51:23
  • 13: Part 1 - Performance Optimization in Snowflake 58:43
  • 14: Part 2 - Performance Optimization in Snowflake 37:37
  • 15: Uploading Data from AWS to Snowflake 1:04:28
  • 16: Unloading Data from Snowflake to AWS Preview 30:38
  • 17: Snowpipe 50:04
  • 18: Part 1 - Stream 50:33
  • 19: Part 2 - Stream 36:53
  • 20: Zero-Copy Cloning and Swapping 1:08:44
  • 21: Time Travel 29:50
  • 22: Time Travel - Practical 51:49
  • 23: Fail Safe 34:22
  • 24: Types of Tables in Snowflake 1:07:12
  • 25: Part 1 - Snowflake Access Management 56:07
  • 26: Part 2 - Snowflake Access Management 53:15
  • 27: Snowflake Interview Questions 55:09

Snowflake - Course Syllabus

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

1. Business Intelligence (BI) Professionals:

   • Data analysts, BI developers, and report designers who want to leverage Snowflake's cloud data platform for faster and scalable data analysis.

   • Professionals looking to optimize data warehousing and reporting processes.

2. Data Analysts and Data Scientists:

   • Individuals who work with large datasets and need to perform advanced analytics, data exploration, and visualization using Snowflake's capabilities.

3. Data Engineers:

   • Professionals responsible for building and maintaining data pipelines, ETL processes, and data integration workflows who want to use Snowflake as their data warehouse solution.

4. IT and Database Administrators:

   • Those managing data infrastructure and looking to migrate to or manage Snowflake's cloud-based data platform.

5. Analytics Managers and Decision-Makers:

   • Leaders who want to understand how Snowflake can enhance their organization's data-driven decision-making processes.

6. Professionals Transitioning to Cloud-Based Data Platforms:

   • Individuals with experience in traditional data warehouses (e.g., Oracle, Teradata) who want to upskill and transition to modern cloud-based solutions like Snowflake.

7. Aspiring Data Professionals:

   • Students or beginners in the field of data analytics and BI who want to learn Snowflake as part of their skill set for career advancement.

Passion and determination to achieve your goals!

• Snowflake Developer
• Data Engineer
• Data Analyst
• Data Warehouse Engineer
• Cloud Data Engineer
• Big Data Engineer
• ETL Developer
• Data Architect
• Cloud Data Architect
• Snowflake Consultant
• Data Analytics Engineer
• Business Intelligence Developer
• Data Platform Engineer
• Cloud Solution Architect
• Database Administrator
• Machine Learning Engineer (with Snowflake integration)
• Data Consultant
• Data Scientist

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